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Top Data Analytics Companies in Jaipur for Smart Business Insights

1. Introduction 

Jaipur is rapidly growing as a technology and analytics hub, with more businesses using data to improve decisions as well as performance. Many startups, mid sized companies, and large enterprises in Jaipur depend upon data analytics partners to understand the customers, track operations and also for the future planning. Data analytics companies in Jaipur help organisations in turning raw data into clear and meaningful insights using services such as business intelligence, data engineering, AI solutions etc. These services help better planning and also better decision making. This list below highlights the top 07 data analytics companies in Jaipur that are well known for their skills, reliability and successful delivery. Good analytics partners provide long term growth, smarter strategies as well as measurable business outcomes.

2. What is AI and Data Analytics in Jaipur?

Data analytics is the process of collecting, preparing and analyzing the business data in order to discover certain types of hidden trends, patterns and insight. It helps in the proper planning and making decisions for the end business growth. Data analytics basically consists of data integration, predictive modeling, performance measurement, visual reporting and a list of tools & technologies to facilitate the business objectives of various departments. Leading data analytics companies in Jaipur utilize a combination of tools & technologies to help different industries like healthcare, energy, E-commerce & logistics, Manufacturing, Banking & FinTech etc. to solve their business problems.

3. Top Data Analytics Companies in Jaipur

3.1) DataTheta

DataTheta is a decision sciences and analytics consulting firm that is headquartered in Texas USA, and has delivery centres in Jaipur. When the enterprises need reliable, governed as well as scalable analytics environments, and not just dashboards, they choose DataTheta. DataTheta is known for its strong focus on governance, compliance, security and scalability. The DataTheta team of senior data engineers & scientists brings together more than 10 years of average experience. It ensures high-quality delivery across complex analytics initiatives. They have successfully delivered 80+ data projects with a 98% client satisfaction rate. DataTheta combines industry expertise with flexible engagement models. 

These are:- fixed-time projects, managed services, and developers-on-demand (also known as fixed time resource). 

This agile approach helps the businesses to modernize their analytics capabilities efficiently along with properly maintaining control over cost and better performance.

3.2) Navsoft 

Navsoft is a global technology company with more than 25 years of experience helping businesses grow using simple, practical digital solutions. The company supports organizations in big data and analytics, ERP, CRM enabling them to use data in an effective manner and also make better business decisions.

Trusted by Leading Brands Worldwide

• Worked with fortune 500 companies
• Solutions actively  used in 33+ countries

Making AI Simple and Useful for Businesses

GenAI Solutions

Custom large language models built for real business needs and also used for customer support automation, content creation etc.

Conversational AI

Questions can be asked in any language, insights can be directly sourced from business data, and helps the teams in making faster and better decisions.

Predictive Analytics

Uses historical data to forecast trends, risks and opportunities are identified early and also supports better planning and decision making.

Data Engineering

Builds reliable as well as scalable data pipelines, and ensures that analytics and AI systems run smoothly in a real environment.

Computer Vision

Helps in converting existing CCTV systems into smart monitoring tools and also tracks safety and unusual activities.

AI Consulting

Practical AI strategies aligned with clear business goals.

Min project size: $10,000+

Hourly rate: $25 - $49 / hr

Employees: 250 - 999

Year founded:- 1999

3.3) Edvantis

Edvantis is a global software engineering company that has more than 400 skilled professionals working across USA as well as Europe. It helps the businesses in building high quality software products on time and scale. It has a delivery and hiring hub in jaipur that helps in building scalable & robust data analytics programs which improves the optimal operational measurements, risk assessment and reporting systems.

Tech Expertise: 

Software Engineering Services:

Includes software architecture and software development, quality assurance and testing, business analysis and IT operations.

BI & Data Analytics:

Includes Data integration and data warehousing, data science and machine learning, predictive analysis and advanced data visualisation.

Artificial Intelligence (AI):

Includes AI assisted software development, Natural Language Processing, Voice and speech recognition.

 Service Models:

  • Dedicated teams work with more than 1 week hiring time.
  • Fixed-Price Project led by Agile, Scrum and PMI certified experts
  • Digital Transformation & IT Consulting

Focus Industries:

  • Hi-Tech
  • HealthTech
  • Real Estate
  • Public Sector
  • Supply Chain & Logistics

Why Edvantis?

  • On time delivery with agile planning
  • More than 72% senior and expert level engineers
  • Strong security standards including DSS, GDPR, HIPAA etc.
  • Flexible engagement with free consulting.

Min project size - $10,000+

Hourly rate- $25 - $49 / hr

Employees - 250 - 999

Year founded - Founded 2005

3.4) Massive Insights

Massive Insights has been helping the companies to use data effectively since 2012. The company works with data rich organisations to turn information into clear insights as well as business results. Massive insights mainly focus on speed, clarity and execution. 

What  Massive Insights Do:-

BI & Dashboards - Helps in building simple and clear dashboards that are actually used by people and helps teams take actions based on the data.

Data Strategy - Creates practical data roadmaps, and aligns data work with real business goals.

AI & ML Enablement - Starts with small, useful use cases and proves value quickly and scales over time.

Cloud Data Architecture - Design modern, scalable data systems and focus on speed, reliability and trust.

Platform Modernization & Adoption - Provides dedicated or part time analysts and architects. Also helps the teams in getting more value from existing tools.

Why Massive Insights?

Proven experience - They have an experience of more than 12 years in analytics. They have completed more than thousands of successful projects.

Business-First Approach - Primarily focuses on solving real business problems and every project is tied to measurable outcomes.

Fast and Focused - They have MVP driven approach

Stakeholder-Ready Solutions - Helps the internal teams in supporting marketing, finance and operations. It also delivers insights that are easy to understand and act on.

Min project size- $25,000+

Hourly rate - $25/hr

Employees - 10 - 49

Year founded - Founded 2012

3.5) Scalo 

Scalo is a Polish software development company that helps in building modern, future ready digital solutions for businesses. They have an experience of more than 17 years with more than 750 successful projects. Scalo is well known for delivering reliable and scalable technology that grows with changing business needs.

Scale provide services such as:

  • They offer custom software development that adapts as the business grows.
  • Data & AI solutions that turn data into clear and relevant insights.
  • IT staff augmentation to add skilled technology experts to the team.

Building Future-Ready Software 

  • Design solutions that work today and scale for tomorrow.
  • Adapts easily to new technologies and changing markets
  • Helps businesses stay competitive over the long term. 

Areas of Expertise:-

Digital Innovation & AI - Provides smart automation solutions and data driven prediction software..

Cloud & DevOps Excellence - Provides full stack software development and cloud performance and cloud optimisation.

Data Engineering & Analytics - Helps in processing and usage of big data and also builds reliable pipelines.

Digital Experience Design - User focused design using a clean and intuitive interface.

Software Architecture & Integration - API development and system integration.

Partnerships & Certifications 

  • Has an expertise in Microsoft Azure and AWS cloud platforms.
  • Partnership with AB Initio and Form.io for data governance
  • ISO 27001 certified for information security.

Min project size - $10,000+

Hourly rate - $50 - $99 / hr

Employees - 250 - 990

Year founded - Founded 2007

3.6) Reenbit

Reenbit is an AI driven software development and data engineering company based in eastern Europe. It helps the businesses in building, modernizing and scaling digital products from enterprise software to data platforms that support faster as well as smarter decisions. 

Reenbit is an AI focused software and data engineering company. They have a team of more than 100 engineers, and an experience of more than 7 years. They are partners of Microsoft and also ISO 27001:2022 certified for security. They work as a long term technology partner. 

What Reenbit does-

  • They develop custom software for enterprise applications.
  • They use data engineering and AI to improve data visibility and decision making.
  • Cloud engineering on Microsoft Azure.
  • Process automation to improve efficiency.

Industries Served -

  • Retail and e-commerce
  • Energy
  • Healthcare
  • Logistics

They have delivered more than 70 projects worldwide and help the companies in improving operations, scalability and data clarity. Companies choose Greenbit because they have a strong engineering culture with a business first mindset. They focus on real business outcomes and not just codes.

Min project size - $25,000+

Hourly rate - $25 - $49 / hr

Employees - 50 - 249

Year founded - Founded 2018

3.7) Data Understood

Data Understood is a data analytics consulting firm that helps organisations use their data in the right way to achieve real business goals. 

What Data Understood does -

  • It helps the businesses in finding clear direction from large and complex data.
  • They helps to turn useful insights that support today’s needs and future growth.
  • Keeps the organisation flexible and agile as data and technology evolve.

Why Data Strategy matters -

  • Data is a valuable business asset when it is used correctly
  • Many companies adapt AI and analytics too quickly.
  • Without a clear strategy, this leads to confusion as well as wasted effort.

Data understood builds clear data strategies aligned with business goals. They ensure that the data is well structured, governed and reliable. They also help in creating a data driven culture across teams. When the organisations need smarter decision making, better efficiency and stronger competitive advantage they choose Data understood.

Min project size - $10,000+

Hourly rate - $100 - $149 / hr

Employees - 25 - 50

Year founded - Founded 2018

Related Post:- Top Data Analytics Consulting Companies for Enterprises in India

4) Conclusion

The data analytics ecosystem in Jaipur is still growing at a rapid pace with more enterprises needing to find the structured analytics support, prediction systems, and performance measurement tools. In the above article, I have listed some of the reliable companies in Jaipur that provide data analytics services like data engineering, predictive modeling, reporting structures, consulting etc. They have prior experience in working with small & medium size (SMB) and large scale enterprises in solving their business problems using the help of advanced technology stack. You can check these analytics companies capabilities, pricing structure, data engineer experience & expertise and take a final call while selecting the best data analytics partner in Jaipur for your business.

Top Data Analytics Companies In Ahmedabad for Manufacturing and Retail Businesses

1) Introduction

Ahmedabad has become an important centre for manufacturing and retail businesses. Companies are increasingly using data to improve efficiency, reduce costs and understand customers. As the factories and supply chains are generating a large amount of data, businesses need data analytics partners who can turn this information into clear and practical insights. Managing this data without analytics often leads to delays, wastage and also missed opportunities. Data analytics companies in Ahmedabad helps manufacturing and retail organisations in tracking production performance, forecasting demand, managing inventory and in improving customer experience. They help manufacturers and retailers make sense of this data. They convert raw numbers into simple reports, dashboards and forecasts that show what is working, what is not and improvements. Their services include business intelligence dashboards, data engineering, predictive analytics designed for real operational needs. This list highlights the top data analytics companies in Ahmedabad that help in supporting the manufacturing and retail businesses in making faster, smarter and more reliable decisions.

Read more :- Top Data & AI Solution Companies in India (Ranked)

2) What is Data Analytics?

Data analytics is the process of collecting, preparing and analyzing the business data in order to discover certain types of hidden trends, patterns and insight. It helps in the proper planning and making decisions for the end business growth. Data analytics in Ahmedabad basically consists of data integration, predictive modeling, performance measurement, visual reporting and a list of tools & technologies to facilitate the business objectives of various departments. Leading data analytics companies in Ahmedabad utilize a combination of tools & technologies to help different industries like healthcare, energy, E-commerce.

3) Top Data Analytics Companies In Ahmedabad

3.1) DataTheta

DataTheta is a data analytics consulting firm that has delivery centres in Ahmedabad, India. When companies need reliability, security and scalable data environments they choose DataTheta. They don’t only make basic dashboards. The team consists of experienced data engineers and data scientists that have more than 10  years experience. DataTheta ensures high quality execution especially for complex data as well as AI projects. They have a strong focus on governance, compliance as well as security. DataTheta has delivered more than 80 projects and has a client satisfaction rate of 98%.

DataTheta offers flexible working models based on client needs

  • Fixed time projects
  • Managed analytics services
  • Developers on demand.

This  flexible approach helps the businesses in modernizing their analytics systems efficiently along with maintaining cost controls.

3.2) Spec India

Spec India is a software development company established in 1987. Over the years the company has grown by adopting modern technologies and AI driven development. They help in building secure, scalable as well as practical digital solutions while ensuring the privacy of data and enterprise level security. SPEC India has a skilled team of developers who focus on creating smart, easy to use and reliable applications. They understand the business needs first and then build the solutions accordingly.

They work globally with many companies such as the USA, UK, Europe, Germany, Canada etc.

Their clients include-

  • Fortune 100 and 500 companies
  • Startups
  • Small and medium enterprises.

SPEC India works across many industries such as -

  • Healthcare
  • Finance and insurance
  • Retail
  • eCommerce
  • Sports

SPEC India is chosen by many businesses because they have a good hold across the industry. They have more than 3000 projects delivered, They have already served more than 40 companies, they have a 200 plus client rate etc. They build intelligent and AI powered solutions while ensuring data security.

Min project size - $5,000+

Hourly rate - $25 - $49 / hr

Employees - 250 - 999

Year founded - Founded 1987

3.3) Cygnet.One

Cygnet.One is a global digital transformation and engineering company that helps mid-sized and large enterprises in using data, AI and cloud technologies in order to improve business performance. Cygnet.One helps businesses in organising scattered data systems. They help the companies in building strong data foundations that support growth, governance and help in making smarter decisions. They also help the organisations in adopting artificial intelligence in a responsible way by providing them AI consulting and implementation services. 

These AI solutions help in-

  • Improving business decisions
  • Supporting generative AI use cases
  • Enabling intelligent workflows

They also provide cloud engineering services such as -

  • Cloud Migration
  • Cloud optimisation
  • Secure and scalable platform setup

Min project size - $10,000+

Hourly rate - $25 - $49 / hr

Employees - 1,000 - 9,999

Year founded - Founded 2000

3.4) RadixWeb

RadixWeb is a Ahmedabad based company that helps the organisations in providing analytics as well as data solutions. They support  end to end analytics work that includes data strategy, analytics dashboards, data engineering etc. They help in building systems that trunks the data into clear insights and smarter decision making tools. When the businesses or the companies look for reliable analytics and data driven transformation support, they usually go for RadixWeb. It helps the businesses in combining the data from various sources, preparing it for reporting, building analytics dashboards etc.

Min project size - $25,000+

Hourly rate - $25 - $49 / hr

Employees - 250 - 999

Year founded - Founded 2000

3.5) Analytics Liv Digital LLP

Analytics Liv Digital LLP is a google certified and met business partner that helps businesses grow using data driven marketing strategies. They combine SEO, Google Analytics and CRO to understand what is working, to identify gaps and make practical changes that increase the revenue.

They mainly focus on-

  • Increasing conversions
  • Reducing customer acquisition costs.
  • Helping brands in making clearer decisions.
  • Improve overall marketing performance.

They mainly help by using data to find what is leaking in markets, by fixing issues using SEO, ads and UX improvements and by running focused experiments that are tied directly to the business goals. The decisions are guided by real data  instead of assumptions.

Min project size - $1,000+

Hourly rate - $50 - $99 / hr

Employees - 10 - 49

Year founded - Founded 2021

3.6) Elegant MicroWeb

Elegant MicroWeb is a software products and services company focused on delivering real value through technology. It works with customers across the USA, Europe, Japan and India supporting businesses of all sizes. They have years of experience of delivering technology solutions worldwide.

The company mainly wants to make technology simple to use and also easy to deploy. They provide feature- rich solutions in a stable and reliable environment. They offer services such as application development and maintenance, mobile application development and IT consulting services.

Industry and client experiences -

  • Works with SMEs, software companies and web agencies
  • Experience across multiple industry as well as business models
  • Handles projects of all sizes and complexity

This company ensures proven delivery and product delivery processes. Their team is full of skilled and professional members. They make sure that the delivery is on time along with a strong focus on quality. Elegant MicroWeb helps the businesses in building and maintaining reliable software solutions by making technology easy, effective as well as valuable.

Min project size - $5,000+

Hourly rate - $25 - $49 / hr

Employees - 50 - 249

Year founded - Founded 2001

4) Conclusion

Ahmedabad is a strong hub for data analytics and BI services, with many firms that support the enterprises in forecasting, customer insights and cloud data integration. Many companies in Ahmedabad offer similar analytics capabilities, but we can differentiate between a good and a bad company on the basis of some factors such as industry understanding, clear ownership of models as well as secure data handling. The firms listed above are trusted by sectors such as finance, retail and media because these firms deliver structured data pipelines and reliable BI reporting. Choosing a right data analytics partner is very important as it leads to smoother planning and better adoption of analytics, and becomes a long term partner that helps in reducing uncertainty and improving decision making.

Top Data Analytics & Business Intelligence Companies in Pune for IT & Manufacturing Businesses

1. Introduction

Pune is one of the leading cities in India for IT services and manufacturing industries. Pune is home to many software companies, engineering firms and technology startups etc. As the businesses are growing at a rapid pace, they are also generating large amounts of data from operations, customers, digital platforms etc. However, having access to data is not enough, the companies must also have the right tools and expertise in understanding and using the data in an effective way. This is where the Data Analytics and Business Intelligence companies play an important role. These firms help the business in collecting, organising and analysing the data in order to create reports and dashboards. Analytics help the companies in improving production efficiency, customer behavior as well as project performance. Data analytics and BI companies in Pune help the IT and manufacturing businesses in making smarter decisions and  improving performance.

2. Top 10 Data Analytics & Business Intelligence Companies in Pune for IT & Manufacturing Businesses

2.1) DataTheta

Data Analytics is a data and analytics consulting company that has an office in Pune. The company helps the businesses in making sense of data that is spread across many systems and tools. Many organisations struggle because their data is scattered, unorganised and also hard to trust. DataTheta helps the businesses in bringing all this data together into one clean and reliable system so the decision makers can clearly see the things happening in their business.

What DataTheta does -

  • Creating cloud based data platforms that store the data in one place.
  • Building automated pipelines that help in moving the data between systems without any manual effort.
  • Designing data warehouses 
  • Developing BI dashboards that show performance clearly.

DataTheta serves businesses across industries such as -

  • Pharma and Healthcare
  • Retail
  • Banking and manufacturing services
  • SaaS and technology companies

Min project size - $5,000+

Hourly rate - < $25 / hr

Employees - 50 - 249

Year founded - Founded 2017

2.2) Finarkein Analytics

Finarkein analytics is a big data analytics platform in Pune that is built specially for BFSI industry that is Banking, Financial, Services and Insurance. This company helps the banks and financial institutions in using the data in a smarter as well as faster way in order to improve customer engagement and collections. Finarkein analytics makes it easier for banks in understanding the customer data and in taking better financial decisions using the technology.

What Finarkein does -

  • Finarkein provides the analytics platform for the bank where banks can start using it without building everything from scratch.
  • Use open data to understand the customers in a better way
  • Supports collections and recovery processes.

Key Features of Finarkein Analytics -

  • Ready to use APIs for faster data integration
  • Designed specifically for banking and financial institutions
  • Helps in converting the raw data into meaningful insights
  • Supports better decision making

Founded year - 2019

2.3) Talentica Software

Talentica software is a Pune based technology and product engineering company that works closely with startups, enterprises and technology driven businesses. Talentica is best known for product development, it also plays a strong role in data analytics, business intelligence that help the companies in making better decisions. Talentica helps businesses in building smart and analytics solutions that help in turning data into useful insights.

Talentica supports IT and manufacturing businesses by -

  • Designing data analytics platform for business insights
  • Building BI dashboards and reporting systems
  • Working with big data, cloud data platforms and analytics tools
  • Help the companies use data for planning, forecasting and performance tracking

Value for IT and Technology Businesses -

  • Product analysis and user behavior tracking
  • Business Intelligence for growth and revenue
  • Cloud based data analytics

Value for manufacturing businesses -

  • Track production as well as operational data
  • Monitor efficiency and machine performance
  • Analyse supply chain and inventory data

Talentica is considered as a strong analytics partner that is located in Pune and is a major IT and manufacturing hub. They have a strong focus on data driven product engineering. They have a good working experience with modern analytics as well as BI technologies.

2.4) SG Analytics

SG analytics is a Pune based data analytics as well as research company that helps the businesses in making better decisions using the data. The company works with global clients and supports industries like IT, Manufacturing, Healthcare etc. SG Analytics companies help the companies in understanding their data clearly, in reducing confusion and making smarter decisions. SG analytics provides end to end analytics and insight services that includes data analytics and business intelligence. They also provide data management and data engineering support, advanced analytics and data science and also consulting services as well as market research.

For the manufacturing companies, SG analytics helps by -

  • Analyzing production and operational data
  • Tracking efficiency, costs and performance
  • Creating dashboards for management reporting.

For IT and Tech businesses, SG analytics support -

  • Business Intelligence for sales
  • Customer and product performance analytics
  • Data driven planning and forecasting

2.5) Rudder Analytics

Ruder analytics is a data analytics and business intelligence company in Pune that helps the organisations in using data in order to make better decisions. The company focuses on converting the raw data as well as unstructured data into clear reports, dashboards and insights that can be easily understood by the business teams. Ruder analytics helps the companies in seeing the actual data and also using it to improve the performance.

Why businesses choose Ruder Analytics -

  • Because they have a strong focus on practical, business friendly analytics
  • They provide easy to understand dashboards and insights
  • Helps the businesses in moving the data from data confusion to clarity
  • It is suitable for both IT as well as manufacturing sectors.

Ruder analytics helps the businesses in turning the data into useful information. By providing clear analytics and BI solutions, the company enables IT as well as manufacturing businesses to make smarter decisions, improve performance and also grow with confidence.

2.6) ScatterPie Analytics 

ScatterPie Analytics is also a data analytics and Business Analytics company that is located in Pune. This company helps the businesses in understanding data for better decision making by making the data simple, clear and useful for the business teams and not just technical teams. ScatterPie helps the companies in turning the raw data into easily readable reports and dashboards that support everyday business decisions.

ScatterPie supports businesses by  -

  • Turning raw data into simple charts as well as dashboards
  • By helping the leaders in seeing that what is working and what is not
  • Connecting the data from different systems at one place
  • Supporting better planning and decision making

2.7) Persistent Systems 

Persistent Systems is a global technology services and software company that is headquartered in India and has various delivery and hiring centers. It helps the businesses in using data, cloud and digital technologies to improve operations, build smarter products and also to make better decisions. Persistent helps the companies in modernizing their systems and using the data effectively in order to grow and stay competitive.

Persistent works across IT, data and digital transformations including -

  • Data Analytics and business Intelligence
  • Data Engineering and cloud platforms
  • AI and advanced analytics solutions

Persistent System Is a trusted partner because -

  • It has a global presence along strong delivery teams in India
  • It has a deep expertise in data, cloud and AI technologies
  • Focus on practical, business focused solutions. 

Read More :- Leading Data Analytics Companies Across India

3. Conclusion

Pune has emerged as one of India’s most important hubs for IT services and manufacturing industries. As digital adoption is rapidly increasing, businesses  in these sectors are also generating a large amount of volume from operations, customers as well as digital platforms. The Data Analytics and Business Intelligence companies in Pune play an important role in helping the organisations by converting this data into meaningful insights. These analytics firms support manufacturing businesses by improving production efficiency, reducing downtime, optimizing supply chains, and controlling operational costs. For IT and technology companies, they help track project performance, customer behavior, revenue growth, and product usage through clear dashboards and reports. By choosing the right analytics partner the IT and the manufacturing companies can improve performance, reduce the risks and achieve long growth.

Top Data Analytics & BI Companies in Coimbatore for Manufacturing & Tech Businesses

1.Introduction

Coimbatore is one of the fastest growing industrial cities in Tamil Nadu. The city is well known for its strong manufacturing base including textiles, engineering and also machinery production. Similarly, many  IT companies and technology startups are also growing here. As the businesses are growing at a rapid speed, they are also generating large amounts of data from machines, sales, supply chains and finance operations. Most companies collect the data and don’t know how to use it, this is where business intelligence and Data Analytics companies play an important role. These firms help the businesses in collecting, cleaning and organising their data. They create simple dashboards, reports as well as visual charts that make the complex information easy to understand, this means that instead of looking at thousands of numbers in the excel sheet, business owners can clearly see the graphs that show performance, profit, losses and trends. For tech businesses, analytics helps in understanding customer behaviour, product usage and website traffic. For manufacturing companies analytics help in tracking production speed, material usage and quality issues. In simple words the data analytics companies and BI companies in Coimbatore help the businesses in making better decisions using facts instead of just guesswork.

2. Top 6 Data Analytics Companies in Coimbatore for Manufacturing & Tech Businesses

2.1) DataTheta

DataTheta is an analytics company with offices in India and the United States. This company helps the businesses in organising the scattered data and turning the data into clear, reliable information that supports decision making. Many companies collect the data from different systems such as ERP, CRM, websites etc. But this data is often disconnected and also difficult to use. DataTheta helps in bringing everything together into one structured and easy to understand system. DataTheta helps in building cloud based platforms in order to store and manage the data securely. They also create automated data pipelines to help the data move smoothly across the systems. They develop BI dashboards and reports for clear performance tracking. They ensure data governance, security as well as compliance.

Industries Served -

  • Pharma
  • Healthcare
  • CPG and retail
  • Manufacturing
  • SaaS

Some different ways by which companies can work with DataTheta-

  • Fixed Project Model - for clearly defined projects
  • Developers on demand - skilled experts are available as per the needs
  • Managed analytics services - End to End analytics support

Min project size - $5,000+

Hourly rate - < $25 / hr

Employees - 50 - 249

Year founded - Founded 2017

2.2) ClousTech Solutions 

ClousTech solutions is a software development company that helps businesses grow by building smart and practical digital solutions. They work closely with clients to turn their ideas into working websites, mobile apps and smart digital systems. Their main goal is to make the technology simple and useful for businesses of all sizes. ClousTech focuses on understanding the needs of a business that helps in improving daily operations, customer experience and also overall performance, instead of offering complicated solutions. They ensure to deliver reliable and modern digital products. 

ClousTech provides a wide range of technology services including -

  • Web Development - Designing and building professional business websites
  • Cloud Migration - Moving the business systems and data to secure the cloud platforms
  • Business Intelligence - Creating reports and dashboards for better decision making
  • Business Automation - Automating repetitive tasks to save time as well as to reduce errors
  • Digital Marketing - Helps the businesses in promoting their brand online.

Founding Year - 2022

Team Size - 2-9

Cost of Services - <$30/h

2.3) Conventus

Conventus is a technology company that helps in building secure and smart automation tools for industries that must follow strict rules and regulations. These industries include sectors such as healthcare, banking, insurance etc. The company focuses on helping the organisations in improving their internal processes using AI driven automation and also ensures that the data is kept safe and compliant along with legal standards. Conventus designs its software in such a way that they can easily connect with a company’s software systems. This means that the business can improve what they already use and there’s no need to replace everything.

Conventus Is Different because -

  • It builds secure and compliance focused technology solutions
  • They specialises in AI powered automation
  • They works well with existing business systems
  • They are designed for regulated industries

Founding Year - 2004

Team Size - 50-249

Cost of Services - $30-70/h

2.4) ScienceSoft

ScienceSoft is an IT consulting and software development company that was founded in 1989. This company has more than 750 experts and also works with clients across the United States, Europe and GCC. This company has delivered projects in more than 70 countries and supports more than 30 industries.

Some well known companies that have worked with ScienceSoft are -

  • IBM
  • eBay
  • Ford
  • Viber

Industries ScienceSoft works with -

  • Healthcare
  • Banking
  • Manufacturing
  • Retail
  • Education

ScienceSoft also builds AI solutions such as -

  • HIPAA compliant voice assistants
  • Automated trading systems
  • Computer vision applications

The main mission of ScienceSoft is to drive the project success, no matter what. They ensure project quality and smooth delivery through a strong Management Office and Technology and Competency Center.

Awards and Certifications -

  • Listed in IAOP Global Outsourcing 100
  • Included in Inc. 500
  • Winner of FinTech Futures Banking Tech Award 2024
  • ISO 9001 for quality management
  • ISO/IEC 27001 for Information Security
  • ISO 13485 for media device quality.

Min project size - $5,000+

Hourly rate - $50 - $99 / hr

Employees - 250 - 999

Year founded - Founded 1989

2.5) LatentView Analytics 

LatentView Analytics is a fast growing company that serves data analytics as well as business insights. This company helps businesses in understanding their data and using it in order to make better decisions. LatentView focuses on solving real business problems using the data instead of just creating reports. They combine strong knowledge of marketing and customer behaviour with advanced skills in analytics, technology as well as Big Data. They help the companies in improving sales, product performance and overall strategy. LatentView works with many 500 fortune companies and provides analytics services.

Areas of Expertise - 

  • Customer management
  • Digital and social media marketing
  • Product analysis
  • Data analysis and modeling
  • Brand management

LatentView is strong because of the following factors -

  • Business-Led Solutions - Focuses on solving the business problems first, understands client goals before building the solutions and maintains open communication with clients
  • Strong Analytics Expertise - Dedicated analytics focused company, converts raw data into clear and meaningful insights.
  • Delivery excellence - Follows strong project management processes, ensures high quality and consistent results by focusing on timely and reliable delivery.

Min project size - $1,000+

Hourly rate - $150 - $199 / hr

Employees - 250 - 999

Year founded - Founded 2006

2.6) phData Inc.

phData is a big data consulting company that helps the businesses in using their large as well as complex data in a better way. This company is famous for its deep expertise in Hadoop and big data platforms. phData works with many 500 fortune companies and large enterprises in order to build, manage and improve their data systems. phData helps in collecting huge amounts of data, organizing it properly and turning the data into useful information for better decision making. phData takes care of designing the data architecture, monitoring system performance and ensuring reliability, security and scalability.

Companies choose phData because they have a strong expertise in Hadoop and big data technologies, they have a good work experience 

With large enterprises, they have worked with fortune 500 companies, they don’t just provide consulting but also end to end support. phData helps the companies handle big data without complexity.

Min project size - $5,000+

Hourly rate - $100 - $149 / hr

Employees - 50 - 249

Year founded - Founded 2014

Related Post:- Ranked List of Data Analytics Companies in India

3. Conclusion

Coimbatore has become an important hub for both manufacturing as well as technology businesses. As these companies are growing at a rapid pace, the data generated is also increasing. This data can be very useful, only if it is properly used and understood. The Data Analytics and Business Intelligence companies in Coimbatore help the businesses in turning raw data into clear and meaningful insights. These companies support the manufacturers by controlling costs and improving quality. For the tech businesses they help to track the customer behaviour, sales performance and product usage. They use dashboards, reports and automated systems through which the decisions become more accurate and faster. Data analytics and business intelligence companies in Coimbatore help the businesses in making decisions that should be based on facts instead of just guesswork. By working with the right analytics partner the tech companies can improve performance, reduce the risks and can grow confidently in today’s market.

Top Data Analytics companies in Indore for Mid-Size IT and SaaS  Companies

1. Introduction

Indore is quickly becoming an important technology hub in central India, especially for mid size IT and Saas companies looking for reliable data analytics partners. Rapid growth of SaaS businesses are generating large amounts of data of products, customers, as well as operational data. The companies need strong support in data engineering, business intelligence and reporting in order to turn the data into meaningful insights. The top IT companies in Indore help IT and SaaS firms in building scalable data systems, improving product performance tracking and making smarter business decisions. Services such as dashboards development, cloud data setups are offered by these firms. In this article we are highlighting the top 06 data analytics companies in Indore that are helping the mid sized IT and SaaS businesses grow through structured and reliable data solutions.

Learn More:- India’s Best Data Analytics Companies Ranked

2. What Is Data Analytics Consulting for IT, SaaS Businesses?

Data analytics consulting for IT and SaaS businesses means helping companies in using their data in order to make better decisions. Consultants organise and clean data, build dashboards and set up cloud data systems. They also create reports that show product performance, customer behaviour and revenue trends. Forecasting and automation is also improved using AI and advanced analytics.

3. Top Data Analytics companies in Indore

3.1) DataTheta

DataTheta is a trusted analytics and decision sciences consulting firm that works closely with mid size IT and SaaS companies in order to build strong and scalable data systems. They help the businesses by creating reliable and governed data environments that support product analytics, customer insights etc. instead of just creating dashboards. They have a team of good senior data experts with a solid track record in data analytics delivery. DataTheta enables SaaS and tech firms to use data more effectively for decision-making, performance measurement, and operational improvements.

DataTheta is relevant for IT and SaaS companies because-

  • They help in building scalable cloud based data architectures
  • They create dashboards for revenue and performance monitoring
  • Ensures data security and access control
  • Helps to improve the report accuracy using the governed models

Min project size - $5,000+

Hourly rate - < $25 / hr

Employees - 50 - 249

Year founded - Founded 2017

3.2) Fractal Analytics

Fractal Analytics is a global analytics company that helps large enterprises use the data in order to make smarter business decisions. Many fortune 500 companies work with fractal because they see analytics as a strong competitive advantage. Fractal majorly focuses on delivering clear insights, practical innovation etc. through predictive analysis and visual storytelling. This company consists of around 600 professionals that are working across locations such as retail, insurance, technology etc. 

What Fractal Analytics does

  • Helps the companies use data to  improve the business decisions.
  • Builds predictive models to forecast trends and outcomes
  • Ensures that the analytics solutions are fully implemented and used.
  • Use visual dashboards to make the insights easy to understand.  

Min project size - Undisclosed

Hourly rate - $25 - $49 / hr

Employees - 1,000 - 9,999

Year founded - Founded 2000

3.3)  Tiger Analytics

Tiger Analytics is a global data analytics and AI consulting company that helps businesses in using data in order to improve performance as well as decision making. Tiger Analytics represents the type of advanced analytics partner that supports growth, customer insights as well as scalable data systems, for mid size IT and SaaS companies. Tiger Analytics focuses on combining business understanding along with strong technical expertise to solve real world problems.

Key strength of Tiger Analytics -

  • Strong expertise in AI and machine learning
  • Focus on long term analytics adoption and not just reports
  • Has a great experience across technology, retail and financial services.

For mid size IT companies and SaaS companies in Indore, firms such as Tiger Analytics set a benchmark for building advanced, reliable and scalable analytics environments.

Min project size - Undisclosed

Hourly rate - Undisclosed

Employees - 10 - 49

Year founded - Founded 2010

3.4)  BestPeers

BestPeers is a software development company that helps businesses in building smart, reliable as well as scalable digital solutions. The company focuses on using the modern technologies in a practical way so organisations can improve efficiency, reduce the costs and grow faster in the digital world. BestPeers works with businesses of all sizes and their main goal is to become a long term technology partner, not just developing the software.

BestPeers work with advanced technologies such as -

  • Artificial Intelligence
  • Blockchain
  • Cloud computing
  • Full Stack web development

Key services offered -

  • UI/UX design and product design
  • Enterprise software development
  • Mobile App Development

Industries served -

  • Healthcare
  • eCommerce
  • Logistics
  • Education 
  • Finance

BestPeers stands out from the other industries because they have a strong focus on quality and timely delivery. They ensure clear communication and transparent processes. They have an agile development approach and they have a team of skilled developers, designers and consultants working together.

Founding Year - 2017

Team Size - 250-999

Cost of Services - $30-70/h

3.5) NextLoop Technologies LLP

NextLoop Technologies LLP is a software company in Indore that helps the businesses grow through smart and reliable IT solutions. The company mainly focuses on innovation, quality as well as practical technology that is used to solve real business problems. Their goal is to support companies in improving efficiency, increasing productivity etc. NextLoop technologies ensures that the businesses turn their ideas into digital products.

Key Services Offered -

  • Custom Software Development - Builds software based on specific business needs and focuses on performance, security and scalability.
  • Web and Mobile app development - Develops responsive websites and builds Android and iOS mobile apps ensuring smooth performance.
  • IT consulting services and cloud solutions - Offers secure cloud setup and reduces infrastructure costs. Reviews current IT systems and provides clear technology recommendations.

Businesses choose NextLoop Technologies because they provide customized solutions for different industries. They also focus on quality as well as timely delivery. They have a team of skilled developers and consultants that have a strong understanding of modern technology.

Founding Year - 2020

Team Size - 10-49

Cost of Services - $30-70/h

3.6) HData Systems 

HData systems is a global Big Data Analytics and Business Intelligence service provider. This company helps businesses in using data in a smart way so that they can grow faster as well as make better decisions. They turn raw data into useful insights using data science and advanced analytics. HData systems support companies by analysing market trends as well as business performance. Their main focus is on helping the clients in improving efficiency and also in increasing the revenue.

  • What does HData System does -
  • Collects and analyses large volumes of data
  • Converts complex data into simple reports and dashboards
  • Provide insights in order to improve the business strategies
  • Helps the businesses in improving competitor trends

They work with -

  • Startups
  • Large enterprises
  • Mid-sized companies

Services offered -

  • UI/UX Design
  • AI development
  • Data Science solutions
  • Big Data analytics
  • Data Visualisation

Companies use HData Systems because they have a strong experience in data analytics. They focus on improving ROI and provide reliable and structured data solutions.

Employees - 10 - 49

Hourly Rate - < $25

4. Conclusion

Indore is steadily growing as a strong technology and analytics hub for mid size IT and SaaS companies. As the businesses scale, the need for structured data systems, clear reporting as well as AI driven insights become more necessary. These top 6 analytics companies in Indore are helping Saas and IT firms in moving beyond basic dashboards and in building reliable, secure and scalable data environments. These services offer services such as data engineering, BI dashboards and predictive analysis. They help businesses track key metrics like product performance in a simple and organized way. Choosing the right analytics partner ensures better decision-making, improved operational efficiency, and long-term growth.

Top 10 Data Analytics Companies in Chennai for AI, BI & Data Engineering Services

1. Introduction

Chennai has grown into one of India's most important centres for data analytics, business intelligence as well as data engineering. Enterprises across industries such as finance, healthcare, retail, manufacturing etc. depend upon analytics teams based in Chennai to support both India focused as well as global operations. As the digital transformation is increasing rapidly, organisations are looking for analytics partners who build reliable data pipelines that do not break as the data volume grows. They should design BI platforms where business teams must trust the numbers and should support both structured as well as unstructured data. Chennai gets differentiated because it combines strong engineering talent and cost efficient delivery. This article highlights the top 10 data analytics firms in Chennai that offer end to end AI, BI and data engineering services as well. These firms help the businesses improve planning, track performance accurately and support better decision making.

Related Post:- Best data analytics companies in India

2. What Is Data Analytics in AI, BI & Data Engineering?

Data analytics basically means turning raw data into useful information that helps the businesses in making better decisions. This process includes collecting data from different systems, cleaning and organising the data and then analysing the data to understand what is happening and what is going to happen next. When data engineering, business intelligence, AI and advanced analytics are combined together, it becomes much more powerful. Data engineering mainly focuses on building reliable data pipelines. It ensures that  the data is collected, processed and made available. Business Intelligence turns this prepared data into reports and dashboards that can be used by the teams to track performance and also to review results. AI and advanced analytics use the same data to create forecasting models that help the businesses in faster response and to plan ahead. When all these three sectors come together then they build scalable data architectures that grow with the businesses.

3. Top 10 Data Analytics Firms in Chennai 

3.1 DataTheta

DataTheta is a Chennai based leading data analytics consulting company that is helping enterprises across industries like Pharma, Healthcare, Retail/CPG, Energy, and BFSI. The Chennai team at DataTheta specializes in transforming fragmented enterprise data into unified, analytics-ready platforms. It further supports faster decision-making and measurable business outcomes. The company provides end-to-end services including Data Analytics, Business Intelligence, Data Engineering & Warehousing, Data Science, and GenAI solutions. DataTheta is known for its strong focus on governance, compliance, security and scalability. The DataTheta team of senior data engineers & scientists brings together more than 10 years of average experience. DataTheta combines industry expertise with flexible engagement models, some of them are- fixed-time projects, managed services, and developers-on-demand (also known as fixed time resource). This approach helps the businesses to modernize their analytics capabilities efficiently along with properly maintaining control over cost and better performance.

3.2 Fractal Analytics

Fractal Analytics is a Chennai based, well-established data analytics company that has well-developed analysis, forecasting, and measurement of activities. The company helps both SMB and large enterprises in the medical field, financial services, retail and technology sector. It provides a wide range of analytics tools and modeling solutions. Fractal analytics in Chennai provides services that are categorized into data preparation, the creation of machine learning models, and the final deployment of analytics. Fractal analytics follow an result oriented approach that is aimed at providing key insights for proper business planning processes. Clients trust on Fractal analytics when they require some help with the performance evaluation systems, trend analysis and scalable reporting solutions that are reliable and in accordance with the enterprise requirements.

3.3 LatentView Analytics

LatentView Analytics is another well known multinational data analytics company that has a solid presence in Chennai. It works in the sectors such as retail, finance, technology, and consumer goods. LatentView also provides high level business analytics services like customer-segments, predictive modeling, optimization methods, and data engineering services. LatentView is a consulting firm in Chennai that uses technical expertise and consulting experience to help the organizations in developing analytics processes that are linked to business planning cycles. Its solutions are fully customized to enhance the accuracy of reporting, expose the useful patterns, and better the overall decision support between functions. LatentView works with quantifiable analytics results as well as reporting systems that help in long-term performance monitoring.

3.4 Tiger Analytics

When the enterprises need an analytics system to connect business metrics across teams rather than operating as isolated dashboards, they go for Tiger Analytics. Instead of building dashboards, this company in Chennai focuses on making sure that the numbers behind those dashboards are correct and consistent and can be easily trusted across the business. They help the enterprises in defining right business KPIs and keeping the data pipelines stable so that the reports don’t break. They also monitor the data issues early and use SQL and Python to transform raw data into something usable.

3.5 Tredence

Tredence is one of the best top data analytics services providers in Chennai and it is also a popular data science and analytics advisory firm. They provide data engineering, analytics model implementation, and forecasting systems offered by the company to help their clients in improving the accuracy of the planning and performance evaluation. Tredence collaborates with every type of business to implement analytics processes & solutions that can be used to achieve quantifiable results and performance data. Tredence provides its services to consumer goods, health and technology industries. Its strength lies in the well structured delivery and clarity of insights that are often praised by organizations.

3.6 Absolutdata

Absolutdata provides customer analytics and behavioral modeling and data based decision support consulting and services to Chennai based clients. The company provides the features of segmentation analysis, data preparation, predictive models, and performance reporting dashboard. Absolutdata helps the businesses from different industries like retail sector, financial services and technology in improving their understanding of customer behaviour and quantifying results in the key performance areas. Its services facilitate unceasing enhancement of analytics solutions and the augmented view of performance indicators. Absolutdata has a strong delivery presence that makes it a preferred analytics partner for global enterprises. They have a highly skilled analytics talent, provide cost-effective delivery, strong domain expertise, and deep expertise in providing scalable project execution on time.

3.7 Genpact

Genpact is another well known name in providing top notch analytics solutions in Chennai. It helps in building scalable & robust data analytics programs which improves the optimal operational measurements, risk assessment and reporting systems. Genpact key services include data preparation, implementation of analytics, and performance tracking systems which helps the enterprises. The Genpact analytics team in Chennai works in emerging sectors such as finance, supply chain, and compliance sectors where reliability and sound measurement is needed. Genpact helps enterprise businesses to transform via data analytics, artificial intelligence (AI), cloud technologies, and automation. They have clients from different industries like finance, retail, healthcare, supply chain, and manufacturing.

3.8 Cognizant Analytics

Cognizant is another leading global information technology and data consulting company that has offices in Chennai. It further serves to help in preparing data, analytics workflow implementation, and performance evaluation. The experienced team at Cognizant Chennai help businesses to create analytics models, combine reporting systems and enhance the delivery of insights. Cognizant works with healthcare, banking, logistics, and retail clients and helps them to consolidate analytics practices and improve the accuracy of the planning. Cognizant offers a wide range of analytics and data-driven services to solve their clients' business problems.

3.9 WNS Analytics

WNS Analytics is a Chennai based data analytics company that gives proper performance measurement solutions. This includes- predictive modelling, reporting structures, and trend analysis. The firm serves clients in the retailing, insurance and healthcare industries. WNS Analytics' experienced team utilizes proper domain experience and organized analytics operations that helps the organizations in improving the visibility of performance and proper accuracy of reporting at functional levels. WNS analytics integrates data, analytics, AI, and human expertise to help businesses extract key important insights, modernize data infrastructure, and enable smarter decision making. WNS utilizes AI and analytics with domain expertise to deliver business outcomes rather than just dashboards.

3.10 Deloitte Analytics

Deloitte’s analytics in Chennai usually works with large enterprises that operate complex, regulated or global data environments where data issues can quickly turn into business risks. Their main goal is to help the organisations in bringing structure and consistency to analytics at scale. Deloitte in Chennai supports enterprises in building secure and governed data pipelines as well as aligning KPIs across the teams. Deloitte’s analytics is capable of using unified and governed BI layers in order to eliminate KPI conflicts, securing data pipelines for regulated environments etc. When enterprises need long term operational use, they choose Deloitte. Their main strength is in building governance frameworks that remain stable.

4. Pricing and Selection Criteria for Right Data Analytics & BI Companies In Chennai

4.1 Pricing

Pricing for the data analytics and BI services mainly depends upon what you need, how complex your data is and how long the work will run. Some firms charge for a fixed scope project that works well when the requirements are clear. Others work on a monthly model that means the teams support ongoing improvements, reporting and fixing. To avoid conflicts, there should be clear deliverables, timelines as well as ownership from the start.

4.2 Client Reviews & Case Experience

Client reviews and case experience help you to understand how reliable a  data analytics or a BI firm actually is. Testimonials don’t only show the building of dashboards, but it also shows whether there is timely delivery by the firm or not, whether the firm solved the real business problems or not. Case studies are equally important as they show what problem was addressed, how the data was used and what results were achieved. When a client returns for more work, it’s usually a sign of consistent delivery as well as good work, so one should also look for repeat engagements.

4.3 Industry Domain Expertise

Industry domain expertise plays a crucial role in deciding the usefulness of analytics outcomes, that means the analytics partner already understands how your business works. When the consulting partner already understands the business domain, they don’t need long explanations about basic terms, data sources and workflows. For example, a company experienced in healthcare is familiar with patient data as well as clinical operations. This familiarity helps in faster decision making and more relevant insights.

4.4 Data Security & Governance

Data security and governance means keeping your data safe, controlled and trustworthy while it is being used for analytics and BI. This is more important when the data includes customer details, financial records etc. A good analytics partner always explains how the data is protected at every step. Security also includes access controls. Governance always focuses on how the data is defined and used, and supports audits and tracking showing the actual source of data and how it changes.

4.5 Technical Capability

Technical capability means the analytics firm has the right tools and skills to handle the data properly from the beginning to the ending. A strong firm manages data engineering that means the data is collected from different systems, cleaned and gets prepared for analysis. They should also be good at building dashboards that are easy to understand as well as reliable. Compatibility with platforms like AWS, Snowflake, Azure is also very necessary because many platforms use these tools.

4.6 Long-Term Support

Analytics is not a one time setup. A firm with long term support monitors data pipelines, fixes failures and ensures that the reports show the right KPIs. Good partners also help in tracking performance, improving speed and adapting analytics when new data sources or users are added. Long term support helps in supporting real business decisions.

5. Conclusion

The data analytics ecosystem in Chennai is still growing at a rapid pace with more enterprises needing to find the structured analytics support, prediction systems, and performance measurement tools. In the above article, I have listed some of the reliable companies in Chennai that provide data analytics services like data engineering, predictive modeling, reporting structures, consulting etc. They have prior experience in working with small & medium size (SMB) and large scale enterprises in solving their business problems using the help of advanced technology stack. You can check these analytics companies capabilities, pricing structure, data engineer experience & expertise and take a final call while selecting the best data analytics partner in India for your business.

Top 10 Data Analytics Companies in Hyderabad: Enterprise Solutions & AI Experts

1. Introduction

Hyderabad has now become one of India's strongest hubs for data analytics, data engineering, BI, and AI execution especially for IT services and technology based businesses. Nowadays tech organisations are generating data from many sources such as through customer platforms, ERPs, Billing systems and cloud data warehouses. Even after having all this data the teams are still struggling to transform this data into meaningful insights. Some common challenges include things like quite breaking of data pipelines without any alert, driving up cloud costs due to slow queries, schema changes that cause dashboards to fail etc. Due to these reasons the companies want partners who take responsibility for making analytics work consistently, not for the analytics vendors who just build dashboards. The modern enterprises in Hyderabad choose an analytics firm that can keep the data pipelines reliable as well as monitored, ensure security of cloud data environments, ensure that KPIs mean the same thing across the teams etc. Nowadays analytics must support product planning, customer intelligence, cost control and long term decision ownership, they want all these things without confusion. This article highlights the top 10 data analytics companies in Hyderabad that can be easily trusted by the businesses when analytics need systems to scale and also to support real business planning.

Related Post:- Top data analytics services firms in India

2. What Is Data Analytics in Hyderabad?

Data analytics means using data to understand what is happening and to make better decisions. This process includes collecting data, cleaning  data and then studying it to find patterns as well as trends. Organisations use data analytics services in Hyderabad for better planning, predicting future outcomes, tracking performance and reducing risks. It also supports things like dashboards, track performance, cloud data platforms and business reports. If we understand it simply, then data analytics turn raw data into clear information that helps the teams and leaders in making better as well as informed decisions.

3. Top 10 Best Data Analytics Companies in Hyderabad 

3.1 DataTheta

DataTheta is a decision sciences and analytics consulting firm that is headquartered in Texas USA, and has delivery centres in Hyderabad. When the enterprises need reliable, governed as well as scalable analytics environments, and not just dashboards, they choose DataTheta. DataTheta focuses on building analytics systems that help the teams for daily planning and decision making. This includes cloud platforms, SQL and Python pipelines, Continuous monitoring of data quality etc. When enterprises across IT, Saas, Healthcare and industrial sectors look for companies that support real planning cycles, and not just reporting, they choose DataTheta.

3.2 Evalueserve

Evalueserve is another well known name in providing top notch analytics solutions in Hyderabad. It helps in building scalable & robust data analytics programs which improves the optimal operational measurements, risk assessment and reporting systems. Key services of Evalueserve include data preparation, implementation of analytics, and performance tracking systems which helps the enterprises. The Evalueserve team works in emerging sectors such as finance, supply chain, and compliance sectors where reliability and sound measurement is needed. Evalueserve helps enterprise businesses to transform via data analytics, artificial intelligence (AI), cloud technologies, and automation. They have clients from different industries like finance, retail, healthcare, supply chain, and manufacturing.

3.3 Gramener

Gramener is an India based analytics company headquartered in Hyderabad that helps the organisations in understanding the data through clear visuals, machine learning models as well as cloud based analytics systems. Gramener focuses on making data easy to see, understand and act on. They build dashboards that help to track the business performance, models that predict the outcomes, and also the analytics system that supports daily planning operations. This firm mainly works with industries such as banking, manufacturing, retail, etc. This company is mainly famous for visual intelligence as well as practical analysis as they turn complex data into insights that can be easily seen by the business teams.

3.4 Sutherland Analytics

Sutherland analytics is a part of Sutherland, a global services company that helps the large organizations in running and managing analytics at a scale. From its Hyderabad delivery teams, it supports business with forecasting models, secure data platforms and machine learning systems. Sutherland doesn't only build the analytics systems, they also make sure that analytics keep running smoothly everyday. They mainly work with industries such as insurance, healthcare, logistics where data systems must always be available and reliable. When the enterprises need a partner to own analytics end to end and to handle the ongoing reporting they choose Sutherland. Sutherland ensures that the data systems continue to support decisions over time.

3.5 Merilytics

Merilytics is an India based analytics and AI company that is headquartered in Hyderabad. It helps the businesses plan better and see the performance clearly using data, machine learning models as well as automated reports. Merilytics helps the companies in understanding customers, predicting future demands, and in tracking business performance through dashboards as well as analytics systems.  They also build cloud data pipelines that keep the data flowing smoothly and also models that support accurate forecasting. Merilytics works with sectors such as finance, retail and healthcare organisations where good planning and visibility is clear. When the enterprises seek for reliable analytics in order to improve decision making and planning accuracy, they go for Merilytics.

3.6 Tiger Analytics

When the enterprises need an analytics system to connect business metrics across teams rather than operating as isolated dashboards, they go for Tiger Analytics. Instead of building dashboards, this company focuses on making sure that the numbers behind those dashboards are correct and consistent and can be easily trusted across the business. They help the enterprises in defining right business KPIs and keeping the data pipelines stable so that the reports don’t break. They also monitor the data issues early and use SQL and Python to transform raw data into something usable.

3.7 SG Analytic

SG analytics is one of the best data analytics and research service companies that works with global enterprises to turn data into clear insights and also business usable insights. SG analytics has a delivery and hiring presence in Hyderabad that supports analytics work for clients. SG analytics helps the companies in understanding their data and in making better decisions. Their team works on data analytics, business intelligence, advanced analytics etc. They also ensure that their outputs are more business focused and not just technical. SG analytics helps industries such as healthcare, retail, technology etc. When the enterprises need reliable analytics support and ongoing reporting they often go for SG analytics.

3.8 Accenture Analytics

Accenture is a popular name in offering global business and analytics services through technology, transformation, innovation. Accenture provides advanced level data analytics services to enterprise clients of Hyderabad in terms of data platform strategy, analytics implementation and reporting systems. The company operates in areas like the technological, medical, financial, and production industry. Accenture services are used by organizations to easily consolidate the data sources, enhance the accuracy of predictions, and support decision-making processes in the context of digital transformation initiatives. The team at Accenture utilizes a broader data & AI practice with primary focuses on turning data into insights that drive decision making, improve performance, and enable digital transformation.

3.9 EY Analytics

EY analytics is a part of EY (Ernst and Young). It helps large organisations in using data in a structured, secure and well governed way. The Hyderabad team of EY supports businesses with forecasting models, cloud data platforms and reporting systems. Ey helps the companies in setting up analytics in the right way so that the numbers remain consistent, trusted and also aligned with the business planning. They also focus on data governance that means they set clear rules on how data is collected, used, secured as well as reported. The enterprises that need analytics to match planning cycles, financial reporting etc., choose EY. This firm helps the businesses and the organisations in building reliable, secure and also well governed analytics systems that can be easily trusted by the leaders for planning as well as for daily decision making.

3.10 PwC Analytics

PwC analytics is a part of PwC (PricewaterhouseCoopers). They help large organisations in using data for planning and tracking performance clearly. The Hyderabad team of PwC supports the business with forecasting, reporting, cloud data systems etc. PwC helps the companies to collect data, organise data and turn it into clear insights and reports that can be easily trusted by the leaders. They also support AI and advanced analytics and also make sure that the data is secure and follow the governance rules. PwC works with industries such as healthcare, retail, finance and manufacturing where accurate data is critical.

4. Pricing and Selection Criteria to select the right data analytics company in Hyderabad

1. Pricing Model-

Pricing for the data analytics and BI services mainly depends upon what you need, how complex your data is and how long the work will run. Some firms charge for a fixed scope project that works well when the requirements are clear. Others work on a monthly model that means the teams support ongoing improvements, reporting and fixing. To avoid conflicts, there should be clear deliverables, timelines as well as ownership from the start.

2. Client Reviews  - 

Client reviews help you to understand how reliable a  data analytics or a BI firm actually is. Testimonials don’t only show the building of dashboards, but it also shows whether there is timely delivery by the firm or not, whether the firm solved the real business problems or not. When a client returns for more work, it’s usually a sign of consistent delivery as well as good work, so one should also look for repeat engagements.

3. Industry Expertise - 

Industry domain expertise plays a crucial role in deciding the usefulness of analytics outcomes, that means the analytics partner already understands how your business works. When the consulting partner already understands the business domain, they don’t need long explanations about basic terms, data sources and workflows. For example, a company experienced in healthcare is familiar with patient data as well as clinical operations. This familiarity helps in faster decision making and more relevant insights.

4.Data Security - 

Data security and governance means keeping your data safe, controlled and trustworthy while it is being used for analytics and BI. This is more important when the data includes customer details, financial records etc. A good analytics partner always explains how the data is protected at every step. Security also includes access controls. Governance always focuses on how the data is defined and used, and supports audits and tracking showing the actual source of data and how it changes.

5.Technical Capability - 

Technical capability means the analytics firm has the right tools and skills to handle the data properly from the beginning to the ending. A strong firm manages data engineering that means the data is collected from different systems, cleaned and gets prepared for analysis. They should also be good at building dashboards that are easy to understand as well as reliable. Compatibility with platforms like AWS, Snowflake, Azure is very necessary because many platforms use these tools.

5. Conclusion

The data analytics ecosystem in India is still growing at a rapid pace with more enterprises needing to find the structured analytics support, prediction systems, and performance measurement tools. In the above article, I have listed some of the reliable companies in India that provide data analytics services like data engineering, predictive modeling, reporting structures, consulting etc. They have prior experience in working with small & medium size (SMB) and large scale enterprises in solving their business problems using the help of advanced technology stack. You can check these analytics companies capabilities, pricing structure, data engineer experience & expertise and take a final call while selecting the best data analytics partner in India for your business.

Top 10 Data Analytics Companies in Bengaluru/ Bangalore for 2026 (Rankings, Services, Reviews)

1. Introduction

In the past couple of years, Bangalore has established itself as a big hub in the market for providing high quality data analytics and consulting services. It is attracting both local and international companies that are looking for advanced-level analytics solutions to better analyse their business and for growth. The use of data is still very important for the organisation’s to properly plan, measure performance and strategise the future decision making. With digital platforms having been adopted rapidly at large scale, today companies are heavily investing in analytics based solutions that are more than simple reporting, for better performance analysis, and for high level operational assistance. This has pushed the demand for the analytics partners in Bangalore to help in the structured engineering of data, predictive modelling, analytics implementation, and regular reporting frameworks. In this blog, I will share with you an in-depth list of the 10 best data analytics companies in Bangalore on the basis of their key services, customer feedback, experience in the industry, and analytics performance results. This will help you to select the right data analytics services provider in Bangalore that solves your business problems and helps in achieving more revenue for your business.

Related Post:- Leading data analytics service providers in India

2. What Is Data Analytics in Bangalore?

Data analytics is the process of collecting, preparing and analyzing the business data in order to discover certain types of hidden trends, patterns and insight. It helps in the proper planning and making decisions for the end business growth. In Bangalore, data analytics basically consists of data integration, predictive modeling, performance measurement, visual reporting and a list of tools & technologies to facilitate the business objectives of various departments. Leading data analytics companies in Bangalore utilize a combination of tools & technologies to help different industries like healthcare, energy, E-commerce & logistics, Manufacturing, Banking & FinTech etc. to solve their business problems.

3. Top 10 Best Data analytics companies in Bangalore / Bengaluru

3.1 DataTheta

DataTheta is a Bangalore based analytics consulting company that helps the enterprises to use data in a structured and in a practical way. They work with organisations such as financial, retail, media etc. to build reliable data systems that support forecasting, reporting as well as regular decision making. They help the companies in bringing data together from multiple sources and then turning the data into clear insights, enabling better customer understanding, improved performance tracking and stronger risk analysis. The team works closely with leadership, product and operations teams so analytics directly supports business planning and measurable outcomes. When the organisations need structured delivery, secure handling of data and long term analytics of ownership, they believe in choosing DataTheta.

3.2 ScienceSoft

ScienceSoft is a global IT and software solutions company that has a good experience in technology. This company includes services like data analytics, data science, business intelligence and data engineering. ScienceSoft serves many industries such as healthcare, manufacturing, banking, retail etc. This is one of the best data analytics companies in Bangalore because it provides full analytics services, that too from strategy to delivery. It supports both BI as well as advanced analytics. It helps the organisations in collecting and cleaning the data, building reports and dashboards, integrating and managing data sources etc. Their data science services include consulting and strategy that means they help the companies in choosing the right kind of analytics that is needed. It also includes implementation and long term support that means it builds dashboards and data platforms and evolves the analytics over time. This firm works with many large enterprises globally.

3.3 RadixWeb

RadixWeb is a Bengaluru based company that helps the organisations in providing analytics as well as data solutions. They support  end to end analytics work that includes data strategy, analytics dashboards, data engineering etc. They help in building systems that trunks the data into clear insights and smarter decision making tools. When the businesses or the companies look for reliable analytics and data driven transformation support, they usually go for RadixWeb. It helps the businesses in combining the data from various sources, preparing it for reporting, building analytics dashboards etc.

3.4 DataForest

DataForest is a tech based company that helps businesses in using data and analytics in order to make better decisions. They help in building the systems that collect data, clean it, organize it and then turn it into useful insights through reporting, dashboards, analytics tools etc. DataForest in Bangalore helps the companies that are looking for analytics as well as AI help. Dataforest helps in management that means they take raw data from various sources and clean it and make it ready to use. It also helps in data engineering, analytics and dashboards and AI driven solutions. This means that they build the systems so data flows smoothly and turn the data into visuals and charts that can be easily used by people to understand. They also use artificial intelligence to automate the tasks.

3.5 Mu Sigma

Mu Sigma is another well known decision sciences and analytics consulting firm that offers formal analytics and performance measurement consulting to multinational corporations located in India and other Indian cities. Mu Sigma has a substantial presence in Bangalore and helps the companies in addressing complicated business challenges by using statistical methods, organized assessment, and replicable analytics models. Mu Sigma popular services include risk analysis, forecasting systems and operations measurement. Mu Sigma is well trusted by large scale business organizations who follow a proper disciplined analytics execution and regular performance tracking among business units. They have offices located in all the major IT hubs of India.

3.6 Iqvia

Iqvia is a very large global company that has a big presence in India, including operations in Bangalore that used data advanced analytics, technology as well as expertise to help industries such as healthcare, biotech, pharmaceuticals etc. Iqvia helps in collecting and organising huge amounts of health related data from clinics, prescriptions etc. After collecting it uses analytics, AI and machine learning to turn that data into useful insights. It also offers platforms and tools that help the companies see dashboards and recognise the patterns quickly. It is one of the lead data analytics companies because it handles very large and complex data sets and applies deep analytics as well as AI.

3.7 DataRobot

DataRobot is a popular enterprise AI and automated analytics platform that helps businesses in  building, deploying and managing predictive models quickly. DataRobot automates the machine learning process so that companies can make faster decisions, instead of manually coding everything. DataRobot builds and runs the machine learning model so businesses can turn their data into useful insights as soon as possible. DataRobot provides automated intelligence that means it automatically tries different model types on the data and after finding the best one, it builds them without doing heavy coding. Not just data scientists, even business analysts can use it to understand trends and forecast outcomes.

3.8 WNS Analytics

WNS Analytics is a Bangalore based data analytics company that gives proper performance measurement solutions. This includes- predictive modelling, reporting structures, and trend analysis. The firm serves clients in the retailing, insurance and healthcare industries. WNS Analytics' experienced team utilizes proper domain experience and organized analytics operations that helps the organizations in improving the visibility of performance and proper accuracy of reporting at functional levels. WNS analytics integrates data, analytics, AI, and human expertise to help businesses extract key important insights, modernize data infrastructure, and enable smarter decision making. WNS utilizes AI and analytics with domain expertise to deliver business outcomes rather than just dashboards.

3.9 Evalueserve

Evalueserve is another well known name in providing top notch analytics solutions in Bangalore. It helps in building scalable & robust data analytics programs which improves the optimal operational measurements, risk assessment and reporting systems. Key services of Evalueserve include data preparation, implementation of analytics, and performance tracking systems which helps the enterprises. The Evalueserve team works in emerging sectors such as finance, supply chain, and compliance sectors where reliability and sound measurement is needed. Evalueserve helps enterprise businesses to transform via data analytics, artificial intelligence (AI), cloud technologies, and automation. They have clients from different industries like finance, retail, healthcare, supply chain, and manufacturing.

3.10 phData

phData is a data and analytics services company in Bangalore that helps the businesses in collecting, organizing and using their data in order to make better decisions.Their major focus is on data engineering, analytics, AI, cloud data platforms and machine learning. phData has an office in Bangalore and a growing team that works on data analytics projects. phData is one of the best data analytics companies in Bangalore because it helps the companies in solving real data challenges end to end. They don’t just work on simple reporting, instead they work on modern data platforms, AI , and cloud analytics. phData is like a data expert partner that helps the businesses organise their data, get useful insights and build smart analytics and AI systems.

4. Data Analytics Firm Pricing and Selection Bangalore / Bengaluru

4.1 Pricing Model

Pricing for the data analytics and BI services mainly depends upon what you need, how complex your data is and how long the work will run. Some firms charge for a fixed scope project that works well when the requirements are clear. Others work on a monthly model that means the teams support ongoing improvements, reporting and fixing. To avoid conflicts, there should be clear deliverables, timelines as well as ownership from the start.

4.2 Reviews and References of Clients

Client reviews help you to understand how reliable a  data analytics or a BI firm actually is. Testimonials don’t only show the building of dashboards, but it also shows whether there is timely delivery by the firm or not, whether the firm solved the real business problems or not. When a client returns for more work, it’s usually a sign of consistent delivery as well as good work, so one should also look for repeat engagements.


4.3 Industry Domain Expertise

Industry domain expertise plays a crucial role in deciding the usefulness of analytics outcomes, that means the analytics partner already understands how your business works. When the consulting partner already understands the business domain, they don’t need long explanations about basic terms, data sources and workflows.For example, a company experienced in healthcare is familiar with patient data as well as clinical operations. This familiarity helps in faster decision making and more relevant insights.

4.4 Technical Capability

Technical capability means the analytics firm has the right tools and skills to handle the data properly from the beginning to the ending. A strong firm manages data engineering that means the data is collected from different systems, cleaned and gets prepared for analysis. They should also be good at building dashboards that are easy to understand as well as reliable. Compatibility with platforms like AWS, Snowflake, Azure is also very necessary because many platforms use these tools.

4.5 Data Governance and Data Security

Data security and governance means keeping your data safe, controlled and trustworthy while it is being used for analytics and BI. This is more important when the data includes customer details, financial records etc. A good analytics partner always explains how the data is protected at every step. Security also includes access controls. Governance always focuses on how the data is defined and used, and supports audits and tracking showing the actual source of data and how it changes.

4.6 Long-term Support and Ownership.

Analytics systems need ongoing monitoring and refinement. Choose firms that offer continuous support, model updates, and performance tracking beyond initial deployment.

5. Conclusion

Bangalore / Bengaluru is a strong hub for data analytics and BI services, with many firms that support the enterprises in forecasting, customer insights and cloud data integration. Many companies in Bangalore offer similar analytics capabilities, but we can differentiate between a good and a bad company on the basis of some factors such as industry understanding, clear ownership of models as well as secure data handling. The firms listed above are trusted by sectors such as finance, retail and media because these firms deliver structured data pipelines and reliable BI reporting. Choosing a right data analytics partner in Bengaluru is very important as it leads to smoother planning and better adoption of analytics, and becomes a long term partner that helps in reducing uncertainty and improving decision making.

Top 10 Data Analytics Companies in Mumbai for Financial, Retail & Media Enterprises

1. Introduction

Mumbai is one of India’s business as well as financial cities. Many companies in finance, retail and media are based here, and they generate large amounts of data everyday from ERP systems, digital media and cloud tools etc. The financial companies use data to manage risk and to prepare actual and accurate reports. Retail businesses analyse the customer behaviour as well as sales trend. Media companies study audience engagement and content performance. The businesses need analytics partners that help to organise the data properly and turn the data into clear insights. Mumbai has a strong group of data analytics companies that help the enterprises in building a reliable analytics system. These firms mainly focus on technical expertise and clear responsibility along with long term support. When the organisations choose a right data analytics partner, the organisations can use the data with confidence to improve planning, performance and also decision making.

2. What Is Data Analytics?

Data analytics in Mumbai means using data to understand what is happening and to make better decisions. This process includes collecting data, cleaning  data and then studying it to find patterns as well as trends. Organisations use data analytics for better planning, predicting future outcomes, tracking performance and reducing risks. It also supports things like dashboards, track performance, cloud data platforms and business reports. If we understand it simply, then data analytics turn raw data into clear information that helps the teams and leaders in making better as well as informed decisions.

3. Top 10 Data Analytics Companies in Mumbai

3.1 DataTheta

DataTheta is a Mumbai based analytics consulting company that helps the enterprises to use data in a structured and in a practical way. They work with organisations such as financial, retail, media etc. to build reliable data systems that support forecasting, reporting as well as regular decision making. They help businesses located in Mumbai (India) in bringing data together from multiple sources and then turning the data into clear insights, enabling better customer understanding, improved performance tracking and stronger risk analysis. The team works closely with leadership, product and operations teams so analytics directly supports business planning and measurable outcomes. When the organisations need structured delivery, secure handling of data and long term analytics of ownership, they believe in choosing DataTheta.

3.2 Fractal Analytics

Fractal Analytics helps large organisations by using analytics to improve forecasting, customer understanding as well as business planning. The Mumbai team of fractal analysis supports enterprises by building predictive models, BI dashboards as well as reporting systems that can be used together by different departments. They usually work with clients in healthcare, finance, retail etc. Most of the enterprises choose fractal when they need analytics programs that scale across business units and to improve forecast accuracy and also to support long term decision making.

3.3 Mu Sigma

Mu Sigma is an analytics and decision science consulting company that helps large enterprises in solving large and complex business problems using data. It also focuses on structured analysis as well as repeatable frameworks. Mu Sigma’s Mumbai teams work with organisations such as retail, media that manage large data programs. The firm builds statistical models, data pipelines etc. to support forecasting, risk analysis and performance measurement. Mu Sigma is mostly chosen when the organisations need consistent analytics delivery, clear model ownership as well as long term support.

3.4 Latent View Analytics

LatentView Analytics is an analytics company in Mumbai that helps the businesses in bringing the data together from ERP systems, digital platforms and customer channels, and make better use of their data. Their team works with organisations to bring data from different systems into one place that makes it easier to understand. LatentView helps companies by grouping their customers together and also by predicting future trends. They also create dashboards that help the team in tracking important KPI’s,  and also help in making better decisions. Most of the organisations use Latent View Analytics when they want  structured analytics delivery.

3.5 Tiger Analytics

Tiger Analytics helps the companies use data and AI to improve their planning strategy and to run their businesses. The Mumbai team of Tiger analytics work with retail, healthcare and media organisations to build reliable analytics systems. This firm supports enterprises in building modern data platforms and also by building or developing BI dashboards upon which the teams can rely easily. They also make sure that analytics do not get separated by business operations so they work closely with the business as well as the technical teams. Enterprises mainly use Tiger Analytics when they seek support such as product performance, customer insights, supply planning etc.

Related Post:- India’s top data analytics consulting companies

3.6 WNS Analytics

WNS Analytics in Mumbai helps enterprises use the data to improve reporting, forecasting as well as business performance. Their team in Mumbai mainly works with companies in insurance, retail , travel and media. WNS helps the organisation in tracking KPIs, in understanding customer patterns, in forecasting demands and in bringing data from multiple systems. The organisations mainly choose WNS whenever they need scalable and consistent analytics, clear reporting across teams and long term ownership if performance tracking.

3.7 Bridgei2i

Bridgei2i helps enterprises use AI and analytics to improve planning, measurements and decision making. Bridgei2i builds forecasting systems, BI dashboards and cloud data pipelines that are directly connected to business planning. The services of Bridgei2i include data preparation, KPI reporting etc. Organisations mainly use Bridgei2i when they need customer insights, operational tracking as well as forecasting across the teams.

3.8 Capgemini

Capgemini helps the enterprises in Mumbai in building and managing large scale analytics and BI systems. They usually work with organisations in manufacturing, retail, logistics, finance etc. Capgemini supports BI dashboards, data engineering pipelines and predictive analytics. Enterprises choose capgemini when they need scalable analytics delivery as well as strong data governance. The main focus is on long term analytics ownership as well as on reliable systems that support planning and performance.

3.9 Cognizant Analytics

Cognizant Analytics helps the enterprises to use data in order to improve planning as well as business performance. Its team basically works on building pipelines and creating predictive dashboards. They also help in building reliable dashboards so that they can be easily trusted by the teams and can be used by the teams on a regular basis. Cognizant helps the organisations by connecting cloud data platforms and also by deploying machine learning solutions. 

3.10 KPMG Analytics

KPMG Analytics helps the enterprises in Mumbai use analytics in a secure and well governed way. The firm mainly works with organisations in finance, healthcare, retail, industrial sector etc. They specially work where the data is sensitive or highly regulated. KPMG analytics support BI dashboards, forecasting models, risk focused analytics etc. They help the organization in setting up strong data governance as well as controlled data pipelines in order to make analytics accurate and audit ready.

4. Conclusion

Mumbai is a strong hub for data analytics and BI services, with many firms that support the enterprises in forecasting, customer insights and cloud data integration. Many companies in Mumbai offer similar analytics capabilities, but we can differentiate between a good and a bad company on the basis of some factors such as industry understanding, clear ownership of models as well as secure data handling. The firms listed above are trusted by sectors such as finance, retail and media because these firms deliver structured data pipelines and reliable BI reporting. Choosing a right data analytics partner is very important as it leads to smoother planning and better adoption of analytics, and becomes a long term partner that helps in reducing uncertainty and improving decision making.

Top 10 Data Analytics Companies in Noida for IT, SaaS & Tech-Based Businesses

1. Introduction

Noida has now become one of India's strongest hubs for data analytics, data engineering, BI, and AI execution especially for IT services, SaaS companies and technology based businesses. Nowadays tech organisations are generating data from many sources such as through customer platforms, ERPs, Billing systems and cloud data warehouses. Even after having all this data the teams are still struggling to transform this data into meaningful insights. Some common challenges include things like quite breaking of data pipelines without any alert, driving up cloud costs due to slow queries, schema changes that cause dashboards to fail etc. Due to these reasons the companies want partners who take responsibility for making analytics work consistently, not for the analytics vendors who just build dashboards. The modern enterprises in Noida choose an analytics firm that can keep the data pipelines reliable as well as monitored, ensure security of cloud data environments, ensure that KPIs mean the same thing across the teams etc. Nowadays analytics must support product planning, customer intelligence, cost control and long term decision ownership, they want all these things without confusion. This article highlights the top 10 data analytics companies in Noida that can be easily trusted by the businesses when analytics need systems to scale and also to support real business planning.

2. What Is Data Analytics Consulting for IT, SaaS & Tech Businesses?

Data analytics companies consulting for IT, SaaS and technology businesses is all about building and running data systems that the teams can actually trust and can use everyday. Consultants help the companies in turning raw product, customer as well as business data into reliable insight systems that can support real decisions. They make sure that the reports and models stay accurate as the system changes by setting up data pipelines, cleaning and transforming data using SQL and Python and preparing data for AI and machine learning models. A strong analytics consulting company covers factors like reliable data pipelines, data models that are able to handle schema changes without breaking dashboards, monitoring for data delays etc. Consulting focuses on long term ownership and not one time delivery.

3. Top 10 Data Analytics Companies in Noida

3.1 DataTheta

DataTheta is a decision sciences and analytics consulting firm that is headquartered in Texas USA, and has delivery centres in Noida and Chennai. When the enterprises need reliable, governed as well as scalable analytics environments, and not just dashboards, they choose DataTheta. DataTheta focuses on building analytics systems that help the teams for daily planning and decision making. This includes cloud platforms, SQL and Python pipelines, Continuous monitoring of data quality etc. When enterprises across IT, Saas, Healthcare and industrial sectors look for companies that support real planning cycles, and not just reporting, they choose DataTheta.

3.2 Deloitte Analytics Practice

Deloitte’s analytics practice in Noida usually works with large enterprises that operate complex, regulated or global data environments where data issues can quickly turn into business risks. Their main goal is to help the organisations in bringing structure and consistency to analytics at scale. The Deloitte team in Noida supports enterprises in building secure and governed data pipelines as well as aligning KPIs across the teams. Deloitte’s analytics is capable of using unified and governed BI layers in order to eliminate KPI conflicts, securing data pipelines for regulated environments etc. When enterprises need long term operational use, they choose Deloitte. Their main strength is in building governance frameworks that remain stable.

3.3 Tiger Analytics

When the enterprises need an analytics system to connect business metrics across teams rather than operating as isolated dashboards, they go for Tiger Analytics. Instead of building dashboards, this company focuses on making sure that the numbers behind those dashboards are correct and consistent and can be easily trusted across the business. They help the enterprises in defining right business KPIs and keeping the data pipelines stable so that the reports don’t break. They also monitor the data issues early and use SQL and Python to transform raw data into something usable.

3.5 Fractal Analytics

Fractal Analytics is a well-established data analytics company in Noida that has well-developed analysis, forecasting, and measurement of activities. The company helps both SMB and large enterprises in the medical field, financial services, retail and technology sector. It provides a wide range of analytics tools and modeling solutions. Fractal provides services that are categorized into data preparation, the creation of machine learning models, and the final deployment of analytics. Fractal analytics follow an result oriented approach that is aimed at providing key insights for proper business planning processes. Clients trust on Fractal analytics when they require some help with the performance evaluation systems, trend analysis and scalable reporting solutions that are reliable and in accordance with the enterprise requirements.

3.6 Mu Sigma

Mu Sigma is another well known decision sciences and analytics consulting firm in Noida  that offers formal analytics and performance measurement consulting to multinational corporations located in India and other Indian cities. Mu Sigma has a substantial presence in India and helps the companies in addressing complicated business challenges by using statistical methods, organized assessment, and replicable analytics models. Mu Sigma popular services include risk analysis, forecasting systems and operations measurement. Mu Sigma is well trusted by large scale business organizations who follow a proper disciplined analytics execution and regular performance tracking among business units. They have offices located in all the major IT hubs of India.

3.7 Capgemini Analytics & BI

Capgemini helps the organisations in improving their analytics step by step, in a structured as well as manageable way. They  focus on creating a solid data foundation first and then expanding BI usage across the teams. Capgemini helps the companies in preparing and cleaning data through strong data engineering, in defining clear and shared business metrics, in connecting cloud data warehouses to BI tools, in setting up alerts when the pipelines slow down or break etc. Organisations usually use capgemini when they want BI to scale across the business that too in planned phases.

3.8 WNS Analytics

WNS Analytics is a Noida based data analytics company that gives proper performance measurement solutions. This includes- predictive modelling, reporting structures, and trend analysis. The firm serves clients in the retailing, insurance and healthcare industries. WNS Analytics' experienced team utilizes proper domain experience and organized analytics operations that helps the organizations in improving the visibility of performance and proper accuracy of reporting at functional levels. WNS analytics integrates data, analytics, AI, and human expertise to help businesses extract key important insights, modernize data infrastructure, and enable smarter decision making. WNS utilizes AI and analytics with domain expertise to deliver business outcomes rather than just dashboards.

3.9 Tredence Analytics

Tredence is a popular data science and analytics advisory firm with its branch in Noida. They provide data engineering, analytics model implementation, and forecasting systems offered by the company to help their clients in improving the accuracy of the planning and performance evaluation. Tredence collaborates with every type of business to implement analytics processes & solutions that can be used to achieve quantifiable results and performance data. Tredence provides its services to consumer goods, health and technology industries. Its strength lies in the well structured delivery and clarity of insights that are often praised by organizations.

3.10 HCL Analytics Consulting

HCL’s analytics consulting in Noida helps large organisations in running their analytics and BI systems in a stable, predictable as well as accountable way. They don’t just build the systems and move on, instead their focus is on keeping the data systems working reliably everyday. HCL helps the companies in making data pipelines run without any failure, in detecting the issues  early through alerts, and assigning clear ownership for data models.

4. How to Select the Right Analytics Partner in Noida?

The company should understand your needs as well as business objectives. The company must deliver the analytics that help the clients to improve the revenue, efficiency and decision making, and not only provide reports. The company should have deep expertise in data analytics, machine learning and artificial intelligence. 

These skills allow a company to go beyond basic reporting and deliver high-value business intelligence. These skills help to deliver accurate insights that means using tools to analyze large volumes of data to uncover hidden patterns and trends, automation and efficiency, competitive advantage etc.

Strong data protection is important to ensure business continuity, customer trust and legal compliance. Data analytics partners help in the protection of sensitive business data, they have a large amount of confidential information such as customer records, financial data etc. A good data analytics company does not just give simple reports. They help to explain the data in simpler language so that business leaders can easily understand and take legal actions.

These companies help to give simple explanations of insights that means instead  of technical terms, they clearly state if the data is appropriate for sales, costs, customers and growth. They also provide you regular and clear communication to help avoid confusions and delays along with action-oriented guidance.

Related Post:- Top data analytics companies across India

5. Conclusion

The data analytics ecosystem in Noida is still growing at a rapid pace with more enterprises needing to find the structured analytics support, prediction systems, and performance measurement tools. In the above article, I have listed some of the reliable companies in Noida that provide data analytics services like data engineering, predictive modeling, reporting structures, consulting etc. They have prior experience in working with small & medium size (SMB) and large scale enterprises in solving their business problems using the help of advanced technology stack. You can check these analytics companies capabilities, pricing structure, data engineer experience & expertise and take a final call while selecting the best data analytics partner in India for your business.

Top 15 Real-World Business Problems Solved by Data Analytics

1) Introduction

In today's fast growing digital world, data analytics has become one of the most crucial components for faster business growth, decision making and to further optimize the end business operations. Data analytics assist in quantifying the improvements and help the leaders to get a proper perspective on the revenue, costs, KPIs and to get better business insights. In the blog below, we will discuss some of the most common challenges that are being solved by data analytics.

We will also explore how the analytics utilize the customer intelligence to properly handle the customer churn prediction, segmentations, pricing optimization and for proper discovery of certain hidden patterns in the business. This includes how the supply chain, manufacturing and operational reliability are being taken care of along with certain strategic and governance related benefits. Furthermore, the role of Data Theta in facilitating companies in achieving their business goals is explored.

2) The 15 Core Business Problems Solved by Data Analytics

2.1) Commercial and Customer Intelligence

2.1.1) Customer churn prediction

This type of intelligence assists businesses in understanding customer behaviors regarding why they stay or leave and what actions help in preventing churn. Customer churn models work on analyzing past customers who churned and those who stayed. They help in recognizing patterns and issuing early warning signals. 

Meticulously trigger actions for sales, service, or retention teams and continuously keep updating the predictions. Their business impacts include reduced revenue loss from churn and better renewal and retention rates. Higher predictability of commercial performance is achieved.

2.1.2) Customer segmentation for targeting

Data analytics helps in the classification of customers on the basis of shared characteristics and behaviors. This enables companies to target the appropriate consumers with the right offer, product, and message at the best possible moment.

This technique of segregation of consumers into meaningful groups based on factors like behavior, value, needs, and engagement patterns is known as customer segmentation. A wide variety of segmentation dimensions is utilized in data analytics, such as demographic, behavioral, value-based, need-based, and lifecycle segmentation.

This helps in solving a number of business problems, such as low campaign response rates, high marketing and sales costs, and poor customer experience. Resources are concentrated on clients who are most likely to grow or convert, targeted segments increase relevance and engagement, and offers and messages are designed to cater to client demands.

2.1.3) Pricing optimisation

Pricing is one of the easiest methods to increase revenue and margins. It is mostly handled by manual reductions, fixed price lists, or run on intuition based emotions. Analytics help in reducing guesswork and employing better rationale. Analytics helps in understanding how demand changes when prices of things fluctuate. This helps in finding out which products and services are trending among the consumers and are price-sensitive, to derive meaningful deductions regarding consumer groups so that we can improve sales and revenues. Price-conscious and high or low sensitivity segments may be priced differently. This helps to reach better decision- making outcomes.

2.1.4) Claims anomaly detection

Analytics provide businesses with the ability to precisely identify false or high risk claims before they result in losses or regulatory issues. It carefully studies potential loss and abuse by recognising repeated patterns of provider behavior, customer history and claim timing. In spite of checking every claim, analytics pays attention to meticulously detecting anomalies and inconsistencies. It focuses the attention where it is most needed. It tracks patterns that lead to overpayments and removes inappropriate billing behaviors in the system.

2.1.5) Campaign performance validation

Campaign performance validation is simply finding out whether marketing teams are achieving the required goals. Analytics helps to analyze whether real outcomes are achieved or not. Desirable outcomes include leads, conversions, revenue, expenditure and retention of customers.

Analytics carefully researches the campaign activity regarding leads, sales, and renewals. Further, it finds out what actually contributed to growing the business. By tracking the full funnel system, analytics identifies the probable disengagement. Drop-offs and inefficiencies are also tracked. The problem of clicking but not converting, or converting but not closing is also addressed efficiently.

2.2) Supply Chain, Manufacturing, and Operational Reliability

2.2.1) Demand forecasting

Data analytics helps us to understand how demand changes over a period of time, say monthly, quarterly, half-yearly, or yearly. Businesses deal with a wide variety of issues such as stockouts, insufficient/surplus inventory, manufacturing delays, expenses problems, service breakdowns and other problems. This happens when demand projections are not met. 

Analytics facilitates data forecasting accuracy, accountability for demand patterns and seasonality, reduced stockouts and excess inventory issues, and better alignment of production planning with demand.

2.2.2) Inventory optimisation

Inventory resides at the core of supply chain and manufacturing . Some of the repercussions of improper inventory optimization include missed client obligations, delayed production, and stockout. Improper optimization leads to greater risk of obsolescence, which occurs when assets lose value over time. Operational capital is held unnecessarily and storage expenses also escalate. 

2.2.3) Logistics reliability scoring

Analytics facilitate enhancement of logistics reliability by measuring on-time performance of transportation facilities.Longer routes and lanes are critically analyzed and replaced with shorter ones. This process results in smarter carrier allocation and enables fact-based logistics negotiations. High-risk shipments are rigorously monitored, eliminating the occurrence of  last-minute transportation issues.

2.2.4) Manufacturing failure prediction

One of the major causes for manufacturing downtime is unplanned equipment breakdowns. Data analytics helps in predicting the unplanned failures and provides us timely guidance to rectify the issues. Increased maintenance costs can now be easily managed by detecting early warning signs and abnormal patterns. Failures in sensor readings, cycle times, temperature, and output quality can now be easily tracked.

Optimization of maintenance schedules, reducing unnecessary servicing plans, improving asset utilization, proactively identifying the root causes of recurring failures in machinery and tools, and enhancing safety compliance.

2.2.5) Cloud infrastructure waste reduction

Idle servers, unused storage, and improperly sized systems are instances of cloud infrastructure wastes. Futile cloud resources cost businesses unnecessary burdens. To get rid of them, data analytics play a crucial role. It monitors the CPU, monitor, storage and network to identify resources that are turning into waste and helps to minimize them.

2.2.6) Pipeline reconciliation for KPI accuracy

Key Performance Indicators are computed using reliable data from multiple sources so that missing data in pipelines is recognised. Transformations are verified and source-to-target records are analyzed judiciously. This prevents KPI drift brought on by timing problems, logic shifts, or partial loads. 

Technically, analytics compares source system codes with downstream tables and finds out which pipeline will work the best. Transformation logic is validated and duplicated data is found. Loading failures are detected. Thus , it is ensured that no mismatching of KPIs occurs.

2.3) Medical, Regulatory, and Governance Boundaries

2.3.1) GxP, HIPAA, and GDPR audit clarity

Analytics ensures a clear record of how data is handled and the audits are well-performed. All data systems are made traceable, authorized, and consistent with regulatory standards. Data protection is given utmost importance as data privacy is very significant for industries. Clearer audits and lower compliance risk, and reliable data utilization by teams are the outcomes. Data utilization by teams is made more reliable. GxP, HIPAA, and GDPR standards are followed religiously.

2.3.2) Lab result traceability

Lab results are very crucial data and need to be handled with care. Explicitly tracing the origin of data and processing methods is a good practice to follow. Analytics ensures that  nothing is lost or altered. Documentation is made handy and results of source data, test procedures and approvals are made clearly visible to team leaders. This results in faster result verification, problem-solving, and regulatory compliance for labs and medical teams.

To put it simply, traceability guarantees that laboratory results are precise, comprehensible, and completely auditable.

2.3.3) Data residency discipline across regions

Data residency discipline guarantees that the data under consideration is properly processed and accessible as per the laws of the desired geographical location. Internet Protocol restrictions can sometimes prevent cross region data flow which hampers the workflow of the management teams. 

Data localization policies and region-specific cloud deployments need to be well studied. Data Analysts help in removing the hurdles by authorizing role-based access, overcoming IP limitations and data tagging. region -tied encryption keys are also set-up.

2.3.4) Access governance accountability

Accountability in access governance refers to who has control on what kind of data. It also explains what data can be accessed and why it should be accessed. This helps in data control management. The user, time, and purpose of each access request is recorded. This helps in making the systems transparent and clear.

2.4) Strategic, Board-Level Decision Intelligence

2.4.1) Scenario planning under constraints

Since data is immobile, businesses don't hesitate. The lack of restrictions or uncertainty in the modeling of situations causes them to pause. They hesitate because situations are not modeled to handle uncertainty or limits. Important factors like production restrictions, inventory constraints, labor availability, and cost ceilings are used in analytics models to mimic a multitude of conditions. 

2.4.2) Decision scoring instead of intuition

Many enterprises make decisions based on intuition rather than carefully designed models. Decisions should be based on clear rationale. Analytics builds deterministic scoring layers. Factors such as  risk posture, margin sensitivity, demand reliability, anomaly probability, logistics, compliance regulations, cloud spend behavior, and forecasting maturity. 

Decisions are based on business goals rather than biased opinions because each element is fully analysed. Leaders are able to compare options consistently using the same standards. 

2.4.3) Board-level KPI unification

Team leaders are generally uninterested in numerous dashboards. They want one genuine source of information to base their decisions upon. In order to guarantee that leadership sees a single, consistent set of KPIs rather than several variations of the same number, KPI unification is vital. Conflicting reports are eliminated, and reviews and judgments gain credibility. To put it simply, the board now has a single, reliable perspective on how the company is performing.

3) How DataTheta Helps Enterprises Adopt Analytics with Confidence

At DataTheta, analytics is delivered under Lance LABS INC., Texas, USA, operating under US laws with offices in Noida and Chennai, India.  We deal mainly with pharmaceuticals, healthcare, retail/CPG, energy, BFSI, manufacturing, logistics, and SaaS platforms. Supporting mid-market to enterprise-level companies to prosper is our area of expertise.

Our service stack includes:

  • Hybrid source integration
  • Batch and streaming pipeline design
  • KPI unification
  • Reconciliation dashboards
  • Observability-first architecture
  • Lineage dashboards audit natively
  • Cloud cost governance implemented before scale
  • Security alignment (RBAC/IAM)
  • Data quality discipline
  • DataOps + CI/CD sprint integration
  • ML-ready feature exposure for AI teams

4) Conclusion

Data analytics drives businesses to greater profits by replacing uncertainty with clarity. It resolves business issues by transforming dispersed data into consistent and trustworthy data. Leaders are now able to clearly identify opportunities, risks, and performances across very components of the business.

Decisions become easier to defend, quicker and precisely calculated. The operational waste, income leakage and compliance deficiencies. When there is a priority task, teams work together.  In the end, analytics facilitates more intelligent choices and helps in solving business paradoxes.

At DataTheta, analytics is delivered as an owned system of measurable confidence, not task completion, ensuring BI dashboards and AI workloads scale without conflicting KPIs, silent pipeline failures, or ungoverned cloud spend.

What Is Data Engineering? A Beginner-to-Expert Guide for Data Teams

1) Introduction

Over the past decades, there has been an urgent need to process and manage data in business firms. At the same time, there has been a rising demand for improving connectivity, magnanimous amounts of data, and in some cases ultra-low latency communications.

Accumulation of raw data that is not treated well by cleaning, transformation and storage tends to create ill-decisions in businesses. Data engineering is the branch of science and technology that enables easy and efficient processing of data. It governs the principles handy in cleaning, collection, storage and transformation of data.

Data engineers deploy their expertise to build pipelines that deliver good outcomes for the businesses.

2) Data Engineering in Simple Terms

2.1) The Core Job

The key focus of Data engineering is to build systems that take data from a variety of sources and make it suitable for analytics or applications. Designing architecture that governs how data moves and lands in a pipeline. Integrating sources that maximise the outputs derived out of the pipeline.

It ensures that the transformation, observability and orchestration is in the place. The workflow ensures that the right set of data reaches the right place in the right form at the right time, without duplication or failures. The key processes include:

  • Data collection & ingestion
  • Data transformation & modelling
  • Data storage & organisation
  • Data orchestration & pipeline ownership
  • Data observability & reliability SLAs
  • Data security & access governance
  • Data lineage & reconciliation
  • DataOps & deployment discipline

2.2) Beyond ETL/ELT

Many beginners suppose that data engineering is all about ETL/ELT.  In reality, it’s a far greater discipline. Its diversity spans way beyond mere extraction, transformation and loading. It also focuses on orchestration which is like a multifunctional traffic controller of data systems.

Lineage capture and cost discipline also need to be meticulously handled. The tasks are successful only when pipelines are considered trustworthy by all types of teams running the business, rather than just running a pipeline.

3) Skills Progression: Beginner to Expert

3.1) Beginner Level

Beginners shall start with learning foundational concepts that are totally non-negotiable skills. Start by learning the basics of Sequenced Query Language (SQL) that teaches how to filter, join and group data. Side by side, learn any one computer programming language such as Python.

Python is simple and easy to learn and finds great applications in the data science field. Other skills that beginners can also explore include:

  • SQL proficiency
  • Application Programming Interface
  • Basic pipeline logic (ETL / ELT + pipelines)
  • JSON/CSV/Parquet/Logs
  • Cloud computing platforms basics (eg. AWS or Azure)

3.2) Intermediate Level:

Mid-level data engineers have built strong SQL and solid data modelling skills. They can flawlessly build clean and easy to read tables for analytics. They understand facts vs dimensions. They can efficiently build end to end pipelines that can easily handle incremental loads and manage schema changes.

 Core intermediate skills include:

  • Distributed processing
  • Pipeline orchestration
  • Hybrid data integration
  • Schema standardisation
  • Data quality monitoring
  • Cloudcostawareness

3.3) Expert Level

Experts build whole data ecosystems from end to end. They design data systems that scale efficiently, predict failures, improve cost efficiency, and build strong security. This is what differentiates an expert from an intermediate. They employ expertise in deep data modelling and SQL mastery. Their skills include but are not limited to:

  • Lakehouse or warehouse topology
  • Real-time streaming with batch unification
  • Deterministic transformations
  • Data contracts & reconciliation dashboards
  • End-to-end lineage ownership
  • SLA measurement cadence
  • Multi-region & residency alignment
  • DataOps integrated into CI/CD

The following process parameters indicate that you are working with an expert data engineering consultant, and not just someone who knows the tools:

  • Before starting with tools, they ask essential business questions such as: who uses the data, what decisions depend on it, what breaks if it fails, what are the KPIs?
  • They proactively talk about retries, backfills, data quality checking.
  • They simplify the stack by removing unnecessary tools and making sure duplication of pipelines doesn’t occur.
  •  They perform standardization of models and definitions.
  •  They take utmost care of cost effectiveness by query optimization, compute sizing, and avoiding over-processing.
  • They build leadership trust reports, and help mitigate conflicts among teams over metrics.
  • They standardize and document the processes very well.

4) The Business Impact of Data Engineering 

4.1) For Leadership Teams

Leadership teams including CXOs, VPs, and Heads can make better business decisions in less time. They get trustworthy reports that help them to scale. Faster decision cycles lead to better capital allocation and fewer allocation to data.

Data disputes are mitigated easily and teams as well the leaders can properly focus on execution. Valuable time is not wasted in questioning data, rather put into use in building ground-breaking strategies.

4.2) For AI/ML Teams

These teams get significant benefits from well-structured and versioned data. They get access to stable historical data sets and do not need to start afresh every time they sit to build a pipeline.

Reproducible pipelines also help to mitigate workload and make the processes faster. Furthermore, models train faster and re-experimentation costs are reduced. Teams get higher accuracy and the rate of adoption also enhances.

4.3) For Cloud Spend Owners

Data engineering helps to convert uncontrolled spending into meticulously planned investments. It also focuses on delivering lesser cloud wastage, building controlled storage facilities and reducing the arrival of invoices that are uninvited. Predictable compute storage facilities play a significant role in making the processes smoother.

4.4) Compliance and audit teams

Data lineage and historical data sets are now easily accessible, and their access is controlled and monitored as well. It is also taken care that sensitive data is properly protected and only the allowed authorities access them judiciously. The business implications are such that faster audits are performed and lower regulatory risk happens.

This ensures better business outputs and fewer compliance related fallacies. Rather than manual firefighting systems, now businesses rely on efficient system-driven processing.

5) Data Engineering Delivery Models Enterprises Must Understand

5.1) Pipeline Build vs Pipeline Ownership

In this pipeline build, engineers are responsible for making efficient data pipelines. They understand various data sources, then report the needs associated with the data analytics. Once they are implemented, ingested, transformed and validated, final documentation is carried out effectively.

On the other hand, the pipeline ownership model is based on taking accountability for reliability, proper timelines and monitoring of failures and data quality.

5.2) Manpower vs Engineering Outcomes

This model of manpower relies on engaging a greater number of people to derive results. Constant monitoring by human resources is done and repeated handoffs are performed.

On the contrary, engineering models rely on building data systems that run efficiently on their own rather than requiring constant monitoring. The goal here is to build self-reliant systems.

5.3) Batch vs Streaming Unification

Modern enterprises require both. Batch processing takes place at regular intervals, say hourly or daily. Usually used for sales reports. Streaming processing takes place almost real time. It is used for fraud alerts and live tracking.

5.4) Cloud Cost Discipline vs Cloud Cost Promises

Costs are usually assumed and not designed. The majority of the organizations deploy pipelines that are designed to minimize computation and data is reused. This is called cost discipline. Cloud cost promise runs on tools and relies on alerts after money is being spent.

5.5) Self-Serve Data Products vs Bespoke Pipeline Delivery

Self-data products use data as a reusable product. Business, AI and BI teams use data directly and the processing is well documented. They scale with reuse. While the bespoke pipeline scales with people and increases complexity over time. They lay heavy emphasis on coding operations.

6) What Enterprises Should Expect When Working with a Data Engineering Partner

Enterprises must demand:

  • Early architecture documentation
  • Hybrid source connectors
  • Deterministic KPI alignment
  • Pipeline observability before scale
  • Reconciliation dashboards
  • Measurable reliability SLAs in cadence
  • Audit-native lineage graphs
  • Cloud compute sized intentionally

7) Conclusion

Data engineering relies on building great systems that are super reliable and scale under different kinds of circumstances. Raw data is turned into informed and wise decisions. Instead of solely relying on human resources, data system pipelines are built that help to navigate various business operations successfully.

Strong data engineering assets enable businesses to make prompt and wise decisions that are beneficial for business profits.

8) FAQs

8.1) What does a data engineering consultant deliver to the clients?

A data engineering consultant delivers end to end data architecture, pipelines and their respective ownership, clean data models, and orchestration and monitoring with alerts.

They also give security models with access controls and data quality checks with lineage. Other deliverables depend on the plans offered by the consultants.

8.2) How is data engineering different from data analytics?

These are different yet closely related domains. The former works on building the foundation of data while the other focuses on generating insights and supporting decision making.

8.3) Is data engineering only about tools and software?

No, it is way more than tools and software. It is about systems design, reliability, ownership and delivering great outputs for businesses.

8.4)  How does data engineering support AI and machine learning?

It provides clean and organized, structured, and reproducible datasets so that models can be easily trained and faster results are produced.

8.5) When should a company invest in data engineering?

When critical business decisions need to be made, a wise firm should invest. They are used for better decisions, forecasting, and improving compliance and regulatory standardizations. Data engineering is no longer an optional asset.

DataTheta Partner
Top 10 Data Analytics Companies in Gurgaon / Gurugram for Enterprise Digital Transformation

1. Introduction

Gurgaon has become an important hub for data analytics, AI and digital transformation in North India. Companies in finance, retail, logistics etc. use data and AI to improve their efficiency, growth and decision making, and to run operations. Nowadays businesses are handling a large amount of data in order to meet their needs.

The companies are looking for consulting partners who can help them organize this data and apply advanced analytics. These services support better planning as well as decision making and also improve customer understanding. Consulting firms in Gurgaon play a key role by helping these enterprises in building a strong foundation and also in connecting analytics with real business flows. 

This article lists the top 10 data analytics and AI consulting companies in Gurgaon that support enterprises in creating smarter data systems that can be trustworthy.

2. What Is Data Analytics & AI Consulting?

Data analytics and AI consulting helps the teams or the companies to use their data in order to make better decisions. Consultants help to find useful patterns and insights by collecting, cleaning, organising and analysing the data.

Related Post:- India’s most trusted data analytics firms

The main focus is to turn the raw data into clear insights that help in supporting planning, forecasting and automation. All this work includes building data pipelines, creating dashboards, and developing machine learning models.

3. Top 10 Data Analytics Companies in Gurgaon / Gurugram

3.1 DataTheta

DataTheta is a Gurgaon-based analytics and AI consulting firm that helps the enterprises in using data in a practical as well as in a meaningful way. DataTheta works on building strong foundations that support real businesses instead of just focusing only on tools and dashboards.

The company helps the organisations in designing scalable data platforms and also by creating dashboards that could be easily trusted by the teams. DataTheta is differentiated by all others because of its business outcomes.

3.2 Fractal Analytics

Fractal Analytics is a strong analytics and AI consulting company in Gurgaon. It helps the enterprises in using advanced analytics and machine learning for improved business performance and to understand the customers.

Fractal analysis works with companies to build as well as deploy learning pipelines, and to create scalable analytics systems that can be used by the teams on a daily basis. 

Companies in finance, retail, technology etc. use fractal analysis to improve planning cycles and also to increase forecast accuracy. 

3.3 LatentView Analytics

LatentView Analytics is an analytics company in Gurgaon that helps the businesses to make better use of their data. Their team works with organisations to bring data from different systems into one place that makes it easier to understand.

LatentView helps companies by grouping their customers together and also by predicting future trends. They also create dashboards that help the team in tracking important KPI’s,  and also help in making better decisions.

3.4 Mu Sigma

Mu Sigma is an analytics consulting company that helps enterprises use data in supporting better planning and also in tracking the performance. Mu Sigma is also a Gurugram based company that focuses on building structured as well as repeatable analytics solutions.

They help the organisations by analysing risks and model operations. Their main approach is to emphasize consistency and scale, so analytics can be reused across the teams.

3.5 Tiger Analytics

Tiger Analytics helps the companies use data and AI to improve their planning strategy and to run their businesses. This firm supports enterprises in building modern data platforms and also by building or developing BI dashboards upon which the teams can rely easily.

They also make sure that analytics do not get separated by business operations so they work closely with the business as well as the technical teams. They work with the clients across sectors such as retail, healthcare, manufacturing etc.

3.6 Tredence

Tredence helps the organisations in using AI as well as data in improving everyday decision making. The team of Tredence work on building data pipelines and in creating predictive models. Tredence makes sure that analytics is easy to use and understood by the teams and it also supports use cases like performance dashboards, demand forecasting etc.

3.7 Absolutdata

Absolutdata helps the companies in better understanding of their data and use it to improve their business performances. This firm mainly works with clients  in retail, finance and technology.

AbsolutData also supports customer analysis, and tracks the performance by building clean data models and easy to use KPI dashboard.

3.8 Genpact

Genpact supports large enterprises across industries by providing them analytics and AI consulting services from the Gurgaon delivery center. Genpact helps the industries by bringing data together and by building forecasting models.

It also helps in tracking the KPIs and also implements BI systems that helps in improving visibility into business performance. They work closely with finance, supply chains to make reporting more accurate and more reliable decisions.

They also ensure that the data is consistent and also trusted across the organisation.

3.9 Cognizant Analytics

Cognizant Analytics in Gurugram helps the enterprises to use data in order to improve planning as well as business performance. Its team basically works on building pipelines and creating predictive dashboards.

They also help in building reliable dashboards so that they can be easily trusted by the teams and can be used by the teams on a regular basis. Cognizant helps the organisations by connecting cloud data platforms and also by deploying machine learning solutions. 

3.10 Deloitte Analytics

Deloitte Analytics helps the enterprises use data in a much structured as well as in a well-governed way. They support the organizations with defining the KPIs clearly, managing data governance and also by validating AI models.

They support risk planning, performance tracking etc. by bringing the data from different systems and turning the data into the dashboards. They not only focus on being technically correct but also focus on making analytics reliable and easy to explain.

4. How to select the best Data Analytics Company in Gurgaon/Gurugram?

4.1 Pricing

Pricing for data analytics mainly depends on what you need and how you engage. The costs can depend on the project size, data complexity and also on the level of ongoing support required.

In order to keep the budgets under control, one should choose a firm that clearly defines deliverables, timelines and also scope. The company should state clear expectations and transparent pricing.

4.2 Client Reviews & Case Experience

The client reviews and case experience plays a very important role in understanding how well a company actually delivers.They also show that whether a firm communicates clearly, meets the timelines and also provides reliable results.

One should look for experience in your industry while choosing a data analytics company.

4.3 Industry Expertise

Whether the industry is finance, retail, healthcare or technology, one should always choose a data analytics partner that clearly understands your industry.

When you choose a firm who knows your domain, they can ask the right questions early by avoiding common mistakes and also understand your data, KPIs in a better way and challenges better. This leads to more practical insights and faster execution.

4.4 Data Security & Governance

A good analytics partner should be clear about how your data is being protected at every stage. This includes secure methods to store and process data. This also helps in encrypting sensitive information and following well defined governance rules.

4.5 Technical Capability

A strong data analytics partner should be able to handle a full analytics stack. This also includes building data pipelines, deploying AI and machine learning models and also creating reliable dashboards. To ensure that the insights are being  delivered consistently and accurately, they support automated reporting.

4.6 Long-Term Support

Data analytics is not a one time project. Systems need ongoing monitoring, regular updates and also continuous improvements as the business and the data changes.

One should not choose a partner who does not offer long term support after the initial delivery. As the business grows, the dashboards should be maintained and the fixes should be fixed as soon as possible.

5. Conclusion

The analytics ecosystem in Gurgaon is growing rapidly. The firms are offering a strong mix of AI, BI and data engineering services. These companies help enterprises in planning, performance tracking and in regular decision making.

Each of the firms that is listed above brings a combination of technical skills, industry understanding  etc. This helps the organisation in building an analytics system that actually helps in supporting the business growth.

When a business is choosing an analytics partner, they should not only focus on the brand names instead they should focus on clear pricing, strong data security and governance along with an industry expertise.

When the partner is right, the enterprises can turn data into reliable data.

Finance Analytics: How BI Helps in Budgeting, Forecasting & Risk Control

Introduction: Finance Teams Drowning in Data, Starved of Clarity

Nowadays finance teams have access to more data than ever, through billing systems, supply chains, banking feeds etc. Despite having a large volume of data, there is low confidence in the numbers that are reaching leadership. The data arrives late and also comes from various systems so it rarely matches across the departments.

Due to this, the finance analysts are not able to focus on budgeting, risk planning etc., instead they spend a lot of time checking the numbers in the spreadsheet. Even when the reports reach for leadership, the teams still waste their time debating about the numbers,  whether they are correct or not. These slow decisions increase uncertainty.

Business intelligence has become a critical part because they need reliable systems that deliver consistent as well as traceable data. Strong BI contributes in reducing the dashboard conflicts and helps in ensuring that the KPIs are defined clearly or not.

1. Business Intelligence as the Backbone of Finance Decisions

1.1) BI for Finance Is Not Reporting. It Is Behaviour Control of the Reporting System.

Finance BI is not only about creating reports and dashboards. It is about controlling how the financial data is produced, validated and used across the organisation. A strong finance BI system makes sure that the financial KPIs are consistent, traceable and easy to trust. Numbers should be updated in a predictable way and should be auditable also.

This allows budgeting and forecasting and also allows the commercial teams to work from the same number of facts. Enterprises expect enforcement of clear KPI definitions, automated reconciliation etc. from BI.

1.2) Finance BI Answers Questions That Dashboards Alone Cannot Guarantee

Finance BI does much more than only showing charts. It helps the finance teams understand and also trust the numbers behind the charts. When there is a right BI setup, the teams can easily see what was spent, how much difference is there between the actuals and the budget and where the numbers do not match across the systems.

BI also keeps a track of financial reports that determine whether they are delivered on time or not. Finance teams must rely on the BI systems that help in ensuring that whether the data is reconciled, consistent and traceable or not.

2. Budgeting with BI: Moving from Manual Assembly to System-Owned Budgets

2.1) Why Most Finance Budgets Break Down

Finance budgets do not fail because of weak planning , it fails because the data behind the budget is not stable. When the data pipelines fail without any notice or when KPIs differ across teams, then it results in breaking down of the budgets.

When the data lineage is not clear, finance fails at explaining where the numbers came from. Without reliable data foundations, budgeting becomes fragile instead of predictable.

2.2) What BI Changes in Budgeting

Business intelligence changes the budget by making the system responsible for accuracy and not manual checks. Before finalizing the budgets the financial KPIs are built with clear and fixed definitions with the help of BI. Budgets are validated against these deterministic KPI rules so numbers stay consistent as usage grows.

BI also implements schema checks before scaling and helps to ensure whether the financial reports are being delivered on time or not.

Finance BI budgeting enables:

  • Single budget baseline, not ten versions of doubt
  • Real-time budget vs actuals tracking
  • Early anomaly classification for spend mismatches
  • Intentional cluster sizing for budget workloads
  • Audit-native lineage dashboards for budget justification
  • Security alignment for finance publication access
  • Reconciliation dashboards before leadership budget reviews scale

3. Forecasting with BI: From Financial Rear-View to Forward-View Discipline

3.1) Predictive and Financial Forecasts Depend on BI-Ready Foundations

Forecasting only works when it is built on a stable and trusted data foundation. BI keeps ensuring that the financial features are structured properly or not, and KPI definitions do not change over time. When BI is in place the data lineage is clear for audits and the system health is monitored before scale. Because of these foundations, the forecasts are reliable as well as explainable.

3.2) How BI Helps Forecasting

Business Intelligence helps forecasting by making future projections consistent, explainable, reliable as well as controlled.  It also helps forecasting by locking in clear KPI definitions, in order to keep the numbers consistent across the teams. It provides clear lineage and also tracks the reports on time so that  the forecasts can be explained during the reviews. It also reduces the cost of waste by controlling infrastructure usage as well as by shutting down the idle resources.

4. Risk Control with BI: From Reactive Reviews to Proactive Risk Visibility

4.1) Risk Fails When Lineage and Observability Are Missing

Financial risk becomes even harder to manage when the teams cannot clearly see the actual source of data and the behaviour of the systems. The KPIs differ across teams and system failure goes unnoticed when the audits start relying on the people’s memory. When there is no observability, the issues stay hidden until the budgets suddenly spike. During audits it's hard to explain the numbers due to missing lineage and results in increasing stress.

4.2) How BI Helps in Risk Control

Financial risk becomes even harder to manage when the teams cannot clearly see the actual source of data and the behaviour of the systems. The KPIs differ across teams and system failure goes unnoticed when the audits start relying on the people’s memory. When there is no observability, the issues stay hidden until the budgets suddenly spike. During audits it's hard to explain the numbers due to missing lineage and results in increasing stress.

5. The DataTheta Finance BI Lifecycle: What We Actually Deliver

5.1) Architecture Ownership Before Finance Scale Begins

At DataTheta, the ownership of the finance data and the architecture is taken way before the budget and the forecasts start scaling. This helps in preventing frequent resets and broken reports. 

5.2) Deterministic KPI Contracts for Finance

DataTheta defines the KPIs only once with a clear view, so the same numbers mean the same thing everywhere. Each KPI has a fixed definition and a fixed owner. This helps in removing confusion across dashboards and also reduces the forecast drift.

5.3) Observability First, Not Last

DataTheta puts visibility in place, before scale, not after the appearance of problems. It tracks how fast the data arrives, detects unusual behaviour and also alerts the teams when pipelines show the early signs of failure.

5.4) Reconciliation Before Leadership Scale

DataTheta ensures that the numbers match across the systems before the leaders see them. They help in creating the reconciliation dashboards that compare the source data with the final financial reports. This prevents conflicting numbers from reaching the boardroom by catching the mismatches as early as possible. As a conclusion, the leaders can act on the figures with confidence.

5.5) Audit-Native Lineage Before Model Scale

DataTheta helps make data lineage visible and built in from the beginning itself. They show clear lineage dashboards that show the actual source of data and the flow of data through the system. This means that audits do not rely on manual explanations but it relies on simple visual evidence.

5.6) Cloud Cost Governance Before Workloads Scale

DataTheta helps make data lineage visible and built in from the beginning itself. They show clear lineage dashboards that show the actual source of data and the flow of data through the system. This means that audits do not rely on manual explanations but it relies on simple visual evidence.

Some of the key points that define our delivery posture include:-

  • Full system ownership, not task delivery
  • KPI determinism before BI or forecast scale
  • Observability before finance workloads scale into leadership
  • Reconciliation before dashboards or models scale into boardrooms
  • Lineage dashboards that justify chain-of-custody audit-natively
  • Cloud cost governance before scale silently increases infra waste later
  • Full-time senior Indian data engineering consultants embedded into internal tools and sprint cadence under US-law governed contracts via Lance LABS INC.

6. Business Outcomes That Mature Finance BI Delivers

The teams see clear and practical improvements when the finance BI is built in the right way. Dashboards reflect the numbers that are consistent, instead of conflicting metrics. Forecasts stay stable and change only for clear reasons.

When auditors use lineage dashboards instead of relying on people’s explanations, then the compliance reviews become easier. The AI and analytics teams spend most of their time in building data models and very less time in cleaning data. Overall, finance leaders get faster, more confident decisions from data they can trust.

7. Conclusion

Enterprises need to look at finance BI not just for building dashboards, but as a core decision infrastructure. All the factors such as budgeting, forecasting, audits, risk management works well only when the foundation is strong as well as consistent. This foundation includes clear data architecture, clear data lineage, data residency control and cloud cost management etc. 

FAQs 

1) What is Finance Analytics in enterprise terms?
Finance analytics interprets budgeting, forecasting, audit, and risk posture using BI-ready foundations that enforce deterministic KPI contracts, hybrid source reconciliation, audit-native lineage dashboards, observability before scale, security alignment (RBAC/IAM), residency discipline, intentional cluster sizing for transformation workloads, cloud cost governance before idle clusters silently increase infra waste, and DataOps CI/CD sprint alignment.

2) Can BI improve budgeting accuracy?
Yes, when KPI logic is deterministic and reconciliation exists before leadership workloads scale. BI provides a single budget baseline, tracks budget vs actuals in real time, detects spend mismatches early, validates schemas before scale, isolates BI concurrency workloads, auto-terminates idle clusters, captures lineage natively, and governs cloud utilisation before infra waste silently increases avoidable cost anomalies later.

3) How does BI help forecasting?
BI publishes deterministic forecast baselines, monitors latency SLAs, reconciles hybrid sources before scale, captures lineage for forecast justification, isolates forecasting workloads from ad-hoc infra, sizes clusters intentionally for transformation workloads, aligns DataOps CI/CD to sprint cadence, and identifies idle clusters or duplicated storage before infra waste silently increases avoidable cloud cost anomalies later.

4) Why do finance dashboards conflict across teams?
Dashboards conflict because KPI definitions differ, sources aren’t reconciled before scale, pipelines fail silently without alerts, schema drifts disrupt workloads, lineage isn’t audit-native, DataOps doesn’t align to sprint cadence, and cloud utilisation governance is delayed. 

5) Does analytics increase finance cloud costs?
Only when delivered without discipline. Good analytics reduces cost anomalies by auto-terminating idle clusters, eliminating duplicated pipelines, enforcing partitioning or clustering discipline for serverless SQL workloads, sizing clusters intentionally for transformation workloads, isolating BI concurrency workloads, governing infra reuse, and tracking cloud utilisation before analytic workloads silently scale into avoidable infra waste later.

6) Is offshore finance analytics consulting risky?
Not when structured correctly. Offshore consulting works when engineers are full-time, embedded into internal enterprise tools, sprint-aligned, and security-compliant while a partner owns architecture early, publishes deterministic KPI contracts, reconciles hybrid sources before BI or forecast workloads scale, captures lineage audit-natively, and governs cloud utilisation before infra waste silently increases avoidable cost anomalies later.

7) How does DataTheta help finance teams adopt analytics?
DataTheta delivers finance analytics under Lance LABS INC., Texas, USA governed by US law with delivery hubs in Noida and Chennai, India owning architecture early, enforcing deterministic KPI contracts, publishing reconciliation dashboards before leadership workloads scale, enabling observability before BI or model training scale begins, publishing audit-native lineage dashboards, aligning DataOps CI/CD to sprint cadence.

8) How should finance teams evaluate BI maturity?
Finance teams should evaluate BI maturity by KPI determinism before scale, reconciliation dashboards before leadership workloads scale, observability before BI or training workloads scale into production, audit-native lineage dashboards for chain-of-custody justification, security alignment to internal tools, cluster sizing discipline for transformation workloads, DataOps CI/CD sprint cadence fit, and cloud utilisation governance before infra waste silently increases avoidable cost anomalies later.

Top 10 Data Analytics Consulting Companies in Delhi/NCR (Gurgaon, Noida, Faridabad)

1. Introduction

Over the past few decades, the Delhi NCR region has emerged as the hub of new science and technological developments. It has become the focal point for groundbreaking technologies such as data engineering, data analytics, AI, ML, and data warehousing.

Data consultancy firms facilitate businesses  staying aware of new and trending market patterns and help in quicker decision-making.  The region has large clusters of organizations working with finance, retail, pharmaceuticals, healthcare, AI/ML, and logistics.

Consultation firms provide great guidance to these varied sectors and assist in the entire life cycle of data processing and optimization. Organizations in the NCR region are investing in analytics to consolidate data systems, improve rational decision-making skills, support operational planning, and enhance regulatory compliance, leading to better scaling of business.

This guide highlights the top 10 data analytics consulting companies in Delhi NCR that help businesses build strong data management systems and boost their business performance and build consistent and trackable KPIs.

2. What Is Data Analytics Consulting?

Data analytics consulting refers to professional services that help organizations build, design, and govern efficient data management systems that help in the collection, processing, protection, and documentation of data. It also includes data engineering services, BI dashboarding, and building data pipelines that provide efficient conversion of raw data into actionable tasks.

3. List of Top 10 Best Data Analytics Consulting Companies in Delhi/NCR 

3.1 DataTheta

DataTheta is a top rated data analytics company in Delhi/NCR that is helping the enterprises to turn complex data into measurable business outcomes. With experience across healthcare, retail, manufacturing, and financial services, the company delivers services spanning data strategy, engineering, business intelligence, machine learning, and generative AI applications.

Its experienced teams design scalable platforms, modern data warehouses, and real time analytics that support smarter decisions and operational efficiency. DataTheta also  provides industry focused solutions, flexible engagement models, and expert talent to accelerate digital transformation programs.

After combining domain expertise, strong delivery practices, and modern technology stacks, the company enables organizations to unlock value from data, improve customer experiences, and build sustainable competitive advantage.

3.2 Fractal Analytics

Fractal Analytics provides integrated services in the field of AI, engineering, and designing. They help in automating data processing, provide AI-assisted decision-making, and build scalable platforms. They deploy best engineering practices to perform flawless integration using GenAI engineering frameworks such as AI-driven DevOps and security, composable AI platforms, and cloud-native and hybrid infrastructure.

They also provide rigorous monitoring for proactively fixing security issues. Their services are supported with 25 years of research experience, and they invest 5% of their revenue especially in R&D. They serve a wide variety of industries spanning from retail and CPG to healthcare and life sciences.

3.3 LatentView Analytics

LatentView Analytics provides strategy-aligned analytics, customer segmentation, predictive forecasting, and performance dashboards to enterprises in Gurgaon and Noida. The firm’s analytics consulting includes data preparation, optimization models, customer lifetime value analysis, and BI reporting frameworks that support cross-functional planning. LatentView works with large organizations looking to scale their analytics with reliable pipelines and consistent outcome measurement.

3.4 Mu Sigma

Mu Sigma is a renowned analytics and decision sciences consulting firm that provides structured and organized analytics frameworks to Delhi NCR companies. Their website runs on very elegantly designed UI/UX principles. Its teams are experts in forecasting engines, risk analysis, performance dashboards, and operational modeling. Replicable analytics techniques that enable enterprise planning and robust data programs are emphasized in Mu Sigma's consulting approach.

3.5 Tredence

The USP of Tredence is that they deliver great value through industry and functional expertise, assist in faster decision-making through accelerators, and speed up scaling of businesses through deep data and artificial intelligence technologies. They have won several prestigious awards and partnered with tech giants like Snowflake, Google Cloud, Microsoft Azure, and Databricks, among others.

They provide a wide variety of services, including agentic AI, generative AI, data engineering and modernization, and digital engineering. They also work with supply chain management and ensure easy functioning of ML models through MLOps and LLMOps.

3.6 Absolutdata

Absolutdata is an organization based out of Gurugram that provides customizable data architectures and business intelligence solutions that transform unprocessed data into a decisive edge over business competitors.

They offer specialization in data strategy and architecture, BI, advanced analytics & AI, and data governance. They showcase a phenomenal 100% client retention rate and have successfully completed 40-plus client projects.

3.7 Genpact

Genpact provides great technological services to a wide array of industries, such as banking, capital markets, communication, media & entertainment, consumer goods and retail, high tech, insurance, and private equity. Moreover, healthcare and life science industries aren’t untouched by their impact.

For the seventh time in 2025, Ethisphere awarded Genpact one of the World's Most Ethical Companies, honoring their rigorous efforts in fulfilling sustainability in processing. They provide both advanced technological solutions and business services. Some of their services include digital technology, advisory services, and agentic solutions.

3.8 Cognizant Analytics

Cognizant Analytics provides a multitude of services, including engineering R&D, AI business accelerators, responsible AI, Agent Foundry (AI pilots for production-grade agent networks), Neuro Edge (for accelerating time to value), and multi-agent AI systems.

They also work with data and AI management, data modernization, generative AI, enterprise AI agents, and developing AI solutions. They also focus on cybersecurity services such as data protection and privacy services, threat and vulnerability management, and risk and compliance management.

3.9 WNS Analytics

WNS Analytics offers 50-plus productized offerings and 20-plus years of experience. Decision intelligence, which is based on real-time insights, responsible AI, and in-depth domain knowledge, is the key for building intelligent enterprises.

To help their clients innovate continually, grow with ease, adapt constantly, and lead resiliently, WNS Analytics combines the precision of AI, the creative power of agentic AI, human ingenuity, and ten innovative industry practices. They have received 30-plus international awards in recognition of their successful efforts.

3.10 Deloitte Analytics

Deloitte Technology spearheads change with cutting-edge, patented solutions that tackle today's most pressing issues, from operational excellence and expansion to cybersecurity threats and regulatory problems. Their cutting-edge technologies serve as a catalyst for change rather than utilizing mere tools.

They can assist your company in overcoming obstacles, making decisions more quickly, and generating long-term value by combining analytics, automation, cloud, and artificial intelligence.

Related Post:- Data analytics companies evaluated for Indian enterprises

4. Pricing and Selection Criteria for Data Analytics Consulting Firms

4.1 Pricing

It is very crucial to consider cost factors while selecting a suitable consultancy firm. Pricing helps us to understand what we are paying for, how predictable the cost is, and whether we get a good return on investment. Cost per use case should be taken care of to compare firms based on value rather than just efforts.

4.2 Client Reviews & Case Experience

It is a wise decision to check client reviews before investing in any firm. Reviews showcase how consistently and efficiently a firm delivers results. It helps to build trust and harmony with the organization. Case experience demonstrates whether a consultancy firm has effectively addressed scenarios that are comparable to the one you are currently dealing with.

This showcases how the business challenges are dealt with, what kind of analytics approaches are utilized, and how measurable outcomes are easily studied.

4.3 Industry Expertise

It is sagacious to choose consultants that offer expertise in the areas you are currently working with. Care should be taken that the consultants understand domain data sources, are familiar with industry-specific KPIs and metrics, and are experienced with the regulatory and compliance norms.

They should demonstrate proven capacity to design data models aligned to industry-specific workflows, and not just implementation of generic schemas.

4.4 Data Security & Governance

Ensure that the consultation firm follows good data security practices such as data access control, data privacy and protection (encryption, masking, and anonymization of sensitive fields), data ownership and accountability, data lineage and traceability, performing audit logs, and proper monitoring.

4.5 Technical Capability

Properly access the technical capabilities of the consultants by taking care of some crucial factors such as cloud platform expertise, architecture design ability,  strong SQL and data modelling depth, and ability to handle scale and complexity. Clearly evaluate their coding documentation process and production readiness focus.

In a nutshell, observe their problem-solving approach, keeping in mind they design solutions rather than merely using tools.

4.6 Long-Term Support

Look out for consultants that offer reliable support throughout the consultation process, as it is not a one-time task. Data systems need to be well checked, reliable, secure, and valuable even after the project ends.

Defined SLAs and support coverage, proactive monitoring and incident management, knowledge transfer and documentation, and scalable support models go a long way in building a healthy business relationship with the consultants and firms.

5. Conclusion

The Delhi NCR region has become a strong ecosystem of data analytics consulting firms. It's important to go beyond tools and hourly fees when selecting the best data engineering or analytics professional. Effective consultants showcase refined engineering models that proactively work to deliver timely results.

The companies listed here bring a mix of software, technologies, and industry experience suitable for various sectors. When choosing a partner, organizations should prioritize pricing clarity, industry-specific experience, good data security practices, and long-term support harmony. A reliable and efficient analytics consulting firm not only delivers reliable insights but also offers sustainable solutions.

6. FAQs

Q1. What services do top data analytics consulting companies in Delhi NCR provide?

Their expertise depends on the industries they work with and the technological research they have performed. From the bird-eye view, the majority of consultants provide data engineering, BI semantic layer design, Python-based transformations, cloud warehouse integration, ERP/CRM ingestion, anomaly monitoring, predictive modeling, schema governance, latency SLAs, and accountability for cross-team adoption.

Q2. Why do enterprises in Gurgaon, Noida, and Faridabad hire analytics consultants instead of relying only on internal teams?
Enterprises hire internal teams for running the business in terms of carrying out day-to-day responsibilities, operational load, and legacy responsibilities. Consultants bring specialized expertise in solving data management issues. Both of them play entirely different roles and are significant in their respective domains.

Q3. How do NCR firms support multi-cloud warehouse BI for enterprise planning?
Top NCR firms usually deploy BI inside Snowflake, AWS, Azure, or GCP warehouses. Firstly, they standardize data models and business definitions. Secondly, they centralize orchestration and data pipelines. Furthermore, they implement a common BI and semantic layer on top of a multitude of warehouses. Finally, they make sure that governance, security, and cost controls apply uniformly across platforms.

Q4. Which industries in Delhi NCR benefit most from analytics consulting and BI adoption?
Key industries that derive major benefits from data analytics consultants include banking and the fintech industry, retail and e-commerce, telecom and internet services, healthcare and pharmaceuticals, transportation and logistics, and IT and technological services.

Q5. What are the most common mistakes enterprises make while evaluating data consultancy partners in NCR?
Common mistakes enterprises make in the selection process include over-indexing on brand, evaluating based on hourly rates rather than outcomes, confusing BI dashboards with data engineering capability, not thoroughly studying the real production cases, and underestimating cloud cost discipline. Choosing tools-first consultants is also a great blunder. Lack of clarity on documentation and enablement can also lead to losses. 

Q6. Do Delhi NCR analytics firms support AI/ML-compatible BI environments?
Yes, top firms do support it, but the level of technological maturity varies considerably. Compatibility is demonstrated not by mere dashboards with charts; rather, it is supported by a strong data foundation that comprises enablement of BI plus ML loops, embedment of governance and lineage, supporting batch plus near real-time data flows, and maintaining historical ready-to-use tables. 

Q7. How should enterprises evaluate analytics consulting pricing in NCR?
It is very crucial to assess cost predictability. Ask whether the pricing is fixed, checklist-based, or open-ended. Carefully evaluate that the pricing is based on outcomes delivered and not just hourly rates. Examples include a number of pipelines, dashboards, or business use cases enabled.

Q8. What skills must BI consultants in NCR demonstrate for enterprise IT and manufacturing use cases?
They should showcase strong data modeling skills, deep SQL and performance tuning, and manufacturing domain KPI understanding. They should be able to do integration of OT+IT data. Scalability across platforms should be taken care of. Decision-focused dashboard designs should be constructed.

Q9. How do enterprises measure analytics success after consulting delivery in NCR?
Some of the important metrics should be considered wisely. KPI agreement, anomaly reduction, pipeline uptime SLAs, latency adherence, schema stability, secure governance, predictive confidence, cloud cost clarity, and BI adoption success are all indicators of success for teams that rely on analytics for performance monitoring, planning, and execution. 

Q10. Which locations inside NCR dominate analytics consulting adoption for enterprise workloads?
Faridabad provides factory-aligned analytics for manufacturing, logistics, paper, packaging, and automotive ERP-compatible BI, while Noida leads healthcare, SaaS, and AI/ML-compatible BI. Gurgaon is the leader in the adoption of BFSI and media analytics. Businesses select partners who possess dependability in various settings while also complementing local strengths.

How to Build a Data Science Portfolio That Gets Hired (Complete Guide)

1. Introduction

A data science portfolio has become one of the most reliable ways of showing real capability when applying for roles in analytics, machine learning etc. Portfolios show whether a person is able to solve problems in real data environments, unlike degrees and certificates, means degrees and certificates may help one in getting shortlisted but portfolios help in deciding whether a person is hired or not.

The hiring teams look for evidence that you understand data behaviour, validation, pipelines and how the results are communicated instead of just algorithms. Many portfolios get rejected or fail because they only focus on tools and technologies. These portfolios lack some factors such as they show dashboards without reliable data, and charts without business meaning.

These portfolios may look impressive at first glance but they  leave the managers unsure about the real capabilities. All the hiring teams want clarity. They want to clearly understand what problem did you solve, how the results are validated, and how the data was handled safely. A good portfolio always states accountability, reliability and structured thinking and removes the doubts.

A strong portfolio is not only a project gallery, instead it is a proof system that shows that a person is able to build models that work in real business.

2. Portfolio Structure That Hiring Teams Understand Quickly

A good data science portfolio should be easy to understand at first sight, only that means the portfolio should be designed in such a way that it gives fast review and clear understanding.

The portfolio must not be filled with unnecessary details, it should only have clear and concise points stating what you can do, how you can think and what results you achieved because the hiring managers spend less than 90 seconds reviewing a portfolio., and deciding whether they should look deeper or not.

A strong portfolio is structured around decisions and outcomes, and not tools. Each project should be accountable for every question. When the structure is clean and your skills stand out then your portfolio feels confident and professional.

2.1) Start With the Business Question, Not the Algorithm

Most portfolios get rejected because they start with tools and algorithms instead of problems. Saying “ I used Random forest” does not explain what you solved. You should always start with a business question like What risk did you clarify? Or what anomalies did you detect?. Once the question becomes clear, you should start explaining the model choice, how you validate it and what result you achieve. 

2.2) Make Table Ownership and Data Sensitivity Explicit

If sensitive data such as PHI or PII is used by your project,even if it is synthetic, then all of these things should be mentioned in the portfolio very clearly. All the fields that are sensitive along with  their protection protocols should be clearly specified in the portfolio. This helps in understanding the difference between the public analytical data and the restricted information. The hiring team gets attracted when they see that the sensitive columns have been masked, or excluded by design.

2.3) Provide Evidence, Not Assertions

Hiring teams are cautious about the big claims. Some statements like “high accuracy” or “business impact” do not mean much without showing proof. Instead of giving explanations about your work, you should show what actually happened. Show how access was controlled, and also how the sensitive data was protected, if there were any retention reviews or exports then they should be mentioned clearly.

3. Must-Have Portfolio Sections With Correct Numbering

A portfolio with a clear and familiar structure usually gets hired easily. Among all the portfolios, the portfolio with predictable sections helps the hiring managers to work faster with less effort. A good data science portfolio should be typically consisted of these following steps: 

3.1) Data Understanding & Data Preparation

This section mainly shows how you understand and prepare the data before modelling. The data should be kept practical as well as simple. One should explain the following activities like what you checked in the data, how you cleaned it and how you made it ready for training. If any sensitive field gets identified, then it should also be mentioned separately.

  • Handling missing values
  • Normalizing columns for model baseline behavior
  • Tagging sensitivity boundaries for PII/PHI columns
  • Structuring tables for model training and audit clarity

3.2) Model Selection & Training Discipline

The main goal of this section is to explain why you chose a model and how you trained that model responsibly. Try showing the fact that you added complexity only when you needed it, otherwise you started it by trying simple baselines models first. Also explain how the model was trained and validated before using the results. The main focus should only be on discipline without showing many algorithms.

3.3) Validation, Drift, and Behavior Monitoring

This section mainly shows you kept an overview of how the model behaved over time, rather than just focusing on the final score. There should be only 3-4 simple steps that explain how the accuracy was validated, and how the unusual behaviour was detected. The clear ownership should also be mentioned separately, like if something goes wrong or some issues occur, then who will be alerted?.

3.4) Deployment, Endpoints, and Access Accountability

This section should mainly focus on explaining how the model would be used in the real world. It can include some points like where the model would run including APIs, dashboards etc. and who can access it and how the access was controlled.

3.5) Business Outcome, Communication, and Artifact Delivery

This section mainly explains what was delivered and why it mattered. In a very small table or by using 2-3 sentences, the outcome should be described, the improved metric and also how the results would be explained to the stakeholders. Mainly focus on clarity and confidence. 

4. Project Ideas That Actually Impress Hiring Teams

All the hiring teams are impressed by the projects that are easy to understand and are clearly grounded in real business problems. They are mainly about showing responsibility, confidence and end to end thinking instead of just using the newest model.

A good or strong data engineering project idea includes factors like pharma demand forecasting that starts with simple regression and shows how sensitive data is handled and deleted correctly, medical risk classification that classify the patient risk and also restrict access to sensitive data, graph based analysis that map relationships between the entities and also show how the results would be explained during audits.

The good portfolios always include the same fundamentals like validation proof, audit clarity, early ownership etc.

5. GitHub vs Portfolio Narrative

Github is the place where you code lives, not just your portfolio. The hiring teams only open your GitHub profile if your portfolio makes an explanation first. Your portfolio narrative should clearly explain the following points:

  • Why the model category was selected
  • How data behaved before modeling
  • What validation discipline was followed
  • What success metric was hit
  • Who would own the table or incident if flagged
  • Whether sensitive columns were masked or tokenized
  • Whether exports required approvals
  • Whether retention deletion auto-ran with proof logs
  • Whether identity access was federated and MFA protected sensitive roles
  • Whether network paths were private and isolated

6. How to Present Portfolio Projects (Hiring-Friendly Story Flow)

A good hiring- friendly portfolio is not a long technical list, it is a clear story. It should be started by explaining the business questions you solved and describing why this model was chosen along with the algorithm that was used. After this, one should also explain how the results got validated and what outcome you achieved.

Once the result becomes clear, show responsibility also. Some important factors such as who owns the data or model, how the exports were controlled, how retention was handled, how lineage could be traced and who would be responsible if something fails should be clearly mentioned. There should be short paragraphs along with few bullets  to improve the clarity.

The main goal is to make it easy for the hiring teams to understand what you solved, how you solved and why they can trust you.

7. Portfolio Design for Regulated Industries (Where DataTheta Works Most)

In regulated industries, portfolios are not only judged on the basis of models, they are judged on the basis of responsibility and control. Hiring  teams expect you to show how the sensitive data such as PHI or PII is handled. They look for early ownership, private network access, encryption by default etc. The incidents should have named owners and the lineage should be clear. At DataTheta these designs are already built at the time of design, they are not added later, and support enterprises across the UK, EU and India.

8. How to Fix Portfolio Weaknesses Structurally

The main weaknesses of most of the portfolios are about wrong ordering, not about missing skills. Adding more algorithms does not contribute. 

Most portfolio weaknesses can be fixed by ensuring:

  • The business question is written first
  • Data behavior is explained before modeling
  • Model category is selected before algorithm complexity
  • Sensitive columns land masked or tokenized before query consumption
  • Identity is federated and MFA protects sensitive roles before access begins
  • Network paths are private before queries run
  • Query audit trails exist before anomalies are flagged
  • Exports require approvals before data leaves the warehouse
  • Retention deletion triggers auto-run before audits are requested
  • Proof logs are stored before deletion is asserted
  • Lineage is mapped before table dependencies are implied
  • Incident owners are assigned before alerts are created

9. Portfolio Hosting Options That Hiring Teams Trust

A portfolio should be hosted in places that are safe, familiar and also easy to open. The hiring teams should not have to log in, request access or download the files.

These include:

  • GitHub (project storage)
  • Kaggle notebooks (execution visibility)
  • Personal site (project narrative)
  • LinkedIn Featured section (visibility for hiring)
  • DataTheta staffing portfolios (domain relevance)

10. Portfolio Checklist (Short and Practical)

A small checklist can be helpful, but keep it minimal:

  • Business question first
  • Data behavior explained before modeling
  • Sensitive columns masked or tokenized before queries
  • Identity federated + MFA for sensitive role
  • Network private and isolated
  • Query audit trails exist
  • Exports approved and logged
  • Retention deletion auto-running with proof logs
  • Lineage mapped for dependencies
  • Incident owners assigned early

11. DataTheta Portfolio Example (Short Narrative Style Only, No Hype)

DataTheta handles the sensitive data such as PHI by masking sensitive fields before the analytics tables were created. Only the approved users are able to access the data, using secured login and extra verification. All data usage was tracked, exports were approved and old data was deleted automatically with proofs.

The flow of data was clearly documented and the owners are assigned in advance to handle any issue if it occurs. This helps to make the system safe, clear and easy to audit, while it can still be usable for analytics.

12. Conclusion

A data science portfolio that gets hired should not only show models, but also show responsible and real world thinking. The hiring teams trust the portfolios that clearly explain how the data is protected, how the data is validated and also how it is owned.

A good and a strong portfolio shows that the sensitive data is handled safely, the data flow is easy to trace and the old data gets deleted automatically. They also define the clear ownership that means if some issues occur, to whom the complaints will go and who will resolve the issues.

At DataTheta, the portfolios are designed with  these controls at the beginning only and before the beginning of analytics that makes them clear and trustworthy.

13. FAQs

1. Why is a data warehouse part of compliance boundaries?

A warehouse stores and processes centralized analytical tables, making it a regulated endpoint when sensitive identifiers are aggregated. Compliance frameworks evaluate query logs, export approvals, identity federation, masking defaults, retention enforcement, deletion proof, and incident ownership. The boundary expands automatically when PII or PHI lands inside analytical tables.

2. Which ML models should be shown in a data science portfolio?

Baseline regression models should be shown first to set performance expectations. Classification models should follow to show probability-based class boundaries. Tree-based models help show logic interpretability. Ensemble models help show noise resiliency. Sequential models help show forecasting literacy. Density-based clustering helps show anomaly grouping literacy. Complexity is selected only after validation discipline is proven.

3. What is the biggest mistake candidates make in portfolios?

Projects often list algorithms before business questions, skip classification before ingestion, keep sensitive columns readable, authenticate separately per cloud, skip MFA for sensitive roles, expose network paths publicly, lack query audit logs, approve exports manually, enforce retention manually, imply lineage without mapping, and assign owners too late. The fix is enforcing structure first, table creation second, model training third, consumption fourth.

4. How can a data science portfolio show compliance awareness?

A portfolio shows compliance awareness by classifying sensitive identifiers before ingestion, masking or tokenizing sensitive columns before queries run, federating identity via IAM, enforcing MFA for sensitive roles before access, mapping lineage for dependencies before table dependencies are implied, assigning incident owners before alerts are created, and certifying sensitive role access quarterly for regulated workloads.

5. Can offshore data science teams maintain compliance?

Yes. Offshore teams maintain compliance when identity is federated, MFA protects sensitive roles, columns land masked or encrypted by default, query audits exist for sensitive tables, exports require approvals, retention deletion triggers auto-run with proof logs stored, and sensitive role access certifications run quarterly. DataTheta ensures offshore teams follow these compliance checkpoints structurally before analytical consumption begins.

6. What makes a data science portfolio hiring-friendly?

A hiring-friendly portfolio reduces ambiguity by structuring business questions first, explaining data behavior before modeling, selecting model categories based on questions, masking sensitive columns before query consumption, federating identity early, isolating network paths privately, auditing queries for sensitive tables, requiring export approvals, and linking GitHub only after narrative clarity is established.

7. How often should access reviews appear in portfolio projects?

Sensitive role access reviews should appear at least quarterly in portfolio projects when simulating regulated workloads. Query anomaly detection logs and export monitoring should be available continuously. Table ownership must be explicit before analytical table creation completes. DataTheta portfolios follow quarterly access certification cycles for sensitive roles.

8. How does DataTheta evaluate portfolios for staffing roles?

DataTheta evaluates portfolios for clarity of business questions, data reliability maturity, model baseline literacy, classification discipline, anomaly detection structure, query-level audit clarity, export accountability, identity federation, MFA for sensitive roles, network isolation awareness, retention enforcement structure, deletion proof availability, lineage mapping clarity, and whether ownership and incident accountability were assigned before analytical consumption begins.

Data Warehouse Governance - Best Practices, Security & Privacy

1. Introduction

The opening of the paper is to introduce why data warehouse governance becomes a critical success factor for enterprises centralising huge amounts of business, operational and patient associated data.

It should underscore the dangers of scoring ever growing datasets in cloud or on-premises warehouses without designated ownership or security guardrails.

The section below should naturally introduce DataTheta as a companion that can aid companies in the ownership organisation, privacy safe data design, role-level access, security control, query anomaly monitoring, regulatory mapping and audit clarity.

2. What Data Warehouse Governance Means (DataTheta POV)

Data warehouse governance basically means how the data is controlled, protected and used inside the data warehouse on a regular basis. It is quite different from broad, organisation level data governance that mainly focuses on policies and definitions.

We can say that the data warehouse governance is much more practical as it mainly focuses on what actually happens to data when it enters the warehouse and how the teams interact with the data.

In simple words we can say that data warehouse governance ensures that the data is organised, traceable, secure as well as trustworthy. Governance has its clear structure and data.

Every table should be able to clearly show the source of data, how the data is transformed and how the data gets refreshed. This clearly helps the team to understand the flow of data and also to fix issues if something gets broken.

Query visibility and audits are also essential as governance helps the organisation to know who is querying which table and why. Governance also includes data residency and retention that means the organizations must ensure that data stays in approved regions and is kept only for the allowed period.

From DataTheta’s point of view, data warehouse governance is not a policy document or a one-time setup. It is an active system built into data engineering and data model design. 

What to include in this section:

  • Governance rules must be built into the system that means governance should not be optional or manual. The data warehouse should forcefully implement rules for access and security.
  • Clear owners should be assigned before the data is used, that means every table, data domain and data pipeline must have a named owner before it goes live who should be responsible for the accuracy and fixing issues.
  • Sensitive columns or data should be protected from the start, which means sensitive fields like personal, financial etc should be identified as soon as the data is entered.
  • All data access should be tracked and data should stay within approved regions means the system should keep a track of who is using the data and when. Before the data gets shared, the system should check whether it is allowed or not.
  • Old data should be deleted automatically with proof that means the data should be removed automatically once its retention period ends.
  • Every issue should have a clear owner that means if any rule is broken then there must be a specific resolver for that issue.

3. The Three Pillars of Data Warehouse Governance

Effective data warehouse governance is based on three core pillars, and each of these pillars ensure that the data is not only secure but also reliable, usable and also trusted by the business. Each of these pillars addresses a different issue, but all three of them work together for governance to succeed in the real world environment.

3.1 Availability

Availability means that a data warehouse is readily available as well as accessible whenever the teams need it, without breaking the  connections, or last minute access issues. This does not mean giving access to data to everyone. However it makes sure that the right people can access the right data reliably and on time.

3.2 Integrity

Integrity states that the data in the warehouse stay accurate, predictable and consistent over time. This helps in ensuring certain things like the teams that can trust today’s data can easily trust tomorrow’s data also, models behave the same way across releases, and the numbers don’t change unpredictably.

If we say simply, then integrity is all about clarity and control. Without integrity, small changes can silently break the reports without being noticed by anybody.   

3.3 Accountability

Accountability simply answers two questions, “ who is responsible for this?” and “who approved this?”. Accountability means that  everything in the data warehouses has a clearly named owner, nothing in the data warehouse is ownerless. Accountability helps in bringing discipline to daily operations. Queries that access sensitive data are logged.

4. Core Components of a Governance-First Data Warehouse

A governed warehouse is designed with in-built controls from the beginning, not added later as fixes. Governance is also considered as a part of architecture, like it shapes how the data is stored, accessed, monitored and also retired.

This approach helps in ensuring that the data volume grows, and how more teams could rely on analytics. One of the major components of data governance is that every user, service that are accessing the warehouse must be authenticated.

Core governance components include:

  • Metadata catalog with schema definitions, business context, sensitivity tags, and ownership mapping
  • Data lineage mapping showing source-to-table-to-query dependencies
  • Ownership responsibility matrix (RACI/RASCI) assigned at design stage
  • Identity & Access Governance (RBAC, ABAC, MFA, SSO)
  • Query audit trails for sensitive table access
  • Retention enforcement with auto-running deletion proof logs
  • Cross-region table movement approvals and residency validation
  • Column-level masking, tokenization, and anonymization
  • Incident ownership loops assigned to named stakeholders

5. Best Practices for Data Warehouse Governance

An effective data warehouse governance is not only achieved through the policies, instead it requires consistent execution in how data is designed, accessed, monitored and maintained.

This ensures these parameters especially in regulated environments where privacy as well as compliance is non negotiable. The first point to start is by designing governance into a warehouse from day one, this helps in preventing gaps that are difficult to get closed later.

Another best practice is to assign a clear ownership at different levels as it ensures data quality issues and helps to resolve them without any confusion and delay.

Governance best practices include:

  • Centralized governance across all warehouse environments
  • Schema updates must follow: Propose → Review → Approve → Version → Deploy → Certify
  • Table and pipeline owners must be assigned before deployment
  • Access reviews should run quarterly for sensitive roles
  • Sensitive columns must be masked or tokenized by default
  • All warehouse access must be encrypted (at rest + in transit)
  • Network paths must be isolated (VPC/VPN/private routing)
  • Query behavior must be monitored for anomalies
  • Data residency boundaries must be validated before cross-region movement
  • Every export must have an owner and approval log
  • Incidents must be assigned to named owners for resolution clarity

6. Security Practices

Security in a data warehouse should not be only about listing tools and using complex technology, instead security in a data warehouse must be clear, practical as well as enforceable. It helps in making sure that the data is protected at every stage, when it is stored, accessed and shared, without getting the analytics work slowed down.

A secure enterprise data warehouse starts with protecting the data which is at rest as well the data that is being used. This ensures that the infrastructure is compromised and the data remain protected.

Security controls include:

  • Encryption at rest
  • TLS 1.2+ in transit
  • Managed key vaults (KMS/HSM/Key Stores)
  • VPC/VPN/private routing isolation
  • SSO + IAM federation
  • MFA for admin and sensitive roles
  • Failed access monitoring
  • Role change logging
  • Sensitive query auditing
  • Suspicious export alerts
  • Access certification cycles
  • Incident owners assigned for every security alert

7. Privacy Practices

Privacy should not be only about writing long policies, it is all about making sure that the sensitive data is handled safely all the time it enters, moves through or is being used in the warehouse. A privacy safe warehouse starts with classifying sensitive data during ingestion.

The personal as well as patient related data should be classified as soon as it gets loaded. This helps the system in ensuring which columns require extra protection before the data starts getting used by the analytics team. Once the sensitive columns get classified, they are protected by default.

Privacy also depends on enforcing data retention rules that means the governed warehouses automatically starts deleting or archiving data when the retention limits are reached and it also generates proof logs that shows when and how the data was removed.

Data movement must be validated before the organisations allow the data to move across the regions. If any privacy related concern occurs, then there is a clear record to support investigation. As a conclusion we can say that privacy governance requires clear ownership.

Privacy enforcement methods include:

  • Static masking
  • Dynamic masking at query runtime
  • Tokenization for identifiers
  • Anonymization where linkage must be permanently removed
  • Auto-running retention deletion triggers
  • Deletion proof logs
  • Cross-region movement validation
  • Query-level auditing
  • Ownership loops for privacy failures or incidents

8. Compliance & Data Residency

Compliance and data residency is all about making sure that the data is stored, used and moved in line with local laws and industry regulations. This is especially important in enterprises such as healthcare and pharma because they handle sensitive personal and patient data across multiple countries.

Different regions have different rules. In the US, HIPAA sets boundations in order to protect patient health information. In Saudi Arabia, the healthcare data is expected to stay within national boundaries or with limited cross border movement. Due to these differences, compliance cannot be handled after the warehouse is built.

All regulatory requirements need to be mapped during the design as it includes various factors such as, where data can physically reside, which teams can access the data and what audit evidence must be available. Data residency ensures that the data does not cross the regional boundaries without any external permission, otherwise these violations could result in fines and operational disruption.

9. Governance Automation & Tools

Governance must be automated so that the controls work consistently everyday, even as new data, users and use cases are added. As the data warehouses are growing in size and usage, governance cannot depend on people remembering rules or following manual processes.

The first step of governance automation is metadata management. Every table, every column and dataset carries information about what it contains, whether it is sensitive or not, and how it should be used. This helps the teams to easily understand the data.

Metadata is also closely linked to data lineage automation, that means the lineage tools help to track the flow of data from source systems to warehouses, how it is transformed and where it is finally consumed. Automation also plays an important role in ensuring privacy and security controls. 

Governance tooling includes:

  • Warehouse-native security controls
  • Metadata catalogs
  • Lineage frameworks
  • Policy engines (OPA/ABAC frameworks)
  • Data masking APIs
  • Query anomaly logs
  • Access certification schedulers
  • Retention auto-triggers
  • Deletion proof logs
  • Role monitoring
  • Suspicious export alerts
  • Incident assignment trails
  • Residency validation layers

10. Governance Failures to Avoid

Data house governance usually fails because the rules only exist on papers and are not enforced in practice not because the policies are missing. These failures usually appear very slowly and become visible only when trust is lost, and incidents take place.

One of the major drawbacks for this is unclear ownership that means when ownership is only assumed but not assignment and due to lack of this the issues get passed between the teams without any resolution. Another major drawback is when privacy rules are defined but not actually applied. Open access without monitoring is another major risk and the governance fails when the query behaviour is not observed.

Common failures include:

  • No assigned data owners
  • Privacy policies written but not running
  • Open warehouse access
  • No query anomaly logs
  • Manual retention enforcement
  • Silent data drift
  • Unapproved schema updates
  • No residency validation
  • No export approvals
  • No incident accountability

11. DataTheta 5-Stage Governance Activation

DataTheta helps to implement data warehouse governance using a structured 5 stage activation model that makes governance practical as well as enforceable. This model embeds the control step by step as the warehouse is designed, deployed and operated instead of treating governance as a one step model. The first stage primarily focuses on understanding the data as it enters the warehouse.

Sensitive data is identified early so the warehouse knows which columns require protection. In the second step lineage is mapped end to end, before the data is made available to users. This shows how the data flows from source systems through transformation into final tables and dashboards.

In the third stage, privacy and security policies are designed to match regulatory requirements. In the fourth step we enforce the policies by default, once they are defined. Encryption and masking is automatically applied to the sensitive data. In fifth stage monitoring, incident ownership and maturity reviews take place as they ensure that the governance stays active over time.

DataTheta 5-Stage Model:

  1. Data discovery & classification
  2. Map lineage & assign owners
  3. Design policies & residency boundaries
  4. Enforce encryption & masking
  5. Audit, monitor, certify ownership

12. Conclusion

Data warehouse governance is a part of core infrastructure  that allows a data warehouse to operate reliably at scale, it is not just a one time project or a temporary compliance exercise. When the governance is built into the foundation, the warehouse becomes much easier to  trust, easier to manage and to grow.

A governed warehouse performs better because people have confidence in it and it is also trusted by the stakeholders. Teams spend less time in conclusion and more time in acting on it. When something goes wrong, governance limits the impact by clearly showing where the issue occurred and who is responsible for fixing the issue.

Strong governance also helps in reducing risk. Failures are investigated at the earliest and also gets resolved with clear accountability instead of guesswork.

13. FAQs

1. What is Data Warehouse Governance?

Data Warehouse Governance means building and running a data warehouse with clear rules that are always active, not added later. It also ensures that the data is protected, structured and also used responsibly from the moment it enters the warehouse. Data warehouse governance is also responsible for the storage, transformation and sharing of data. A good data warehouse governance also creates trust.

2. How is Data Warehouse Governance different from data governance?

Data governance is broad. It defines the wide rules, policies and responsibilities for how that data should be managed across systems in an organisation. Data warehouse governance is more specific and also hands-on. All these rules are applied directly inside the data warehouse. Simply we can say that data governance helps in setting the rules while data warehouse governance makes sure that those rules actually run and are enforced inside the warehouse.

3. What are must-have security controls for enterprise warehouses?

Enterprise data warehouses need practical security controls that work by default, not  just complex setups that depend upon manual checks. The data when stored must be encrypted and accessed using some secure standards. The access to the network should be restricted through VPC or VPN isolation, so only approved systems can be connected. Users can log in through SSO and IAM federation.

4. How should warehouses handle PII and PHI safely?

Data warehouses should protect sensitive personal and patient health information from the moment the data starts getting ingested. Sensitive data needs to be identified as early as possible with clear tags at both table and column level so the system knows what needs protection.

5. Why is data lineage a governance necessity?

Data Lineage is important because it reflects the original source of data, how the data gets transformed and where data is being used. They connect source systems, pipelines, tables etc. in order to remove confusion during audits. As the data gets broken or the numbers start getting changed, the lineage teams quickly start finding the root cause for that.

Data Analytics Pricing Breakdown: What Companies Actually Pay in 2026

1. Introduction

Nowadays companies no longer ask for what analytics tools can do, instead they ask for what analytics actually cost. Data analytics spending goes far beyond, as compared to software licenses.

Data analytics includes many  services such as cloud infrastructure, data engineering efforts, security, monitoring, management and most importantly people to run all of it securely and reliably.

Many organisations get shocked seeing the analytics cost as they come from places that are quite easy to overlook. Factors such as uncontrolled queries, unclear ownership etc. don’t show up immediately instead they quietly create big issues over time and they slowly increase both cloud costs and audit risk.

There are also some hidden issues that lead to higher bills, harder audits and many more, because the resources are used inefficiently.

They also make the audits harder because it becomes unclear who accessed the data and why it was used. Rather than being a technical requirement, cost transparency has become a business requirement.

All the business leaders are eager to know clear answers to what they are paying for like platform usage, governance controls and BI tools.

2. The Main Categories of Analytics Costs

Recently, enterprise costs come from various sources or areas and not just by tools.Each category is influenced by how the teams use data and also has a direct impact on spending. Each category is closely tied to governance discipline.

2.1) Infrastructure and Compute Usage

Cloud data warehouses charge based on the amount of compute that is required to run the queries. This is often the largest cost driver. Factors such as inefficient queries, large table scans etc. can quickly increase the costs.

2.2) Storage

The storage costs mainly depend upon the amount of data that is kept and for how long it has been kept. Raw data, processed tables, backups all of these factors consume storage and also increases the costs over time.

The real expense comes from poor retention management, encryption adds only a small cost. Storage also keeps growing when the old or unused data is not deleted automatically.

2.3) Data Engineering and Operations

Continuous efforts are needed by the analytics platform to run. Teams must help in building and maintaining pipelines, monitor usage and also in fixing issues.

The cost of this work adds up quickly. Factors like salaries, contractors, and tools used to manage governance contribute in making a large share of total analytics spending, frequently exceeding the platform costs.

2.4) Identity and Access Management

Control access is mandatory for security analytics. This includes activities like sign-on, multi factor authentication, role monitoring etc. Mainly the tools and processes that are needed to control the data access are responsible for driving the IAM costs.

These controls increase the cost day by day, but they also contribute to reducing the security risks.

2.5) Data Governance and Compliance Costs

BI tools usually get priced on the basis of the number of users, but how these tools are used also increases the cost. When dashboards are refreshed frequently, or when they run complex queries by many people at the same time, the computer usage gets increased. 

2.7) Third-Party Tools and Integrations

External tools for export governance, monitoring, data catalogs, model oversight are added by many organisations. Some of these tools are essential but some only add cost without giving a clear value if they are not governed carefully.

BI tools usually get priced on the basis of the number of users, but how these tools are used also increases the cost. When dashboards are refreshed frequently, or when they run complex queries by many people at the same time, the computer usage gets increased. 

2.7) Third-Party Tools and Integrations

External tools for export governance, monitoring, data catalogs, model oversight are added by many organisations. Some of these tools are essential but some only add cost without giving a clear value if they are not governed carefully.

3. Typical Pricing Ranges by Company Size (2026 Estimates)

In 2026, analytics cost varies widely. How much a company spends depends upon the size of the company, the amount of data used, how often the queries run and how regulated the business is.

Smaller companies usually spend less because their data and usage are limited. The points below give a simple and practical view of what companies typically spend.

3.1) Small and Emerging Enterprises

Small companies usually have a very simple need for analytics. They are in total a team of around 50 employees. Data volumes are low, dashboards are limited and the data engineering work is frequently handled by a small team or a small group of people.

  • Infrastructure (compute + storage): $20k–$80k
  • Data engineering (outsourced/contractor): $40k–$120k
  • BI tools & dashboards: $5k–$30k
  • IAM & governance tooling: $10k–$40k

Total: $75k–$270k annually

3.2) Mid-Sized Enterprises

Mid sized companies consist of usually 50-500 employees. These companies have a much higher analytics usage. They run dashboards, support more users, handle sensitive data, and often operate across teams.

  • Infrastructure: $150k–$600k
  • Storage: $50k–$200k
  • Data engineering & operations: $300k–$1.2M
  • BI tools & dashboards: $80k–$300k
  • IAM & governance tooling: $100k–$350k
  • Compliance evidence tooling: $100k–$400k

Total: $780k–$3M annually

3.3) Large Enterprises and Regulated Industries

Large enterprises in healthcare, pharma, manufacturing and financial services have the most complex as well as expensive analytics environment. They usually deal with high volumes of data, strict regulations and also large BI user bases.

  • Infrastructure: $800k–$4M+
  • Storage: $200k–$800k+
  • Data engineering & governance operations: $1.2M–$5M+
  • BI tools & dashboards: $300k–$1M+
  • IAM & access governance: $400k–$1.2M+
  • Compliance & audit evidence tooling: $500k–$2M+

Total: $3.4M–$14M+ annually

4. Hidden Cost Drivers You Might Not See in Your Bill

Some analytics costs slowly show up over time, they do not clearly appear on the invoices. These hidden costs usually appear from how the data platform is used and managed, not only from the tools and softwares.

4.1) Query Behavior Anomalies

A large amount of compute is suddenly consumed when there are poorly written or repeated queries. These spikes often happen without any prior warning. Some tasks like clear ownership, query monitoring etc. help in identifying the issues as early as possible and also help in avoiding unexpected bills.

4.2) Retention Proof Logs

In order to fulfill the compliance requirements, organisations must have a proof of data deletion done on time. Storing these deletion logs adds storage as well as processing cost, but during the audits it also saves a significant effort.

4.3) Export Approvals and Logging

Sensitive data that are included by the exports must be approved and logged. This increases the operational work and also reflects the storage costs, it also prevents sharing of risky data during the compliance reviews.

4.4) Identity Access Certifications

In regulated environments, access to sensitive data must be reviewed regularly. The amount of time spent on access reviews, certification tools etc. adds cost, but it also reduces security and audit risks.

4.5) Lineage Mapping Tools

Across multiple warehouses, the lineage tools can be expensive. They also reduce the investigation time and audit confusion by clearly showing the flow of data and dependency of tables upon each other.

5. Cost Optimization Best Practices

In 2026, active governance and automation is required in controlling analytics cost. The practices that are shown below help keep spending predictable, while reducing audit as well as operational risk.

5.1) Monitor Query Behavior Continuously

Monitor query behaviour helps in tracking the behavior of query in real time. In order to keep the analytics cost under control, it is very important to watch how the queries run everyday. Some queries scan very large tables, run repeatedly, or use efficient joints that contribute to consuming a lot of compute without even adding value.

5.2) Enforce Classification Before Ingestion

This means that the sensitive data should be identified as soon as it enters the system, not after its use. Classifying the data early makes it clear which field needs protection from the beginning itself. This helps to prevent the risky exports, and reduces the last minute changes during the audits.

5.3) Automate Retention Deletion with Proof Logs

Retention rules should not be manual, they should be automated. When the retention period ends the data should be deleted or archived automatically. In order to handle the audits quickly, the proof of deletion should be stored in an easy  to access location.

5.4) Federate Identity and Certify Sensitive Roles Quarterly

With the help of federated identity, the user access is directly tied to official company accounts, and not manual setups. The sensitive data should at least be reviewed once every quarter to make sure that the permissions are still needed.

5.5) Govern Export Paths with Approvals and Logs

The exports that contain or carry the sensitive data should never run silently. The exports must be fully logged and must go through an approval process so it becomes clear who approved the export, what data was shared and when did it happen.

5.6) Use Unified Lineage Across Multi-Warehouse Environments

Unified data lineage basically shows the actual source of data, how the data is transformed and where the data is used across the systems. This makes it accountable for answering questions during the audits or investigations without doing manual digging.

6. Example Pricing Breakdown: A Healthcare Enterprise in 2026

The healthcare enterprise operating across the US, EU and KSA, handles sensitive data and strict regulatory requirements. These organisations use strong governance tools such as identity federation, extra approvals, unified data lineage etc. Analytics is used both in clinical teams as well as senior leadership.

  • Infrastructure (compute + storage): ~$2.2M
  • Data engineering & governance operations: ~$3.2M
  • IAM & access governance: ~$1M
  • BI & dashboards: ~$650k
  • Compliance & line-age tools: ~$1.4M

Total Annual Spend: $8.45M

The cost remains predictable because the governance is built in from the start, despite the scale. The data pipelines are validated, sensitive data is accessed early, ownership is clearly assigned and the incidents are resolved without putting continuous manual efforts.

7. Vendor and Tool Considerations

7.1) Cloud Warehouse Choices

  • Snowflake, BigQuery, Redshift, Synapse
    Each has different pricing models. Snowflake separates storage and computation and offers auto-suspend options. BigQuery charges for scanned data and query usage. Redshift charges for node hours. Synapse has data and compute units. Choose based on query behavior patterns, cost governance tooling, and dataset volume.

7.2) Data Pipeline Orchestration

  • Airflow, dbt, Prefect, Dagster
    Orchestration tools add labor cost for data engineering, retries, observability, and pipeline validation. Choose based on governance hooks, alerting, and incident ownership workflows.

7.3) Governance Tooling

  • Metadata catalogs, lineage tools, export governance platforms, audit evidence stores
    These increase spend but reduce audit cycles, reduce ambiguity, and improve compliance clarity.

7.4) BI and Visualization Layers

  • Power BI, Tableau, Looker
    Consider cost per user, concurrent usage, refresh frequency, and cost impacts of ungoverned dashboards.

7.5) Identity and Access Management

  • IAM federation, SSO, MFA solutions
    These add to governance cost but reduce compliance risk.

8. Conclusion

In 2026, the pricing of data analytics is not just about the tools and the dashboards, instead it is a blend of infrastructure, storage, identity management, query behavior etc. The cost of data engineering and governance work is much more than the cost of BI tools, in regulated industries.

Some hidden issues such as unclear data lineage, untracked exports contribute to increasing costs. All these problems make spending unpredictable and also increases the chances of audit risks. Organisations that are successful in managing the cost, focus better on governance before scale.

Understanding the full pricing view helps the data leaders in better budgeting, choosing the vendors wisely etc.

9. FAQ’s

1. Why do analytics costs spike even when storage seems stable?

Compute spend is driven by query behavior. Repeated full table scans, inefficient joins, or dashboards triggering expensive warehouse queries increase compute hours and scanned data charges. Cost spikes are usually workload-driven, not license-driven. Monitoring query anomalies early prevents silent budget overruns.

2. What is the largest cost component in enterprise analytics in 2026?

For most enterprises, the biggest cost is labor for data engineering, governance operations, and warehouse compute usage triggered by analytical workloads. Tool licenses are often a smaller percentage compared to compute, storage retention, and engineering overhead. Regulated data workloads increase operational accountability and audit evidence requirements.

3. Do companies pay separately for compute and storage in cloud warehouses?

It depends on the platform. Some warehouses split compute and storage billing, while others charge for node hours or data scanned per query. Even in split billing, compute remains variable because analytical workloads trigger warehouse engines repeatedly. Understanding the pricing model helps plan budgets realistically.

4. Can offshore data engineering teams support regulated analytics environments?

Yes, when identity is federated, MFA protects sensitive roles before queries run, network access is private, columns are masked or encrypted before analytical tables complete, queries are audited continuously for sensitive table access, exports require approvals, retention deletion auto-runs with proof logs stored, lineage is mapped early, table and pipeline owners are assigned early, incident owners are assigned early for flagged behavior, and sensitive access is certified quarterly at minimum before business consumption begins.

5. Why is deletion proof more important than retention policies?

Retention policies define how long data should live. Deletion proof logs demonstrate that retention deletion actually happened. Audits ask for evidence, not assertions. Manual deletion creates ambiguity. Automated retention triggers with stored proof logs remove compliance backtracking cycles.

6. What is the most common mistake companies make when budgeting for analytics?

Most companies budget for licenses but underestimate compute, retention storage, identity governance, export approvals, lineage, ownership assignment, and incident resolution loops. They assume pipelines will behave reliably without validation. The fix is budgeting for workload behavior, not only tooling.

7. How do executives measure analytics success today?

Executives measure success by clarity of interpretation, reliability of pipelines, ownership accountability, auditability of sensitive table queries, approved exports, retention proof logs stored, lineage mapped early, incidents resolved with owners assigned early, and insights communicated without repetitive internal reasoning loops. Accuracy matters, but clarity and adoption matter equally.

List of Top 10 Snowflake Consulting Companies and Implementation Partners

1. Introduction

Snowflake has become one of the most chosen cloud analytic and data platforms for all the organizations that demand scale, speed and flexibility.

It helps the businesses by allowing them to store, process and also to analyse large volumes of structured, semi structured as well as streaming data from many sources to one place. Due to this reason snowflake is broadly used in many fields such as business intelligence, advanced analytics etc. 

The finance, sales and operations teams find it very easy to work from the same data and build reliable dashboards for decision making. Eventually, building snowflakes at an enterprise level is not so simple.

Setting up the platform is the main step, despite it being powerful. Many organisations still fail in initial deployment that means bringing data from different teams and systems together in a structured way.

2. What Is Snowflake Consulting?

Snowflake consulting doesn’t simply mean setting up a snowflake environment, but it majorly focuses on designing and operating the platforms that can be used to support real business use cases.

It majorly keeps focus on designing, building and also in operating data platforms that helps in transforming raw enterprise data into trusted, governed as well as decision ready insights. 

Data availability is not the main goal but it is to make sure that the data is reliable, secure, accurate and is easily accessible by everybody in the organization.

A Snowflake consulting engagement typically includes data modeling that aligns with business requirements, setting up secure multi-environment architectures (development, testing, and production), and implementing strong governance frameworks.

Fields like consulting and performance cost management are also covered in snowflake consulting. Snowflake also ensures reliable flow of data from source systems into Snowflake without delays or failures by building automated data ingestion pipelines.

3. Top 10 Best Snowflake Consulting Companies and Implementation Partners in the World

3.1 DataTheta

DataTheta is a Snowflake focused consulting firm that treats snowflakes as more than just a tool to be installed. Snowflake supports everyday business operations and DataTheta allows these organisations by providing platforms like snowflake as a core data system.

DataTheta helps build snowflake environments that are secured, well-managed as well as cost controlled. 

As the data grows, more users are  added and the workload also increases and in order to meet these requirements the systems are built reliably.

Clear rules are provided for data usage, performance expectations, and system reliability so that teams can easily trust the platform. DataTheta is a Snowflake focused consulting firm that treats snowflakes as more than just a tool to be installed. 

Snowflake supports everyday business operations and DataTheta allows these organisations by providing platforms like snowflake as a core data system. DataTheta helps build snowflake environments that are secured, well-managed as well as cost controlled. 

As the data grows, more users are added and the workload also increases and in order to meet these requirements the systems are built reliably. Clear rules are provided for data usage, performance expectations, and system reliability so that teams can easily trust the platform.

3.2 Deloitte Snowflake Practice

Deloitte’s Snowflake practice is designed for large enterprises where governance, compliance, and enterprise-wide consistency are critical, because in such environments data systems are unable to work independently.

Deloitte approaches Snowflake adoption by keeping in mind that analytics systems must fit into existing risk, audit, and regulatory frameworks. If we say technically then Deloitte helps the organizations to implement snowflake in a secure and much more controlled way. 

This includes multi environment architecture, defining strict access control, enabling audits etc. Snowflake environments are set up in such a way that they follow both internal compliance policies and external regulatory requirements. 

3.3 Accenture Snowflake Services

Accenture delivers Snowflake services as part of large-scale, enterprise-wide transformation programs rather than treating them as standalone data projects. Standalone data projects are basically those projects or analytics initiatives that are built independently, without being fully  connected to an organisation’s core system or long term business processes. 

These projects mainly focus on solving a single problem only like creating dashboards, migrating a dataset without even considering how the solution will scale. From a technical point of view accenture focuses on deep integration between Snowflake and the existing enterprise technology landscape. 

3.4 WNS Snowflake Consulting

WNS approaches Snowflake consulting with a strong emphasis on operational analytics, reliability, and sustained adoption across large, distributed enterprises, this means that it focuses on how analytics is used in day to day business operations, not just by building dashboards and reports. The main goal is to make sure that the snowflake platform is reliable, performs consistently and can be used by the teams over a long period of time. 

Rather than only focusing on initial platform setup, WNS helps organizations to design, build, and operate Snowflake environments that can support ongoing business needs at scale. WNS supports end to end snowflake data engineering that includes data ingestion, transformation etc. Data pipelines are designed with reliability in mind to ensure that analytics outputs remain accurate and timely. WNS helps in ensuring that sectors like finance, operations work from consistent as well as trusted data.

3.5 Tiger Analytics Snowflake Practice

Tiger Analytics delivers snowflake consulting with a strong focus on connecting business outcomes directly to data platforms. The practice is built on the belief that Snowflake should act as a single, governed analytics foundation for the entire organization, that means if snowflake is treated as a governed foundation then controls such as data ownership, quality checks are directly embedded directly into the system. 

When the data volumes and usage increases, it ensures that the analytics workflows remain secure, consistent and reliable. Operational reliability is a key part of this model, we can say this because the data pipelines are monitored, performance is regularly tracked and issues are detected early so that the analytics systems continue to work smoothly.

3.6 IBM Snowflake & Hybrid Data Consulting

IBM helps large organizations to use Snowflake in environments where older systems and cloud platforms already exist. Many enterprises still depend on mainframes, ERP systems, and highly secure or regulated data environments in order to run their core business operations. 

These systems often manage crucial functions such as finance, payroll, supply chain etc. Organisations cannot replace them without risk because they are stable, trusted as well as deeply embedded in daily operations. Data stored in these environments has to follow specific rules related to access control, and data protection.

3.7 Capgemini Snowflake Services

Capgemini helps in delivering Snowflake services through structured, and multi-phase adoption programs that are well suited for large and complex organizations. Rather than attempting a full-scale implementation at once, Capgemini follows a step-by-step approach that helps the enterprises in reducing risks, maintaining control as well as measuring progress at every stage. 

Capgemini primarily focuses on setting up the foundation that includes defining the snowflake architecture, security model and environment strategy to meet enterprise requirements to achieve scalability, performance and data protection. This ensures the platform is stable and ready to support future growth. In order to make data accurate and timely, pipelines are monitored and validated.

3.8 KPMG Snowflake & Analytics Governance

KPMG helps large organizations use Snowflake in a much more safe and controlled manner. Their focus is to make sure that the data is trusted, protected and governed properly from the beginning itself, not only by moving data to the cloud. KPMG’s approach is totally  opposite as compared to many companies. 

They design Snowflake setups where rules, controls, and accountability are built in from day one, so teams can use data freely without creating risk for the business and the data is also accessible by all. One of the major key strengths of KPMG is data access control. 

They help companies to decide who can see the data, who has the access to change the data and who is responsible for the accuracy of data. They set their snowflake environment in such a way that helps the auditors, risk officers to verify that from where all the data came, how the data was processed, who reads the data and whether the data meets regulatory rules or not.

3.9 Slalom Snowflake Consulting

Slalom helps the organisations to adopt snowflake in such a way that it helps in delivering quick value for everyday business use. Their main approach is to make sure that snowflakes don’t just remain a technical project owned by the IT department, it should be a platform that can be used by all business leaders and organisations for decision-making. 

As the projects run for months without having any visible outcomes, and sometimes it also becomes overly complex, due to this issue many companies struggle with data systems. Slalom helps to resolve this issue by focusing on fast and outcome driven delivery. 

3.10 Snowflake Professional Services & Partner Ecosystem

Snowflake has its own team that helps companies in setting up snowflakes, it also works with many certified consulting partners. These teams help the organisation to avoid the mistakes and get started faster while using the snowflake platform. 

Snowflake owns the experts who know the platform quite well who help the companies to design the snowflake correctly from the beginning. It specifies how the data is stored, how it is shared, how security works and how different teams contribute to using the systems safely. It plays an important role when snowflakes are being used across many teams, countries as well as regions.

4. How to Select the Right Snowflake Consulting Partner

Choosing  a right snowflake consulting partner doesn’t mean selecting someone who knows the snowflake platform, the main question that a right snowflake consulting partner should be able to answer is “Who  will own the system once it is live?”. Many partners are quite good at setting up snowflakes, but only fewer are willing to take responsibility for keeping it reliable and affordable. 

There are many factors that should be considered before choosing a right snowflake consulting partner and some of them are ownership of data pipelines that means ensuring that data arrives on time, and does not fail.

There should be clear schema and data model governance that means the partner should define who owns schema, tables and data models. Enterprises should ask for clear uptime commitments. If pipelines break, someone should be accountable for fixing them within agreed timelines.

5. Conclusion

Snowflake makes it very easier for the organisations to collect and bring all their data at one place only. It allows the teams to run reports, build dashboards and use data for planning and machine learning on a single platform. But setting up a snowflake does not only mean. 

But only setting up snowflakes doesn’t only guarantee better decisions. Many organisations realise this after setting up the snowflake.

Some of the major issues like reports not being refreshed on time, numbers don’t match between teams, costs get increased, are seen. This doesn't happen because of the weakness of snowflakes, this happens due to lack of ownership.

Data Analyst vs Business Analyst vs Data Scientist - Key Differences

1.1) Introduction

Organisations often find it difficult to differentiate between three roles that are Data Analyst, Business Analyst and Data scientist. All three of these work with data, but each one uses it for different purposes in different ways.

Understanding these differences is important because it helps organisations assign the right responsibilities to the right role, and to avoid overlapping. A data analyst focuses on understanding what has already happened by looking at the past and present data.

A data analyst collects data from different sources, cleans it and then organizes it, so that it's accurate and ready to use. Their main role is to turn raw data into understandable information, by using reports, charts, dashboards to present data in a clear and visual way.

This helps to notice changes as well as see patterns over time. A data analyst turns raw data into clear insights, that helps the team to understand performance and find the problems as early as possible.

A business analyst uses data to understand how a business actually works and where things can be done better or improvements can be made. They study data along with business processes to spot problems, gaps, or areas that slow down the things.

Based on these understandings, a business analyst suggests what actions should be taken next. These suggestions focus on improving workflows, and help the decisions to make better decisions. A business analyst works as a translator between data and people.

A data scientist uses statistics, mathematics and machine learning to analyze large and complex datasets. Their main focus is to find the patterns and relationships that are not obvious at first sight.

They build predictive models that can forecast future outcomes, such as trends or behaviour and also optimise decisions by suggesting the best possible actions based on data. The main goal of the data scientist is to find what is likely to happen next and how the outcomes can be improved.

2) Core Role Differences in Enterprise Language

2.1) Data Analyst – Insight From Historical and Operational Data

A Data Analyst works with structured data to help teams understand what has already happened in the business as well as the current flow of business. They work closely on dashboards, reports, and BI tools, and their main focus is to make sure that the numbers are accurate, consistent, and also easy to understand.

On a daily basis, a Data Analyst pulls data using SQL i.e structured query language, builds and maintains dashboards, checks that KPIs are calculated correctly, and explains trends or changes to business teams.

They also make sure that different reports don’t show different answers to the same question.

Enterprises expect a Data Analyst to answer questions such as:

  • What happened last quarter?
  • Which region performed best?
  • What are the KPI trends?
  • Why did this metric change?
  • Are the dashboard numbers aligned

A good Data Analyst helps the business clearly understand what happened last quarter, which regions truly performed well, and how key KPIs are moving over time. Most importantly,  they explain why a metric changed not just that it did.

2.2) Business Analyst – Defining the Business Meaning of Data

A Business Analyst helps make sure data work actually supports real business decisions. Their role starts with understanding what the business is trying to achieve. They listen to stakeholders, study how processes work, and identify where decisions get delayed or unclear.

Instead of focusing on tools or visuals, they focus on why something is being measured and how it should be used. A Business Analyst clearly defines KPIs and also assigns ownership,so that everyone gets to know what a metric actually means and who is responsible for it.

They document business rules in as simple language as possible, helping data engineers and BI teams build the right logic without unnecessary guessing. Before anything goes live, they validate or check the dashboards and reports from a business point of view just to make sure that it actually answers real questions, not just to look good.

Their work includes things like:

  • Process mapping
  • KPI definitions and ownership
  • Business rules documentation
  • Solution validation from business context
  • Dashboard design alignment to decision questions

Business Analysts are evaluated on the basis on how clearly they think, communicate, and document. Their success is measured by clarity as well as usefulness not just by technical complexity.

3) Skills, Tools, and Expectations Comparison

The Data Scientist concentrates on whether they can use the data to predict what will happen next or whether they can help the business make a more informed decision. They don’t ask about the work you’ve done, they ask about what will happen later.

They analyze data using statistics and machine learning models to uncover patterns, minimize risks, test hypotheses before deciding on anything. Their work is also argued to rely a great deal on clean, reliable data the best model is of little value if the data imported into it cannot be trusted.

They also help in deploying models into real systems that need their predictions to be used every day and not just in an experiment style.

Data scientists typically are problem solvers like:

1.Predicting need so that teams can anticipate better

2.Identifying high-risk customers early

3.Detecting unusual patterns

3.1) Tools Commonly Used

Each role uses a different tool because each role is responsible for solving different types of problems.

A Data analyst works with SQL and BI tools like Power BI, Tableau, Lookers etc. in order to create reports and dashboards. These tools help data analysts analyse past data and also to present data in a clear and readable form.

A business analyst uses tools such as workflow diagrams, and documentation tools to understand the flow of business. Business analysts sometimes also use SQL and BI design layers to check if the KPI’s are defined correctly or not to match the business rules.

A data scientist uses advanced versions of tools like Python and machine learning frameworks like TensorFlow or PyTorch. They also use tools  related to model management & optimization to build, test and run prediction models, depending upon the complexity of the model.

3.2) What Enterprises Should Expect From Each Role

A data analyst should help the business users understand the past performance as well as the current trends. Data analysts don’t  only build dashboards, but they also ensure that the reports show the same numbers everywhere by creating consistent KPI’s. By looking at the dashboards, the business users should immediately understand what the numbers mean and  also trust the numbers. 

A business analyst makes sure that the data solutions match the business needs. A business analyst defines KPI’s, maps business processes, aligns workflow and clearly communicates with the team. They also make sure that the reports and dashboards only answer real business questions instead of the unnecessary information.

A data scientist mainly focuses on helping the business look ahead and also to prepare the future plans. Data analysts use past as well as current data to build predictive and optimisation models that estimate future risks, opportunities and outcomes.

They extract meaningful features from raw data and design them, test different approaches. They also ensure that the predictions are accurate and reliable. They also make sure that the data going in the model matches the data coming out, so that the results can be explained and trusted by the business teams.

4) Bullet Summary: Quick Differentiation Checklist

  • Data Analyst: Data analysts help the organisation to understand what has already happened to the businesses in the past as well as what’s the current status of the business. They mainly work on the structured data from systems such as finance, sales, operations etc. They use the SQL to pull the right data and to check the accuracy of that data. They also ensure that the KPIs are calculated consistently across all the reports. This helps in preventing businesses from situations where different dashboards show different numbers for the same metric. Data analysts are also responsible in creating and maintaining dashboards that can be used by the business teams. These dashboards are built in such a way that help the leaders to easily read and also see their performance without any kind of technical help.
  • Business Analyst: Business analysts act as a connecting bridge between business teams and data teams. They are good at understanding the business needs, day-to-day processes as well as the decisions that are needed to be made by a leader. On the basis of this, analysts clearly define the KPIs and also make sure that everyone agrees on what each metric means and how they should be measured. They ensure that the dashboards are designed around real decision questions, not just to display the numbers only. Their main goal is to make sure that analytics output show the actual flow of business and help the team to make honest and reliable decisions.
  • Data Scientist: The major focus of a data scientist is to use the data to predict the outcomes as well as to support the future focused decisions. A data scientist identifies patterns, forecast trends and estimate risks by creating statistical and machine learning models. A key part of their work is feature engineering, that means converting raw data into meaningful inputs that can be used by model to learn from. They help the team to detect the issues early by calculating the probability of unusual activity. They continuously monitor the model performance and are responsible for accuracy SLA’s.

These roles complement each other when the data foundation is reliable and governed before scale, not after failure symptoms.

5) How Enterprises Should Decide Which Role They Need

Enterprises should choose the right role by clearly understanding the questions they need to answer, not just by hiring based on the job titles or market trends. Organisations can structure their teams more effectively when they are trying to focus on solving real problems, whether it is understanding past performance, aligning data with business decisions etc. 

If we need an answer for “ what happened?”, then the organisation needs a data analyst to answer. Data analysts majorly focus on analysing past and current data, in building trustworthy dashboards as well as to explain trends and changes in performance.

Data analyst helps the organisation to clearly understand the current position of the business. If the question is "What should we build to support business intent?”, then we need a business analyst. This question is all about understanding why something is needed before deciding what to create.

This involves identifying the business goals, the decisions that are needed to be made as well as the actions that will follow once the information is available. This also includes deciding which KPIs actually matter, how these KPIs should be calculated, who owns them and how they fit into existing business processes.

If we have to answer the question “ What is likely to happen next, especially at scale or under constraints?”, then one needs a data scientist to answer. Data scientists analyse patterns using past data as well as present data that helps the businesses to predict the future outcomes and possibilities.

They help in building predictive models that forecast demands, as well as customer behaviour. 

6) Conclusion

A Data Analyst, a Business Analyst, and a Data Scientist each play a very different role in how an organization uses data. Data analysts help the organisation to understand what has already happened to the businesses in the past as well as what’s the current status of the business.

They mainly work on the structured data from systems such as finance, sales, operations etc. They use the SQL to pull the right data and to check the accuracy of that data. Business analysts act as a connecting bridge between business teams and data teams.

They are good at understanding the business needs, day-to-day processes as well as the decisions that are needed to be made by a leader. On the basis of this, analysts clearly define the KPIs and also make sure that everyone agrees on what each metric means and how they should be measured.

The major focus of a data scientist is to use the data to predict the outcomes as well as to support the future focused decisions. A data scientist identifies patterns, forecast trends and estimate risks by creating statistical and machine learning models.

A key part of their work is feature engineering, that means converting raw data into meaningful inputs that can be used by model to learn from.

Top 10 Data Analytics Service Providers Companies in USA (United States of America) [2026 Updated List] with (Reviews, Ranking & Services)

1. Introduction

Currently, American businesses are swimming in more data than ever, however they find it very difficult to turn all that information into clear and useful plans. This is because data is scattered across different apps and cloud systems. Many organisations are now hiring expert analytic firms to help them. 

These partners don’t only make basic charts, but they also ensure that the data is accurate and help the leaders to predict future trends. They help the fast-moving industries like healthcare, finance, energy, retail and tech. 

Nowadays, companies don’t only look for one time report but they seek for reliable experts who understand their specific business goals and help them in making better and faster decisions.

2. What Is Data Analytics? 

Data Analytics basically means collecting the data and then studying the data thoroughly to understand what is happening in a business or an organisation. It helps people to identify problems, find patterns and opportunities by converting the raw data into something meaningful.

Data analytics help organisations to make better decisions, improve efficiency, reduce risks, predict future outcomes and track company’s performance over time. This prevents the organisation from relying on assumptions and guesswork.

3. Top 10 Best Data Analytics Companies in the USA (United States of America)

3.1) DataTheta

DataTheta is a US-registered company that helps businesses  to make better usage of data and helps in better decision making. Rather than only focusing on tools and technologies, the company works on real business problems and builds data analytics systems that support planning, forecasting and tracking performance.

DataTheta provides services such as organising and preparing data, analysing future trends, using AI to support decisions and creating clear reports for leadership teams. 

The company makes sure that the data efforts support business and revenue goals by working closely with managers and executives. DataTheta places strong importance on clean, reliable data and proper governance as it has experience in working with industries like healthcare, pharma, manufacturing etc.

3.2) Mu Sigma

Mu Sigma is an experienced data analytics company that usually works with large businesses across the United States. This company helps the organisations by answering difficult business questions by using data, statistics and structured analysis.

Areas such as business strategy, operations, marketing insights and risk management are supported by this company. Many fortune 500 companies rely on MU Sigma when they need analytics support at a large scale. 

This firm is quite popular for its systematic approach, strong data governance and ability to support consistent decision making across different teams and business functions.

3.3) Fractal Analytics

Fractal Analytics helps large and mid-sized companies in the US use data as well as artificial intelligence to improve the growth of their business. They work across industries such as retail, healthcare, financial services where data plays a major role in decision making.

Fractal supports companies in fields like customer behavior analysis, pricing and operational planning. It helps the team to understand the current growth of business and actions that should be taken in future to boost the growth. 

Fractal builds analytic solutions that are used in real business workflows rather than just focusing on the reports and dashboards. This firm uses the combination of strong technical execution with business consulting. This helps the organisation move beyond the pilot projects and make analytics an important part of decision making.

3.4) Tiger Analytics

Tiger Analytics helps the companies in the United States in using data more effectively. It provides services in data engineering, analytics and machine learning. In order to build solutions for reporting, forecasting and tracking performance, the company works closely with both business and technology teams. 

Tiger Analytics supports industries such as retail, banking, financial services, media  and healthcare. It majorly focuses on practical analytics that helps to solve real business problems while ensuring that the data systems are reliable, scalable and easy to maintain.

3.5) Tredence

Tredence works with companies in the United States to help them to solve business problems using data. The company primarily focuses on understanding the business first and  then applying analytics to understand specific challenges.

Services like building data systems, advanced analytics and decision support tools that help improve sales performance and operational efficiency are included. Tredence ensures that analytics insights lead to clear actions as well as measurable results by working closely with business teams.

3.6) Alteryx

Alteryx is a US-based software company that provides tools for data preparation and data analysis. This platform helps analysts and business users clean, analyse and combine data without needing advanced coding skills.

Alteryx is used by many companies to create faster reports , automate repetitive data tasks and reduce their dependency on IT teams. Advanced analysis is also supported by this platform. This also allows teams across different departments to use a consistent, repeatable reporting process.

3.7) Palantir Technologies

Palantir technologies help to build data platforms that are used by US government agencies as well as by large companies. It helps the organisation to collect data from different sources and systems together at a place.

Palantir’s tools are used to support planning as well as day to day decision making, especially in complex environments. These platforms are built for situations where data must be accurate, secure, and able to handle very large volumes. Industries like defense, healthcare, manufacturing and energy.

3.8) Genpact

Genpact works with large companies in the United States to help them improve how they use data in every time business operations. Genpact helps the organisation to track financial performance, monitor risks and manage supply chains. Genpact solutions are often built into regular  business processes not kept separate as one time process. 

This means that teams can consistently rely on data during decision making. Genpact helps organisations reduce efficiency, improve accuracy and scale their operations more smoothly across departments and regions, by combining deep industry knowledge with strong analytics and data management skills.

3.9) Deloitte Analytics

Deloitte is a huge global company. They mainly help the US companies with the giant projects like hospitals, banks, factories. They also organize their information in a much sorted way and moreover make plans for them to work on.

Deloitte is a quite  large firm so they only focus on large projects, Ex. updating old computer systems into new ones and also makes sure that everything works under the guidelines of the government. Their main objective is to build large and organised systems that help the company to see the data clearly.

3.10) Accenture Analytics

Accenture is a giant global company that is popular in helping some of the biggest businesses in America to handle giant and complex projects.

It is the company that is remembered by everybody when they need to build a brand-new and huge data system from scratch. Even if they  are so large, they are best at managing projects that include different teams and also take quite a long time to get finished. They also ensure that different parts of a company’s technology work together correctly.

4. How to Choose the Best Data Analytics Company in the USA?

4.1) Pricing

Pricing is not only about the costs, it is also evaluated on the basis of project’s scope that also means the size of the project, the complexity of the project that means how difficult the project is and on the value that means the benefits the client will get. There are 3 key pricing models:- The first one is Project-based pricing that means there is a fixed price for a job. Second is Monthly-retainer that means that you charge a fixed charge for each month for the ongoing work. Third one is Dedicated resources that means the clients pay for your time and skills.

4.2) Reviews and Client Feedback

Client feedback is basically a report card from the past clients that reveals aspects like strengths in delivery that means how well did they complete the work, communication that means how clear and timely the updates are and also reliability that show if the work is on time or not.

4.3) Industry and Domain Expertise

Industry experience means that the provider has hands-on work experience in specific fields like business, social media management etc. They matter more because they have already learned the tricks, trends and tools for those specific fields. They only focus on the exact needs and avoid the unnecessary stuff.

4.4) Technology and Tool Expertise

Strong analytics partners know how to handle various tools smoothly. They  should know how to comfortably work across cloud platforms like AWS, google cloud etc, on databases like SQL & MongoDB, on reporting tools like Google Data Studio or Tableau for pretty dashboards, and analytics frameworks. This makes sure that they have a smooth and clear integration with the existing systems.

4.5) Alignment with Business Goals

Analytics work helps the business to plan better and track real results like growing sales etc. rather than just providing the charts or the numbers. A good analytics partner links insights to KPIs to provide clear goals by connecting data and also shows what decision should be taken. This also helps you to understand how performance can get improved for the future.


5. Conclusion

Choosing the right data analytics company in the USA plays a crucial role as it affects how well a business uses its data to plan ahead, track performance and also to make decisions.

However many data analytics companies offer similar tools and technical skills, but what truly sets a good one apart is differentiated by factors like understanding the industry properly, delivering the work on time, by focusing on helping the leaders make better decisions, rather than just only creating reports. 

A strong analytics partner has clear and honest pricing, shows real client success and feedback and has a good and honest experience in your business domain. The major factor is that they work closely with your teams and turn the raw and messy data into reliable insights that help with both daily operations as well as long-term strategy.

6. FAQs 

Q1. What do top data analytics service providers in the USA offer?

The top data analytics companies in the United States set up the full data system that means connecting all the data sources, cleaning and organising the data and making sure that the data flows continuously without breaking down, so that everything works smoothly and reliably, rather than just making the reports. 

They help by connecting all data sources from different places like CRM Tools, accounting software so that the information doesn’t get scattered. 

Raw data is often very messy, so they fix these problems by removing the duplicates & arranging data in a meaningful manner. The Analytics team sets one clear definition for each KPI and applies it everywhere which results in no confusion, no arguments and everyone sees the same number. 

They automate data updates so that no one has to manually upload files that helps saving time and reduces human errors. The best analytics firm always makes sure that the data flows continuously without breaking and the issues are already fixed before the user notices it.

Q2. Why do enterprises face dashboard distrust even after investing in BI tools?

Dashboard distrust happens when people stop believing the numbers they see. That means when the team looks at the dashboard, they see a number and it doesn’t seem real to them. When that happens, people stop using the dashboard and move abc to the excel sheets and ask others to ‘confirm’ the numbers. 

They make decisions based on the gut feeling instead of data. This usually happens because of problems behind the dashboard, not because of the dashboard tool itself. 

Some behind the scenes problems cause distrust when the data comes from multiple places and is not synced, when the data is late or missing and when the same metric is calculated in different ways. The dashboard only shows what it receives. If the data is broken, the dashboard will reflect that.

Q3. Do US analytics firms deploy AI models inside cloud warehouses?

Deploying AI is only the first step, many data firms can deploy AI and machine learning models on popular cloud platforms like AWS, Azure, snowflake, bigQuery and many more. This is important but this isn’t the hard part anymore. The main thing is “What happens after deployment”.

AI models don’t work on their own, they depend completely on the data they receive. In order to give reliable results  the data must be clean, consistent and should be continuously monitored. 

If the data changes, the AI starts giving wrong predictions. Many companies don’t only look for AI models. Someone must take responsibility for the full system around it. This includes managing data structure changes, ensuring that the data arrives on time, maintaining audit records, refreshing models regularly etc.

Q4. What is the biggest mistake enterprises make when choosing analytics vendors?

The biggest mistake enterprises make while they choose analytics vendors are when they choose only by price or tools. When companies pick a data partner just because the price looks low or the proposal list contains many popular items, they make the wrong decision. 

Most proposals focus only on building things rather than running and maintaining them. There are many problems and issues that remain unnoticed like who will watch the data everyday, who will fix the broken pipelines, who will prepare for audits and manage data change, who will ensure timely arrival of data and model updates? 

The problem slowly starts building up, at first the dashboards look fine and the teams are satisfied but slowly many problems start occurring like failures of pipelines, increment in cloud costs and much more. 

Dashboards get abandoned because the numbers can’t be trusted, and it takes way too long to fix the issues. So, analytics is an ongoing responsibility rather than just a project. The best partners stay accountable for reliability, accuracy and trust over time.

Top 10 Best Data Analytics Companies in India [2026 Updated List] with (Rankings, Services, Reviews)

1. Introduction

In the past couple of years, India has established itself as a big hub in the market for providing high quality data analytics and consulting services. It is attracting both local and international companies that are looking for advanced-level analytics solutions to better analyse their business and for growth. 

The use of data is still very important for the organisation’s to properly plan, measure performance and strategise the future decision making. With digital platforms having been adopted rapidly at large scale, today companies are heavily investing in analytics based solutions that are more than simple reporting, for better performance analysis, prognostication, and for high level operational assistance.

This has pushed the demand for the analytics partners in India to help in the structured engineering of data, predictive modelling, analytics implementation, and regular reporting frameworks. 

In this blog, I will share with you an in-depth list of the 10 best data analytics companies in India on the basis of their key services, customer feedback, experience in the industry, and analytics performance results. This will help you to select the right data analytics services provider in India that solves your business problems and helps in achieving more revenue for your business.

India host a number of popular analytics hubs such as Bangalore, Hyderabad, Mumbai, Delhi/NCR, and Chennai, each offering specialized data analytics services todifferent industry clients.

Related Blogs-

Top Data Analytics Companies in Bangalore

Top Data Analytics Companies in Hyderabad

Top Data Analytics Companies in Mumbai

Top Data Analytics Companies in Delhi/NCR

Top Data Analytics Companies in Chennai

Top Data Analytics Companies in Gurgaon / Gurugram

Top Data Analytics Companies in Noida

2. What is Data Analytics in India?

Data analytics in India is the process of collecting, preparing and analyzing the business data in order to discover certain types of hidden trends, patterns and insight. It helps in the proper planning and making decisions for the end business growth. 

In India, data analytics basically consists of data integration, predictive modeling, performance measurement, visual reporting and a list of tools & technologies to facilitate the business objectives of various departments.

Leading data analytics companies in India utilize a combination of tools & technologies to help different industries like healthcare, energy, E-commerce & logistics, Manufacturing, Banking & FinTech etc. to solve their business problems.

3. Top 10 Best Data Analytics Companies in India [2026 Updated List]

I have done proper research on the basis of different metrics like their analytics capabilities, client impact, industry expertise, scalability etc. and curated this list of the top 10 data analytics companies in India to further grow the business. Each of the below mentioned companies has a proven track record of successfully delivering data-driven business outcomes for different industry clients. 

The reviews focus on what each of these analytics firms does best, the industry they serve, technologies they utilize, and certain other reasons why they stand out in the India fast growing artificial intelligence (AI) and data analytics ecosystem.

3.1 DataTheta

DataTheta is a leading data analytics consulting company based out of India that is helping enterprises across industries like Pharma, Healthcare, Retail/CPG, Energy, and BFSI. They have been in this industry for the past 7+ years. 

The team at DataTheta specializes in transforming fragmented enterprise data into unified, analytics-ready platforms. It further supports faster decision-making and measurable business outcomes.

The company provides end-to-end services including Data Analytics, Business Intelligence, Data Engineering & Warehousing, Data Science, and GenAI solutions. DataTheta is known for its strong focus on governance, compliance, security and scalability. 

The DataTheta team of senior data engineers & scientists brings together more than 10 years of average experience. It ensures high-quality delivery across complex analytics initiatives.

They have successfully delivered 80+ data projects with a 98% client satisfaction rate. DataTheta combines industry expertise with flexible engagement models. These are:- fixed-time projects, managed services, and developers-on-demand (also known as fixed time resource). You can hire on demand fixed time resource from DataTheta according to your project requirements.

This agile approach helps the businesses to modernize their analytics capabilities efficiently along with properly maintaining control over cost and better performance.

DataTheta - Company Profile

Key Services:

  • Data analytics and data engineering services
  • Business Intelligence (BI) solutions and dashboarding
  • Data warehousing strategy, implementation, and modernization
  • AI-powered analytics and predictive insights
  • Decision support systems and analytics consulting
  • End-to-end analytics implementation from strategy to execution

Industries Served:
DataTheta works with enterprises across multiple sectors, including:

  • Pharmaceuticals & life sciences
  • Healthcare
  • Consumer Packaged Goods (CPG) / Retail
  • Energy & utilities
  • Banking, Financial Services & Insurance (BFSI)
  • Other data-intensive verticals requiring analytics transformation

Year Established / Founded:
2017
(DataTheta was founded in 2017 to bridge analytics and decision science for enterprises).

Headquarters:

  • Chennai, Tamil Nadu, India (registered presence)
  • Additional offices in Noida, Uttar Pradesh, India
  • U.S. presence in New York and Texas (Katy, TX) as part of global operations.

Best for:
Enterprises seeking scalable, outcome-driven analytics and decision intelligence solutions that go beyond dashboards to build data ecosystems, predictive models, and BI platforms with measurable business impact.

Employee Size:
Approximately 1-50 employees (LinkedIn and Glassdoor company size range), consistent with an agile analytics consultancy.

Founders:
According to public company registry info, DataTheta (Lance Labs Pvt Ltd) was founded by:

  • Easter Prince
  • Abhishek Keshav

3.2 LatentView Analytics

LatentView Analytics is another well known multinational data analytics company that has a solid presence in India. It works in the niches like retail, finance, technology, and consumer goods. LatentView also provides high level business analytics services like customer-segments, predictive modeling, optimization methods, and data engineering services. 

LatentView is a consulting firm that uses technical expertise and consulting experience to help the organizations in developing analytics processes that are linked to business planning cycles. 

Its solutions are fully customized to enhance the accuracy of reporting, expose the useful patterns, and better the overall decision support between functions. LatentView works with quantifiable analytics results as well as reporting systems that help in long-term performance monitoring.

LatentView Analytics - Company Profile

Key Services:

  • Data analytics consulting and business intelligence
  • Data engineering and big data services
  • Advanced analytics including predictive analytics
  • Customer, marketing, supply chain, financial, HR, risk & fraud analytics
  • AI and machine learning solutions
  • Analytics advisory and roadmap services, GenAI readiness

Industries Served:

  • Technology
  • Consumer Packaged Goods (CPG)
  • Financial Services
  • Retail
  • Industrials & other enterprise sectors

Year Established / Founded:
2006

Headquarters:
Ramanujan IT City, Rajiv Gandhi Salai, Taramani, Chennai, Tamil Nadu 600113, India

Best for:
Enterprise analytics and digital transformation through data-driven insights and AI-led decision support. Well suited for large companies seeking advanced analytic solutions and strategic analytics adoption.

Employee Size:
Approximately 1,001-5,000 employees (LinkedIn range); other estimates show ~1,100-1,200 employees.

Founders:
Venkat Viswanathan and Pramad Jandhyala 

3.3 Fractal Analytics

Fractal Analytics is a well-established data analytics company in India that has well-developed analysis, forecasting, and measurement of activities. The company helps both SMB and large enterprises in the medical field, financial services, retail and technology sector. 

It provides a wide range of analytics tools and modeling solutions. Fractal provides services that are categorized into data preparation, the creation of machine learning models, and the final deployment of analytics. Fractal analytics follow an result oriented approach that is aimed at providing key insights for proper business planning processes. 

Clients trust on Fractal analytics when they require some help with the performance evaluation systems, trend analysis and scalable reporting solutions that are reliable and in accordance with the enterprise requirements.

Fractal Analytics – Company Profile

Key Services:

  • Enterprise AI and advanced analytics solutions
  • AI strategy consulting and decision intelligence platforms
  • Data science, machine learning, and predictive modeling
  • AI product incubation (e.g., Asper.ai, Flyfish, Senseforth.ai)
  • Generative AI and automation solutions for business processes
    Fractal focuses on powering human decisions with scalable AI across the enterprise.

Industries Served:

  • Financial Services
  • Consumer Packaged Goods (CPG)
  • Healthcare and Life Sciences
  • Retail
  • Insurance
  • Technology and other global enterprise sectors
    Fractal works with Fortune 500 companies across these verticals.

Year Established / Founded:
2000

Headquarters:
Dual headquarters in Mumbai, India and New York City, United States

Best for:
Delivering enterprise-grade AI solutions and analytics transformation to global organizations, especially Fortune 500 companies looking to embed AI into strategic decision-making and business operations.

Employee Size:
Approximately 4,500-5,500+ employees globally (varies by source; estimated around 4,600-5,500)

Founders:

  • Srikanth Velamakanni
  • Pranay Agrawal
    (also co-founders including Nirmal Palaparthi, Pradeep Suryanarayan, and Ramakrishna Reddy in early history)

3.4 Mu Sigma

Mu Sigma is another well known decision sciences and analytics consulting firm that offers formal analytics and performance measurement consulting to multinational corporations located in India and other Indian cities. Mu Sigma has a substantial presence in India and helps the companies in addressing complicated business challenges by using statistical methods, organized assessment, and replicable analytics models. 

Mu Sigma popular services include risk analysis, forecasting systems and operations measurement. Mu Sigma is well trusted by large scale business organizations who follow a proper disciplined analytics execution and regular performance tracking among business units. They have offices located in all the major IT hubs of India.

Mu Sigma - Company Profile

Key Services:

  • Decision sciences and data analytics consulting
  • Big data analytics and data engineering
  • Predictive analytics and decision support systems
  • Marketing, risk, supply chain, and customer analytics
  • Business intelligence & advanced modeling for enterprise decision-making

Industries Served:

  • Finance and Banking
  • Consumer Packaged Goods (CPG)
  • Retail and eCommerce
  • Technology and Telecom
  • Healthcare and Pharmaceuticals
  • Airline, Hospitality & Entertainment

Year Established / Founded:
2004

Headquarters:
Northbrook, Illinois, USA (3400 Dundee Rd, Suite 160)

Best for:
Enterprise-level decision sciences and analytics solutions, especially for Fortune 500 clients seeking deep analytics, data-driven decision frameworks, and scalable decision engineering.

Employee Size:
~3,500-4,000+ employees (various estimates; often listed as ~3,500 to ~5,000)

Founders:

  • Dhiraj Rajaram (Founder, Chairman & CEO)

3.5 Tredence

Tredence comes at the 5th place in this list of top data analytics services providers in India and it is a popular data science and analytics advisory firm. They provide data engineering, analytics model implementation, and forecasting systems offered by the company to help their clients in improving the accuracy of the planning and performance evaluation. 

Tredence collaborates with every type of business to implement analytics processes & solutions that can be used to achieve quantifiable results and performance data. Tredence provides its services to consumer goods, health and technology industries. Its strength lies in the well structured delivery and clarity of insights that are often praised by organizations.

Tredence - Company Profile

Key Services:

  • Data science and analytics consulting
  • AI and machine learning solutions
  • Data engineering and data modernization
  • Generative AI and Agentic AI implementations
  • Customer analytics and customer 360 platforms
  • Supply chain and revenue growth analytics
  • MLOps and LLMOps services
  • Digital engineering and analytics accelerators
    These services help bridge the gap between insights and real business outcomes with tailored solutions.

Industries Served:
Tredence serves enterprises across multiple sectors, including:

  • Retail
  • Consumer Packaged Goods (CPG)
  • Banking, Financial Services & Insurance (BFSI)
  • Telecom, Media & Technology
  • Healthcare & Life Sciences
  • Travel & Hospitality
  • Industrials & Manufacturing
    Its solutions are deployed in industry-specific contexts to maximize value and adoption.

Year Established / Founded:
2013
(Tredence was founded in 2013 as a data analytics/AI services firm).

Headquarters:
San Jose, California, USA
(with delivery and presence in Chicago, London, Toronto, Delhi, Chennai, Bangalore, and other locations).

Best for:
Delivering industry-centric data analytics, AI and AI-driven transformation solutions that help enterprises realize tangible business impact from data investments. It is known for solving the “last mile” problem between insights generation and value realization.

Employee Size:
Approximately 3,500+ employees globally (with teams of data scientists, engineers, ML experts and consultants).

Founders:

  • Shub Bhowmick (Co-Founder & CEO)
  • Sumit Mehra (Co-Founder & CTO)
  • Shashank Dubey (Co-Founder & CRO)

3.6 Absolutdata

Absolutdata provides customer analytics and behavioral modeling and data based decision support consulting and services to India based clients. The company provides the features of segmentation analysis, data preparation, predictive models, and performance reporting dashboard.

Absolutdata helps the businesses from different industries like retail sector, financial services and technology in improving their understanding of customer behaviour and quantifying results in the key performance areas. Its services facilitate unceasing enhancement of analytics solutions and the augmented view of performance indicators. 

Absolutdata has a strong India delivery presence that makes it a preferred analytics partner for global enterprises. They have a highly skilled analytics talent, provide cost-effective delivery, strong domain expertise, and deep expertise in providing scalable project execution on time.

Absolutdata Analytics – Company Profile

Key Services:

  • Advanced analytics and AI-driven decision science services
  • Proprietary NAVIK AI platform with SaaS solutions (e.g., SalesAI, MarketingAI, ResearchAI)
  • Predictive analytics, customer & sales optimization
  • Marketing analytics, data integration, and AI model solutions
  • Custom analytics consulting and data science services
    Absolutdata helps enterprises turn data into actionable decisions using analytics and machine learning.

Industries Served:

  • Consumer Packaged Goods (CPG)
  • Retail & eCommerce
  • Financial Services & Insurance
  • Technology & Telecom
  • Healthcare & Life Sciences
  • Other global enterprise sectors (Fortune 500 clients)
    Absolutdata works with large mid-market and enterprise customers across multiple verticals.

Year Established / Founded:
2001
- Absolutdata was founded in 2001 as an advanced analytics & AI company.

Headquarters:
Alameda, California, USA
1320 Harbor Bay Parkway, Suite 170, Alameda, CA 94502, United States

Best for:
Enterprises seeking AI-powered analytics and decision intelligence technology and services that combine analytics consulting with scalable AI solutions. Often chosen for marketing, sales, customer analytics, and enterprise-wide AI deployments tied to business outcomes.

Employee Size:
Estimated 250–500+ employees globally (Glassdoor and industry estimates range from ~380 to ~500).

Founders:

  • Suhale Kapoor
  • Anil Kaul (also served as CEO)
  • Sudeshna Datta
    Originally founded by this team before being acquired by Infogain in 2020.

Acquisition:
Absolutdata was acquired by Infogain (a global digital services firm) in December 2020, aligning its AI and analytics capabilities with Infogain’s digital and engineering services.

3.7 Genpact

Genpact is another well known name in providing top notch analytics solutions in India. It helps in building scalable & robust data analytics programs which improves the optimal operational measurements, risk assessment and reporting systems. 

Genpact key services include data preparation, implementation of analytics, and performance tracking systems which helps the enterprises. The Genpact analytics team works in emerging sectors such as finance, supply chain, and compliance sectors where reliability and sound measurement is needed.

Genpact helps enterprise businesses to transform via data analytics, artificial intelligence (AI), cloud technologies, and automation. They have clients from different industries like finance, retail, healthcare, supply chain, and manufacturing.

Genpact – Analytics & Data Services Profile

Key Services:

  • Data strategy and data engineering
  • Intelligent data management, data governance, and data products
  • Business analytics and advanced analytics solutions
  • AI and machine learning–powered analytics transformation
  • Cloud analytics and modern data platforms
  • Industry-specific data solutions and managed services
    Genpact’s analytics practice helps enterprises turn complex data into actionable business insights using AI and advanced data platforms.

Industries Served:
Genpact delivers analytics and data services across a broad array of sectors, including:

  • Banking, Financial Services & Insurance (BFSI)
  • Healthcare & Life Sciences
  • Energy and Utilities
  • Consumer Goods and Retail
  • Manufacturing & Automotive
  • Technology, Telecom & Media
  • Transport & Logistics
  • Chemicals and Life Sciences
    Genpact couples industry expertise with analytics to drive outcomes for clients worldwide.

Year Established / Founded:
1997 (as GE Capital International Services; renamed Genpact when it spun off from GE)

Headquarters:

  • Primary executive headquarters: New York City, New York, USA
  • Legal domicile: Hamilton, Bermuda (stand-alone corporate entity)
    Genpact also maintains major delivery centers globally, including in India, Europe, Asia-Pacific and the Americas.

Best for:
Large enterprises seeking end-to-end data transformation and analytics services integrated with digital transformation, process intelligence, AI and operational consulting. Genpact is especially strong where analytics is tied to business process optimization and AI-driven decision support at scale.

Employee Size:
~125,000+ employees globally (varies by source, ~125k to 150k+).

Founders:

  • Pramod Bhasin (Founder; former CEO and long-time leader who established Genpact as a GE unit and led its growth)

3.8 WNS Analytics

WNS Analytics is an India based data analytics company that gives proper performance measurement solutions. This includes- predictive modelling, reporting structures, and trend analysis. The firm serves clients in the retailing, insurance and healthcare industries. 

WNS Analytics' experienced team utilizes proper domain experience and organized analytics operations that helps the organizations in improving the visibility of performance and proper accuracy of reporting at functional levels. 

WNS analytics integrates data, analytics, AI, and human expertise to help businesses extract key important insights, modernize data infrastructure, and enable smarter decision making. WNS utilizes AI and analytics with domain expertise to deliver business outcomes rather than just dashboards.

WNS Analytics - Company Profile

Key Services:

  • Predictive, descriptive and exploratory analytics to uncover patterns and actionable insights
  • Data architecture design & analytics consulting
  • AI-powered and ML-enabled analytics platforms and accelerators
  • Business intelligence (BI), reporting & insight generation
  • Data governance, data management and cloud data modernization
  • Industry-specific analytics products and frameworks blending AI and domain expertise
    WNS pairs proprietary AI assets with consulting to drive decision intelligence for enterprises.

Industries Served:
WNS Analytics supports enterprise clients across 10+ sectors (examples broadly included across WNS):

  • Banking & Financial Services
  • Insurance
  • Healthcare & Life Sciences
  • Retail & Consumer Goods
  • Travel & Hospitality
  • Technology & Telecom
  • Manufacturing
  • Logistics & Supply Chain
    WNS serves global clients with analytics embedded in broader digital transformation engagements.

Year Established / Founded:
WNS was originally founded in 1996 as World Network Services (later WNS Global Services). WNS Analytics emerged as the company’s analytics, data and AI practice over the past two decades as part of its service portfolio.

Headquarters:

  • Corporate HQ: New York, United States
  • Operational and delivery centers across Mumbai, India and other global locations.

Best for:
Enterprise analytics and business decision intelligence programs that are part of broader digital transformation, AI and intelligent operations services. Particularly well-suited for organizations needing analytics integrated with BPM, cognitive automation and industry-specific solutions.

Employee Size:
WNS (Holdings) Ltd employs ~64,000+ professionals globally across business process management, digital transformation and analytics functions; a significant portion supports data/analytics-driven services.

Founders:
WNS (Holdings) was launched in 1996 as a business process management operation originally part of British Airways and became an independent company following private equity investment. It does not have a single founder in the classic startup sense.

Key Leadership (related to analytics):

  • Keshav R. Murugesh - Group Chief Executive Officer of WNS Holdings.

3.9 Cognizant Analytics

Cognizant is another leading global information technology and data consulting company that has offices in India. They provide AI based analytics consulting and execution services based in India delivery centers. 

It further serves to help in preparing data, analytics workflow implementation, and performance evaluation. The experienced team at Cognizant help businesses to create analytics models, combine reporting systems and enhance the delivery of insights. 

Cognizant works with healthcare, banking, logistics, and retail clients and helps them to consolidate analytics practices and improve the accuracy of the planning. Cognizant offers a wide range of analytics and data-driven services to solve their clients' business problems.

Cognizant Analytics - Company Profile

Key Services:
Cognizant’s analytics and data capabilities are part of its broader Data & AI Services offering. This includes:

  • Enterprise data strategy and analytics consulting
  • Data engineering and modern data platform solutions
  • Business intelligence and visualization
  • Machine learning, AI and generative AI solutions
  • Data governance, master data management, and AI governance
  • Industry-specific analytics and decision-made platforms
    Cognizant also offers modular accelerators like Ignition for data modernization and scalable analytics deployments.

Industries Served:
At present, Cognizant serves a wide spectrum of industry verticals with analytics and data-enabled capabilities, including:

  • Banking, Financial Services & Insurance (BFSI)
  • Healthcare & Life Sciences
  • Retail & Consumer Goods
  • Technology, Media & Telecom
  • Manufacturing & Automotive
  • Travel, Transportation & Logistics
  • Utilities and Energy
  • Public Sector & Education
    The practice embeds analytics within transformation programs across these sectors.

Year Established / Founded:
Cognizant Technology Solutions was founded in 1994 (as Dun & Bradstreet Satyam Software, later renamed).

Headquarters:
Teaneck, New Jersey, USA (primary corporate headquarters).

Best for:
Large enterprises seeking end-to-end analytics, data modernization, AI and transformation services embedded within a full technology and consulting ecosystem. It excels at tying analytics to enterprise modernization and business outcomes.

Employee Size:
Cognizant employs over 300,000 people globally across services including analytics, IT consulting, and digital transformation.

Founders:
The company traces its origins to:

  • Kumar Mahadeva
  • Lakshmi Narayanan
  • Francisco D’Souza
    These leaders played major roles in establishing Cognizant’s early growth and global identity.

Notes on Leadership:

  • Current CEO is Ravi Kumar Singisetti (also referred to as Ravi Kumar S).

3.10 Accenture Analytics

Accenture is a popular name in offering global business and analytics services through technology, transformation, and innovation. Accenture provides advanced level data analytics services to enterprise clients of India in terms of data platform strategy, analytics implementation and reporting systems. 

The company operates in areas like the technological, medical, financial, and production industry. Accenture services are used by organizations to easily consolidate the data sources, enhance the accuracy of predictions, and support decision-making processes in the context of digital transformation initiatives. 

The team at Accenture utilizes a broader data & AI practice with primary focuses on turning data into insights that drive decision making, improve performance, and enable digital transformation.

Accenture Analytics - Company Profile

Key Services:
Accenture’s analytics practice operates as part of its broader data, cloud and AI services, delivering:

  • Data strategy and analytics consulting
  • Data engineering and modern data platforms
  • Business intelligence and reporting
  • AI and machine learning solutions
  • Generative AI and decision intelligence
  • Cloud-enabled data capabilities and governance
  • Analytics-driven transformation programs

Accenture utilizes its latest technology and consulting ecosystem to help organizations scale data insights and transform with AI and cloud at the core.

Industries Served:
Accenture serves a wide range of industries globally, including:

  • Financial Services & Insurance
  • Healthcare & Life Sciences
  • Retail & Consumer Goods
  • Communications, Media & Technology
  • Energy & Utilities
  • Public Service & Government
  • Manufacturing & Automotive
  • Travel & Hospitality
    The firm provides analytics and digital capabilities tailored to industry-specific needs.

Year Established / Founded:
Accenture was founded in 1989 (originally as Andersen Consulting).

Headquarters:
Accenture is headquartered in Dublin, Ireland (1 Grand Canal Square, Dublin 2).

Best for:
Accenture Analytics is best for enterprise-level data and analytics transformation, especially for large companies seeking an integrated approach that combines strategy, technology, cloud, AI and consulting. Its strength lies in end-to-end data-driven reinvention at global scale.

Employee Size:
Accenture employs ~779,000 people worldwide across strategy, consulting, technology and operations practices (fiscal 2025 figure).

Founders:
Accenture as a firm was formed from Andersen Consulting, and it became an independent public company in 2001; its roots trace back to Arthur Andersen. It does not have individual founders in the startup sense; rather it evolved from Andersen Worldwide and transitioned to Accenture Plc.

4. How do we choose the top data companies in India for Data Analytics and AI Consulting Services?

4.1) Strong Expertise Across Data Analytics, AI, and Engineering

The top data analytics company in India should have core expertise in Data analytics and AI. A strong firm demonstrates in-depth capabilities across data analytics, machine learning, artificial intelligence and data engineering, rather than just  focusing on a single area. 

Data analytics expertise ensure the ability to collect, process and interpret structured and unstructured data accurately. Machine learning and AI capabilities enable the development of predictive models, pattern recognition etc. Data engineering skills ensure that data pipelines are scalable and reliable.

4.2) Proven Track Record and Measurable Business Results

A good data analytics company must have proven track records and measurable outcomes. A proven track record means that the company has successfully completed data analytics and AI projects in the past. This is usually shown through case studies or examples of earlier work. 

These case studies mainly explain what were the problems that company faced, how it used the data or AI to resolve them and what were the results they achieved. Measurable outcomes such as improved accuracy, faster processes and better predictions, shows that the company solutions actually work. 

When a company shows its past results, it proves that it has real experience rather than just having theoretical knowledge that helps to reduce risks and build confidence that the company can deliver reliable solutions again.

4.3) Industry and Domain Experience That Drives Better Insights

A good data analytics company must have an industry as well as domain experience that means the company should understand how a specific field works, along with the type of data it uses and the challenges it faces. 

When a data analytics company has experience with industries such as healthcare, finance, manufacturing, or education, it already knows the common data problems, processes and key metrics used in those fields. 

Domain experience also helps the company interpret data correctly to provide relevant and meaningful insights. As a result, the solutions delivered are more practical and accurate.

4.4) Scalable Technology Stack and Reliable Infrastructure

The best data analytic companies must have a technology stack and infrastructure. A scalable technology means the company uses modern tools and platforms that can handle increasing amounts of data without having performance issues. 

This includes cloud platforms, big data framework, and analytics tools that allow systems to score, process, and analyse large datasets efficiently. A strong infrastructure also ensures reliability, flexibility and future readiness.

5. Why do we need Data Analytics in India?

India is generating huge amounts of data everyday like from digital payments, E-commerce, healthcare systems, education etc. The data that is gathered or collected from these various platforms has a little value on its own, until this data is structured, analysed or converted into insights. 

Data analytics plays a crucial role in converting data from volume to value. Data analytics in India is mandatory to convert this raw data into meaningful insights to promote efficiency and innovation. Data analytics is used by businesses in monitoring the customer behaviour, and also to make faster and evidence based decisions. 

India’s digitalisation will only increase the complexity and scale of data. Data analytics turn fragmented data into clear insights for better decision making and provide efficiency and growth.

5.1) Turning India’s digital data into meaningful insights

India’s digital ecosystem is expanding rapidly with platforms such as Digital UPI, Online services and much more. All these services produce a large amount of data on a daily basis. Every digital interaction like payments, transactions help in creating data continuously. 

Data analytics plays an important role in organising, processing and interpreting this data. Analytics help identify patterns, trends and relationships within data, turning raw data into meaningful insights. Data analytics add structure to scattered information by cleaning data, integrating the data  across systems and using it by applying analytical models. 

Data analytics ensure that the data collected through these digital platforms is not wasted and transformed into knowledge that supports better planning and optimisation.

5.2) Enabling Better Decision-Making Through Data Analytics

Data analytics helps in better decision making across different sectors or industries in India. Organizations in India are shifting from intuition-based decisions to data- driven decision making. 

As the digital data is growing day by day from systems like E-commerce platforms and much more, this helps the leaders to no longer depend upon past experience or assumptions in order to make decisions. Data analytics helps in identifying patterns, trends and relationships that are not easily able to detect manually. 

All these insights help the decision makers in understanding the causes behind the blunders and also what actions should be taken next to reduce them. By using analytics, decision makers can easily evaluate different options and reduce uncertainty, which leads to decisions that are more accurate and consistent.

5.3) Building sustainable growth through predictive analytics

Data analytics helps in predictive analysis as well as future-oriented planning, meaning prepare for the future instead of looking at what has already happened in the past. 

Earlier many businesses used to make decisions after problems, but now with the help of data analytics the organisations can easily detect the problem and also predict what is about to happen next and take the measurable actions for them. 

After analyzing historic data and identifying patterns, analytics help in forecasting trends and future outcomes such as customer behaviour, operational requirements etc. 

When the planning is based on data it becomes more accurate and less risky. Analytics supports early identification of potential risks and allows organisations to prepare for the preventive measures in advance. 

With this, planning becomes more structured and informed. As a result, predictive analysis makes planning more structured and informed. The decisions are based more on future expectations rather than unnecessary guesses.

5.4) Building intelligent and scalable technologies with data analytics

Data analytics support innovations and technology adoption. India is emerging rapidly as a global hub for artificial intelligence, machine learning, startups and IT services. These technologies depend upon large volumes of data to function effectively. Data analytics provide the foundation needed to collect, process and analyse this data. 

Without analytics, technologies like AI, Machine learning  cannot improve or deliver accurate outcomes. Data analytics helps the organisations build intelligent systems, and develop scalable technology solutions to strengthen India’s position in the global technology ecosystem.

6. What Data Analytics Companies In India Actually Do?

6.1) From Data Collection to Clean, Analysis-Ready Intelligence

The data analytics companies in India help in cleaning, collecting and organising data. These companies make sure that the business data is complete, correct and well-organised before doing any analysis. 

They collect data for multiple sources like customer databases, websites, mobile apps, finance systems etc. Then they clean the data by removing errors. These companies then connect data from different departments to give a complete view of business, not only isolate numbers. 

Data is then stored in a proper format so that it can be easily analysed, reported and reused in future. Clean data ensures that reports and predictions are accurate and trustworthy.

6.2) Making business performance visible through dashboards and reports

The data analytics company in India helps in business intelligence as well as reporting. They turn data into clear dashboards and reports in order to help business leaders understand the flow of business. 

They help in creating charts, graphs, dashboards to show important metrics such as revenue, sales growth, product performance etc. In order to track performance and spot issues, reports can be updated daily or weekly. 

They also help in faster and better decision making. These companies use the BI tools to get the complete overview of business rather than the unnecessary  scattered information.

6.3) Predictive Analytics & Forecasting for Future-Ready Decisions

These Indian based analytics firms help in predictive analytics and forecasting that means they use past as well as the current data to predict the future possibilities. By analyzing historical sales data, market patterns and much more, they predict the future sales. 

The machine learning models help in identifying the customers who are likely to stop using a product or service,and allow them to take early actions to retain them. As more data is added, the prediction models become more accurate.

6.4) Big Data Processing and Management at Scale

These companies help in big data processing and management that means they can handle large amounts of data that are complex to handle. They help in managing extremely large datasets that get difficult to be  stored or handled on a single computer. Instead of storing data at one place, they use various distributed systems. 

Data gets broken into smaller pieces and gets stored across various nodes or machines. This helps in increasing reliability that means even if one node fails, the data still remains safe. 

They are dependent upon parallel computation for processing. In this, large datasets get divided into smaller pieces, and each piece gets processed at the same time across multiple systems. This results in reducing the processing time compared to running everything on a same system at the same time.

6.5) Data Visualization and Dashboards for Clear Understanding

These companies use dashboards and data visualisation tools that help to present the data in a much easier way, that helps in turning the complex data into much easier and easy to understand visuals for decision-making.

Rather than working with raw tables or spreadsheets, they present the data using charts, graphs, maps etc. These visuals help in highlighting the patterns that are a bit difficult to see in plain numbers. Visuals make complex data simple and clear, even for people who don’t have much technical knowledge. 

Dashboards bring different data points from multiple systems, at one place for viewing. Visualisation helps people interpret data accurately in order to avoid confusions caused by large datasets.

7. How to Select the Best Company in India for Data Analytics Services?

7.1) Business Goal and ROI focus

The company should understand your needs as well as business objectives. The company must deliver the analytics that help the clients to improve the revenue, efficiency and decision making, and not only provide reports.

7.2) Strong analytics, AI and Technical Expertise

The company should have deep expertise in data analytics, machine learning and artificial intelligence. These skills allow a company to go beyond basic reporting and deliver high-value business intelligence. 

These skills help to deliver accurate insights that means using tools to analyze large volumes of data to uncover hidden patterns and trends, automation and efficiency, competitive advantage etc.

7.3) Data Security and Compliance

Strong data protection is important to ensure business continuity, customer trust and legal compliance. Data analytics partners help in the protection of sensitive business data, they have a large amount of confidential information such as customer records, financial data etc.

These companies also help in risk reduction as they have strong security frameworks that help to reduce the risks of cyberattacks, data breaching and system failure.

7.4) Clear communication and strategic partnership

A good data analytics company does not just give simple reports. They help to explain the data in simpler language so that business leaders can easily understand and take legal actions. 

These companies help to give simple explanations of insights that means instead  of technical terms, they clearly state if the data is appropriate for sales, costs, customers and growth. They also provide you regular and clear communication to help avoid confusions and delays along with action-oriented guidance.

7.5) Proven Track Record and Case Studies

The work history of the company shows whether it can actually deliver results or not. Case studies show how the company handled real challenges like in increasing sales, reducing costs etc. 

The company must have evidence of practical experiences which is basically a proven track record that states the company has worked with real data, real systems and real deadlines. When a company has already succeeded with similar projects, the chances of failure of your business becomes the least.

8. Conclusion

The data analytics ecosystem in India is still growing at a rapid pace with more enterprises needing to find the structured analytics support, prediction systems, and performance measurement tools. In the above article, I have listed some reliable companies in India that provide data analytics services like data engineering, predictive modeling, reporting structures, consulting etc.

They have prior experience in working with small & medium size (SMB) and large scale enterprises in solving their business problems using the help of advanced technology stack. You can check these analytics companies capabilities, pricing structure, data engineer experience & expertise and take a final call while selecting the best data analytics partner in India for your business.

9. FAQ’s

1. What is a Data Analytics and AI consulting company?

A data analytics and AI consulting company helps the organisations collect, process, analyse and interpret data using data analytics, machine learning and AI techniques to generate meaningful insights and intelligent solutions. A Data analytics and AI consulting company helps the organisations in managing and using their data effectively.

It helps collect the data from multiple sources, clean it, organise it, and prepare it for analysis. Using data analytics techniques, the company identifies patterns, trends, and relationships within the data. Machine learning and artificial intelligence are then applied to build models that can predict outcomes, and improve accuracy over time.

2. What criteria are used to identify top data analytics companies in India?

Top Data Analytics companies are evaluated on the basis of-

  • Core expertise in analytics and AI
  • Proven track record and case studies
  • Industry and domain experience
  • Scalable technology stack
  • Data security and compliance practices.

3. What technologies do leading Data Analytics companies use?

Most leading data analytics companies use technologies like-

  • Programming languages like Python, SQL, R
  • Analytics tools like Power BI, Tableau
  • Big Data Framework like Hadoop, Spark
  • Cloud platforms such as AWS, Azure, GCP

4. How do data analytics companies ensure data security?

All the data analytics companies follow governance practices like-

  • Secure data storage and access control
  • Encryption and monitoring
  • Compliance with data protection regulations
  • Role based access management
  • Regular security audits and testing
  • Data backup and recovery mechanisms
Databricks Lakehouse: Next Level of Data Brilliance

Databricks Lakehouse is the new architecture used for data management which merges the best parts from Data Warehouse with the best parts from Data Lake. It combines ACID transactions and data governance of DWH with flexibility and cost efficiency of Data Lake to enable BI and ML on all data. It keeps our data in massively scalable cloud object storage in open-source data Standards. Lakehouse radically simplifies the enterprise data infrastructure and accelerates innovation in an age when ML and AI are used to disrupt any industry. The data Lakehouse replaces the current dependency on data lakes and data warehouses for modern data companies that require.

  1. Open, Direct access to data stored in standard data formats.
  2. Low query latency and high reliability for BI and Analytics.
  3. Indexing protocols optimized for ML and Data Science.
  4. Single source of truth, eliminate redundant costs, and ensure data freshness.
Fig. A simple flow of data through Lakehouse

Components of Lakehouse

1. Delta Table

With the help of Delta tables, we can enable downstream data scientists, analysts and ML engineers to leverage the same production data which is used in current ETL workloads as soon as it is processed. Delta table takes care of ACID transactions, Data Versioning and ETL. Metadata used to reference the table is added to Meta store in declared schema.

2. Unity Catalog

It ensures that we have complete control over who gains access to which data and provides a centralized mechanism for managing access control without needing to replicate data. Unity Catalog provides administrators with a unified location to assign permissions for catalogs, databases, table and views to group of users.

Delta Lake

Delta lake is a file based, open-source storage format that provides ACID transactions and scalable metadata handling, unifies streaming and batch data processing. It runs on top of existing data lakes. Delta lake integrates with all major analytics tools.

  Fig. Lakehouse with Databricks

Medallion Lakehouse Architecture – Delta Design pattern

The Medallion architecture describes the series of data layers that denote the quality of data stored in Lakehouse. The term Bronze, Silver and Gold describe the quality of data in each of these layers. We can make multiple transformations and apply business rules while processing data through the different layers. This multilayered approach helps to build a single source of truth for enterprise data products.

  1. Bronze – Raw data ingestion.
  2. Silver – Validated, Filtered data.
  3. Gold – Enriched data, Business level aggregates.

Data Objects in Databricks Lakehouse

The Data Bricks Lakehouse organizes data stored with Delta Lake in cloud object storage with familiar relations like database, tables and views. There are Five primary objects in Databricks Lakehouse.

  • Catalog
  • Database
  • Table
  • View
  • Function

Lakehouse Platform Workloads

  • Data Warehousing
  • Data Engineering
  • Data Streaming
  • Data Science & Machine Learning

Pros

  • Adds Reliability, performance, governance and quality to existing data lakes.
  • ACID Transactions
  • Handling large metadata
  • Unified data teams
  • Reducing the risk of vendor lock-in
  • Storage is decoupled from Compute.
  • ML and Analytics support

Cons

  • Complex setup & Maintenance – The platform can be complex to set up and maintain, requiring specialized skills and resources.
  • Its advanced capabilities may not be suitable for some lower functionalities use cases.

The purpose of this post is to provide a broad overview of Databricks Lakehouse. Please get in touch with us if our content piques your interest.

Data Modeling in Power BI: Build a Strong Analytics Foundation

Introduction:

In today’s data-driven world, Power BI has emerged as a leading tool for transforming raw data into actionable insights. However, the true power of Power BI lies not only in its visualization capabilities but also in its robust data modeling features. Effective data modeling lays the groundwork for meaningful analyses and impactful visualizations. In this blog, we will delve into the nuances of mastering data modeling in Power BI, exploring essential concepts and best practices to construct a solid foundation that maximizes the potential of your data.

Understanding Data Modeling:

At its essence, data modeling in Power BI involves structuring data to facilitate analysis and visualization. This begins with importing data from diverse sources, such as databases or spreadsheets, into Power BI Desktop. Subsequently, relationships between different tables are established, calculated columns and measures are created, and the data model is optimized for performance.

1. Establishing Relationships:

Relationships dictate how tables in the data model are connected. In Power BI, relationships are based on shared fields or keys between tables. By defining relationships, Power BI can perform complex cross-table calculations and aggregations. Understanding the various relationship types (e.g., one-to-one, one-to-many, many-to-many) and selecting the appropriate cardinality and cross-filter direction is crucial based on the data structure and analysis requirements.

                                        Relationship between tables (Source: learn.microsoft.com)

2.Creating Calculated Columns and Measures:

Calculated columns and measures serve as integral elements within the realm of data modeling. Calculated columns allow for the generation of new columns through the application of calculations to existing data, while measures dynamically aggregate data based on predefined conditions. Leveraging DAX (Data Analysis Expressions), the proprietary formula language of Power BI, provides a wide array of functionalities including summation, averaging, and counting. It’s imperative to prioritize simplicity, efficiency, and reusability when crafting calculated columns and measures to uphold maintainability and optimize performance.

   Calculated Column

3.Optimizing Performance:

Performance optimization is paramount in data modeling, particularly with large datasets or intricate calculations. Employing techniques such as minimizing calculated columns, utilizing appropriate data types, avoiding unnecessary relationships, and optimizing DAX formulas can significantly enhance report responsiveness and efficiency. Additionally, features like query folding, partitioning, and incremental data refresh can further optimize performance.

Best Practices and Tips:

To master data modeling in Power BI effectively, adhere to these best practices and tips:

– Thorough Understanding: Gain a comprehensive understanding of data sources, relationships, and business requirements before constructing the data model.

– Simplicity: Strive for simplicity by minimizing complexity and redundancy in the data model. Simplified models are easier to maintain and troubleshoot.

– Descriptive Naming: Use clear and descriptive names for tables, columns, relationships, and measures to enhance clarity and comprehension.

– Testing and Iteration: Rigorously test the data model with sample data and iterate based on feedback and performance evaluations.

– Stay Updated: Keep abreast of the latest Power BI features and updates to leverage new functionalities and optimizations.

Conclusion:

Developing proficiency in data modeling within Power BI is crucial for establishing a solid foundation conducive to in-depth analysis and engaging visualizations. By grasping fundamental concepts, embracing best practices, and employing optimization strategies elucidated in this guide, users can fully harness the capabilities of Power BI. Whether you’re new to the platform or a seasoned user, dedicating time and effort to mastering data modeling will undoubtedly yield significant benefits, enabling you to make informed decisions and propel business success through data-driven insights.

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Azure Databricks Overview: Big Data & AI Analytics Platform

What is Azure Databricks?

Azure Databricks is Apache spark based big data and analytics platform optimized for Azure cloud services. Databricks includes an interactive notebook environment, monitoring tools and security controls that make it easy to leverage Spark. Azure Databricks supports multiple languages such as Scala, Python, R and SQL. Along with these it supports multiple API’s. Azure Databricks offer three environments for developing data intensive applications:

  1. Databricks SQL
  2. Databricks Data Science & Engineering
  3. Databricks Machine Learning

                                                                 Fig.  Azure Environment

Azure Databricks empowers organizations to extract meaningful insights from their data, whether through interactive analysis, batch processing, or machine learning, and is a key component in Microsoft’s Azure analytics and data services ecosystem.

How do Databricks work in Azure?

Azure databricks is optimized for Azure and highly integrated with other azure services like Data Lake Storage, Azure Data Factory and Power BI to store all data in simple open lakehouse. On top of this Azure Databricks integrates seamlessly with Azure Active Directory for access control and authentication. Overall azure databricks provides well architected and tightly integrated environment for big data analytics and machine learning on Azure.

Components of Azure Databricks:

The key components of the Azure Databricks platform include:

  1. Workspace
  2. Notebooks
  3. Clusters
  4. Workflows
  5. Delta Lake
  6. Auto Loader

Workspace: Azure Databricks Workspace is an integrated development environment (IDE) provided by Microsoft Azure for data engineering, data science, and machine learning tasks. It’s a collaborative platform that allows multiple users to work together on big data analytics. We can write code and configure jobs using workspace.

                                                                Fig. Azure workspace

Notebooks:  Azure Databricks provides a notebook interface where users can write and execute code in multiple languages, such as Python, Scala, SQL, and R. Notebooks are interactive documents that combine live code, visualizations, and narrative text, making it easy to explore, analyze, and visualize data. Any type of business logic we can write and apply on data using notebooks.

                                                              Fig. Sample Notebook

Clusters: A databricks cluster is a set of computation resources and configurations on which you run data engineering, data science and data analytics workloads. These workloads such as ETL pipelines, streaming analytics, ad hoc analytics are run as a set of commands in notebook or as a Job. There are primarily two types of clusters, All-purpose clusters and Job clusters. All-purpose clusters analyze data collaboratively using interactive notebooks, while job clusters run automated jobs in an expeditious and robust way. It’s better to use All-purpose clusters for ad hoc requests and development work. Cluster’s usually takes 3 to 6 minutes to start, and we can stop it manually or it is auto terminated after certain set limit. Also, there is SQL warehouse compute available for ad hoc SQL queries which takes relatively less time to start.

                                                                     Fig. Cluster’s

Delta Lake: Delta Lake is the technology at the heart of Azure Databricks platform. It is open-source technology that enables building a data Lakehouse on top of existing storage systems. Delta Lake builds upon standard data formats, it is primarily powered by data stored in the parquet format, one of the most popular open-source formats for working with big data. Additionally, Delta Lake is default for all tables created in Azure Databricks.

Data Bricks Auto Loader:  Auto Loader provides an easy-to-use mechanism for incrementally and efficiently processing new data files as they arrive in cloud file storage. This optimized solution provides a way for data teams to load raw data from cloud object stores at lower cost and latency. By using Auto loader no tuning or manual code required. Auto loader can load files from ADLS Gen2, Azure Blob Storage and Data Bricks File System. Auto loader can be very useful and efficient when used with Delta Live Tables.

Workflows: A workflow is a way to run non interactive code in databricks clusters. For example, you can run ETL workload interactively or on a schedule. A workflow can consist of a single task or can be a large, multitask workflow with complex dependencies. Azure Databricks manages the task orchestration, cluster management, monitoring and error reporting for all the jobs. We can run jobs immediately or periodically through an easy-to-use scheduling system. Also, we can set dependency on upstream job by using file arrival trigger in workflow.

                                              Fig. Workflow Schedule and Triggers

Summary: Azure Databricks can be very useful and game changer in today’s modern big data analysis due to its optimized environment, Persistent collaboration in notebooks, real time team-work and user-friendly workspace.  Also, azure databricks integrates closely with PowerBI for hand-on visualization, this can be very effective for ad hoc analysis.

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Getting Started with Pentaho Data Integration

Introduction:

Pentaho Data Integration (PDI) stands as a cornerstone in the realm of data integration and analytics. Whether you’re a seasoned data professional or a newcomer to the field, this guide will navigate you through the crucial initial steps in leveraging Pentaho Data Integration for your ETL (Extract, Transform, Load) needs. Unveiling Pentaho Data Integration (PDI)

Introduction to PDI:

Pentaho Data Integration, often referred to as Kettle, serves as the data integration powerhouse within the Pentaho Business Analytics suite. Renowned for its user-friendly graphical interface, PDI empowers users to craft intricate ETL processes without delving into intricate coding. Supporting an extensive array of data sources, PDI emerges as a versatile solution for diverse data integration challenges.

Installation and Configuration:

Step 1: Acquiring Pentaho Data Integration

Initiate your journey by downloading the latest version of Pentaho Data Integration from the official website

Step 2: Installation Guidance

Click on “Download Now” on the official website and choose the version you want to install. Typically, we opt for the one labeled “Pentaho Data Integration (Base Install).” Navigating the Pentaho Data Integration Interface

Crafting Your Inaugural ETL Job:

Step 1: Initiating a Transformation

Within Spoon, create a new transformation—a set of interconnected steps defining your ETL process. Introduce source and destination steps to depict the data flow.

Step 2: Step Configuration

Configure the source step to establish connectivity with your data source, whether it’s a database, CSV file, or another format. Simultaneously, configure the destination step to specify where your transformed data will be loaded.

Step 3: Exploration of Transformation Steps

Delve into the diverse transformation steps PDI offers. For beginners, commence with fundamental steps such as Select Values, Filter Rows, and Add Constants to manipulate your data effectively

Step 4: Transformation Execution

Execute your transformation to witness the ETL process in action. Monitor the log window for any potential errors or warnings during the execution.

Preservation and Reusability of Transformations:

Step 1: Save Your Transformation:
Once content with your transformation, save your work. This preserves your efforts and facilitates future modifications.

Step 2: Transformation Reusability:
PDI advocates for the reuse of transformations across different jobs, fostering a modular and efficient approach to ETL design. This approach proves invaluable in saving time and effort when encountering similar data integration tasks.

Conclusion:

Embarking on your Pentaho Data Integration journey unveils a realm of possibilities in the ETL landscape. This guide has initiated you into crafting ETL processes with PDI’s intuitive graphical interface. As you grow more accustomed to the tool, explore advanced features such as job orchestration, scripting, and integration with big data technologies.  Always remember, proficiency in Pentaho Data Integration is cultivated through practice. Begin with uncomplicated transformations and progress towards more intricate scenarios. The Pentaho community and documentation serve as indispensable resources for ongoing learning and troubleshooting. Happy ETL endeavors!

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An overview on Azure’s NoSQL Cosmos DB

Azure Cosmos DB is a fully managed platform-as-a-service (PaaS). Offers NoSQL and relational database to build low-latency and high available applications with support to multiple data stores like relational, document, vector, key-value, graph, and table.  Azure Cosmos DB offers single-digit millisecond response times, high scalability. Guaranteed SLA-backed availability and enterprise-grade security.

Global distribution: Cosmos DB is a globally distributed database that allows users to read or write from multiple regions across the world. Helps to build low latency, high availability applications. Cosmos DB replicates the data across the globe with guaranteed consistency levels. Azure Cosmos DB offers 99.999% read and write availability for multi-region databases.

Consistency levels: Azure cosmos DB supports 5 different consistent levels.

  • Strong: Linearizable reads.
  • Bounded staleness: Consistent Prefix. Reads lag behind writes by k prefixes or t interval.
  • Session: Consistent Prefix. Monotonic reads, monotonic writes, read-your-writes, write-follows-reads.
  • Consistent prefix: Updates returned are some prefixes of all the updates, with no gaps.
  • Eventual: Eventual

Cosmos DB resource hierarchy:

A Cosmos DB account can hold multiple databases. A Database can hold multiple containers.

Data is stored in containers. Each container contains a partition key. Partition key helps to distribute the data across all partitions equally. Partition key should be selected cautiously because choosing a wrong partition key will increase the consumption of RUs. The easiest way to determine the partition key is the field that will be used on your WHERE clause. Data is stored in physical partitions; Cosmos DB abstracts the physical partitions into logical partitions. If a container contains 10 distinct partition values, 10 logical partitions are created. Each physical partition is replicated at least 4 times to increase availability and durability.

Containers are schema-agnostic which means items in containers can be of different schema but with same partition key. All items are indexed automatically, a custom index policy is also available.

Pricing: Azure cosmos DB calculates all the database operations in Request Units (RU’s) irrespective of the API. One request unit equals to 1KB of item read using a partition key and ID value.

There are three modes we can use to setup the cosmos DB.

  • Provisioned Throughput: A fixed number of RUs per second is assigned to the Cosmos DB based on the requirement.
  • Serverless: No assignment needed, billed based on the consumption. Serverless mode comes with some limitations like single region only, can store maximum of 1TB, RUs ranges between 5000-20000.
  • Auto scale: Auto scales based on the consumption. Suitable for building scalable high available applications with unpredictable traffic. No need to handle rate limiting operations.

Cosmos DB emulator: Cosmos DB also offers an emulator that can be installed on your local system. Emulator comes with limited features and can be used for developing and testing applications locally without creating an actual cloud account.  Fixed RU’s, fixed consistency levels and supporting only NoSQL API are few on the limited features.

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Optimizing Power BI Performance: Unleashing the Full Potential of Your Reports

Power BI stands as a robust tool for transforming raw data into actionable insights. However, as reports and dashboards become more intricate, optimizing performance becomes paramount. Slow-loading reports and sluggish interactions can hinder user experience, diminishing the impact of your data-driven decisions. In this post, we will explore key strategies to optimize Power BI performance, ensuring a seamless and responsive user experience.

1.Streamlined Data Modeling

The foundation of every Power BI report is its data model. A well-designed data model not only improves report performance but also enhances overall usability. Here are some tips for efficient data modeling:

  • Simplify Relationships:

Ensure that relationships between tables are necessary and optimized. Remove unnecessary relationships and use bi-directional filtering judiciously.

  • Optimal Data Types:

Choose the appropriate data types for your columns to minimize storage requirements and enhance query performance. Avoid unnecessary data conversions.

  • Leverage Aggregations and Summary Tables:

Pre-calculate and store summarized data using aggregations and summary tables. This reduces the load on the system during report rendering.

2.Power Query Optimization

Power Query is a potent tool for data transformation, but inefficient queries can slow down the entire data refresh process. Consider these optimizations:

  • Early Data Filtering:

Apply filters as early as possible in Power Query transformations to reduce the data loaded into Power BI.

  • Column Limitation:

Import only the columns you need. Eliminate unnecessary columns during the Power Query stage to minimize the data transferred to Power BI.

  • Harness Query Folding:

Utilize query folding to push some transformations back to the data source, reducing the amount of data brought into Power BI for processing.

Screenshot of the Power Query Editor’s optimization steps show the outcomes of each stage when you pick it.

3.Effective Visualization Techniques

Visualizations are the face of your reports, and optimizing them is crucial for a responsive user experience:

  • Limit Visual Elements:

Avoid cluttering your report with too many visuals. Each visual adds to the load time, so prioritize key insights and remove non-essential elements.

  • Aggregations in Visuals:

Aggregate data within visuals instead of relying on Power BI to aggregate large datasets, significantly improving rendering speed.

  • Optimize Maps:

If using maps, limit the number of data points displayed, and consider using aggregated data for better performance.

Sales Dashboard using optimized visualizations.

4.Monitor and Optimize DAX Calculations

DAX calculations can be resource-intensive, impacting report performance. Optimize DAX with the following tips:

  • Measure Dependencies:

Review dependencies of your measures and ensure they are calculated only when needed. Avoid unnecessary recalculations.

  • Optimize Time-Intensive Calculations:

Identify and optimize time-consuming DAX calculations, especially those involving large datasets or complex logic.

5.Maintain Security and Governance

Implementing proper security and governance measures contributes to a secure and well-maintained Power BI environment:

  • Role-Level Security (RLS):

Utilize RLS to restrict data access based on user roles, ensuring each user sees only relevant data, thus improving query performance.

  • Regular Review and Clean Up:

Regularly review and clean up your Power BI workspace. Remove unnecessary datasets, reports, and dashboards to streamline the environment.

Conclusion

Optimizing Power BI performance is an ongoing process that involves efficient data modeling, optimized queries, effective visualization techniques, and careful monitoring of DAX calculations. By implementing these best practices, you can unlock the full potential of Power BI, providing users with fast, responsive, and impactful reports and dashboards. A well-optimized Power BI environment is the key to turning data into insights that drive informed decision-making.

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Extended Team Model: An Alternative To Outsourcing

Most companies and startups struggle with having proper resources onboard and building a  team with the right balance of all skill sets. Outsourcing is often done to get the work done, but without synchronization and the right talent on board, the execution process can be quite challenging! This is where the Extended Team Model can accelerate an in-house team’s progress by complementing it and fostering efficiency.

Imagine having extra hands complementing your team’s skill set and augmenting the development team! That is precisely how an extended team model contributes to an onsite team. In this article, we will understand how an Extended Team Model is the perfect, modernized alternative to Outsourcing and has been befitting multiple software development companies!

What Is An Extended Team Model (ETM) in Analytics?

Businesses are scaling higher these days with effective connections within the team and a network of professionals who can maximize a team’s productivity. The Extended Team Model is an alternative to Outsourcing, where the in-house team extends to a virtual team of professionals who group up with the core team to exercise the skill sets that are lacking in the core team.

If your company is rooting for growth on a larger scale on a long-term basis, then having an extended team model is your solution to scale higher. With an extended team model, there is more transparency and flexibility as the team members are in constant communication, working as one team with a fixed goal. Your focus also becomes the extended team’s focus, fostering highly effective collaboration between the in-house and extended team.

How is an Extended Team Model different from Outsourcing?

One might ask how an extended team model differs from outsourcing since the job gets done both ways. The answer is that the quality of the final delivery is always better when a team comes together and works in collaboration, which is the crux of the extended team model concept. In outsourcing, the company does not have the option to directly communicate with the developers or gain an insight into the workings; they simply get the code or product delivered.

The extended Team Model bridges this gap between the in-house and offshore outsourcing teams, making the execution process more efficient and collaborative.

Let’s look at the features of an Extended Team Model to understand better how it is a better alternative to Outsourcing and a more positive approach:

  1. Complement the core team:
  1. An Extended Team Model is meant to complement the core team, not replace it. If your local talent pool lacks specific technical skillsets or business expertise, the extended team model steps in to bridge that gap. The core team works onsite, while the Extended Team Model might be operating offshore.
  2. Sharing the same work culture
  1. An Extended Team is hired when a company wants to grow on a long-term basis and wishes to work with the extended team for future projects. This means that the extended and in-house teams can share the same focus, and the extended team will not get sidetracked by any other project. The Extended Team will receive the same objectives and training (depending on their expertise) and share the same work culture with the core team. This develops a strong team spirit and oneness between the inhouse and Extended teams, naturally improving the quality of the final product.
  2. You have control over the project
  1. In outsourcing, the requirements are shared with the offshore team, and you don’t have a say in their day-to-day productivity or ways of working. In an Extended team Model, a single point of contact and authority overlooks the work for both the core and extended teams.
  2. You have control over the project
  1. One of the significant aspects of hiring an extended team is that you have complete flexibility in adding people with new skill sets or reducing your team to the required professionals as you move to different stages of the delivery process. You can swap out developers who are no longer required for the project or grow your team headcount as you deem fit.
  2. Working towards a common goal
  1. In an Extended Team Model, the responsibilities are shared equally between all team members (both onsite and offshore) depending on their skill set, and everyone is equally responsible for the success or failure of a project. This makes the entire team stay invested in the execution process and share a common goal of delivering a quality product.
  2. Easy hiring process
  1. Once you provide your requirements to the Extended Team Provider, they perform an initial screening and provide professionals most suited for your project. You can have your own screening and interview process for them before making the final call regarding who you want in your Extended Team Model. Moreover, finding the right match for a missing skill set in your local talent pool can be costly and time-consuming. By hiring an Extended Team Model, you can access top techies and developers across the globe and narrow down on developers who are best suited for your project in a cost-friendly manner. Now, the most crucial aspect is understanding at what point you should consider hiring an Extended Team for your project. Read on to find out!

When to opt for an Extended Team?

Suppose you wish to expand your business in the long-term and deliver projects that are currently beyond your company’s scope, but you have the resources to make it happen. In that case, this is the perfect opportunity to hire an extended team to grow your business significantly.

An extended team will help you enrich your in-house team for long-term projects and augment the development process by bringing in skill sets that your local talent lacks.

Moreover, it is often costly to hire skillsets from a local pool, however, if you hire an extended team, the charges are much more reasonable, and the Extended Team provider will allocate all the required resources such as computers and workspace.

To conclude, scaling a business involves considering options that will help your business expand exponentially and accrue potential clients. Achieving this requires resources that will benefit you and your team. Hence, an Extended Team Model is the best way to deliver value while essentially adding value to your team and resources.

Transforming data using DBT (Data Build Tool)

Software tool that allows us to transform and model data in the data warehouse.

DBT supports ELT (Extract, Load, Transform) process. Data is extracted, loaded into a data warehouse, and then transformed using DBT.

Shift from ETL to ELT has increased the popularity of DBT.

How DBT Differs from Other ETL Tools:

While traditional ETL tools focus on moving and transforming data before it reaches the warehouse, DBT operates directly within the Datawarehouse.

    Capabilities of DBT:

  • Performance: By transforming data directly in the warehouse, the computational power of modern data warehouses (Snowflake, Big Query, and Redshift) is enhanced.
  • Version Control: DBT uses SQL and Jinja2, which helps in version control. This ensures transparency and traceability of changes made to data models.
  • Data Modeling: DBT offers a strong framework for creating and maintaining data models. SQL-based reusable models that are simple to maintain.
  • Testing and Documentation: Data transformations can be automatically tested with DBT, thus ensuring accuracy and integrity. It automatically creates documentation, offering insight into the data transformation procedure.
  • Workflow management and collaboration: DBT makes it possible for team members to work on the same project at the same time, which promotes cooperation. By integrating with version control systems, it facilitates an organized change and release workflow.

    Transformation flow in DBT:

  • DBT has native integration with cloud Data Warehouse platforms.
  • Development: Write data transforming code in .sql and .py files.
  • Testing and documentation: It is possible to perform local tests on all models before submitting them to the production repository.

  • Deploy: Deploy code in various environment. Version control enabled by Git allows for collaboration.

Versions of DBT:

There are two versions of DBT:

  • DBT Core: It is an open-source command-line tool that allows for local data transformation.
  • DBT Cloud: It is a web interface that enables fast and reliable implementation of DBT. Through this interface, it is possible to develop, test, schedule, and visualize models.

Core components of DBT:

  • Models: SQL queries that define data transformations.
  • Tests: Ensure data quality by validating models.

DBT supports both built-in tests (like unique or not null) and custom tests defined in SQL.

  • Snapshots: Track historical changes in data.
  • Documentation: Auto-generates documentation for clarity on data processes.
  • Macros: Reusable SQL code snippets.
  • Data: This directory is used to store CSV files or other data sources used for testing or development purposes.

Basic commands in DBT:

dbt init: Initializes a new dbt project.

dbt debug: Runs a dry-run of a dbt command without actually executing the command.

dbt compile: Compiles the SQL in your dbt project, generating the final SQL code that will be executed against your data warehouse.

dbt run: Executes the compiled SQL in your data warehouse.

dbt test: Runs tests defined in your dbt project, checking for errors or inconsistencies in your data.

dbt deps: Installs dependencies for your dbt project.

dbt docs generate: Generates documentation for your dbt project.

dbt docs serve: Serves the documentation generated by dbt docs generate on a local server.

dbt seed: Seeds your data warehouse with initial data.

dbt snapshot: Takes a snapshot of your data warehouse, capturing the current state of your data.

dbt snapshot-freshness: Checks the freshness of your snapshots and generates a report indicating which snapshots need to be refreshed.

dbt run operation: Runs a custom operation defined in your dbt project.‍

 

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Power BI’s Role in Data Storytelling

In the ever-evolving realm of business intelligence, the ability to weave a compelling narrative through data sets successful professionals apart. Enter Power BI, Microsoft’s formidable analytics tool, offering a canvas for crafting narratives through dynamic visualizations. In this blog, we delve into the core of data storytelling and how Power BI serves as your artistic tool in this creative endeavor.

The Power of Data Storytelling

While data alone may seem dry and intricate, when skilfully woven into a narrative, it transforms into a potent decision-making tool. Data storytelling is the art of translating raw numbers into a coherent and engaging tale that resonates with your audience. It transcends mere charts and graphs, aiming to make data relatable and easily understandable.

   Raw Data

Visually compelling data story

Unleashing the Potential of Power BI

Power BI stands as an ideal platform for sculpting data stories, boasting an intuitive interface and robust visualization capabilities. With an array of chart types, maps, and customizable dashboards, Power BI empowers users to create compelling narratives that drive insights and actionable outcomes.

Steps to Craft a Data Story in Power BI

  1. Define Your Audience:

Recognize your audience and tailor your story to their specific requirements and interests.

  1. Identify Key Insights:

Before visualizing, pinpoint the crucial insights your data can offer. Determine the story you want to tell and the answers your audience needs.

  1. Choose the Right Visualizations:

Select visualizations that enhance your story and effectively convey your data. Power BI offers a vast collection of graphs, charts, and maps to bring your narrative to life.

  1. Create a Logical Flow:

Organize your visualizations in a logical order to guide readers through the information effortlessly.

  1. Add Context and Commentary:

Enhance your visualizations with context through annotations and commentary. Explain the significance of each data point, highlighting trends that contribute to the overarching narrative.

  1. Use Interactivity to Engage:

Leverage Power BI’s interactive features to allow users to explore the data themselves, fostering engagement and a deeper understanding of the story.

Real-world Examples

1.Sales Performance Dashboard:

Showcase a sales team’s achievements through a Power BI dashboard, highlighting fluctuations, successful strategies, and the impact of market dynamics.

     Sales Performance Dashboard

2.Operational Efficiency Story:

Illustrate how process improvements increased efficiency using Power BI visuals, demonstrating cause-and-effect relationships through clear data representation.

Operational Efficiency Dashboard

Conclusion

Mastering the craft of data storytelling is imperative in the era of data-driven decision-making. Power BI serves as a creative tool, enabling individuals and organizations to communicate complex information in an engaging and approachable manner. Each chart, annotation, and color choice contributes to the narrative canvas. Utilize Power BI to unleash the creative potential of your data, telling a compelling tale that inspires action and fosters success.

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Snowflake’s Unparalleled Cloud Data Warehouse Features


Snowflake is a cloud data warehousing solution that offers several unique features that give it an edge over other data warehouse solutions. Here are six of the distinctive features of Snowflake:

1. Major Cloud Platform Support: Snowflake is a cloud-agnostic solution that is available on all three major cloud providers: AWS, Azure, and GCP.

    • All major functionalities and features are available across the cloud providers.
    • This enables support for multiple cloud regions and organizations can host the instances based on their business requirements.
    • Pricing depends not on the cloud provider but on the snowflake edition that you are planning for your data platform.
    • You only pay for what you store and running compute. When compute is not used, you are not charged anything for compute.

2.  Scalability: Snowflake is natively built using cloud technologies. Hence, it takes advantage of very high scalability, elasticity, & redundancy features. You can store more data and scale up or down your computing resources as needed.

    • Snowflake has implemented auto-scaling and auto-suspend features.
    • Auto-scaling feature enables Snowflake to automatically start and stop resource clusters during unpredictable load processing.
    • Auto-suspend feature stops the virtual warehouse when resource clusters have been sitting idle for a defined.

3.Near Zero Administration: Snowflake is a true SaaS offering with No hardware (virtual or physical) to select, install, configure, or manage.

    • Snowflake handles Ongoing maintenance, management, upgrades, and tuning.
    • Companies can set up and manage their database solution without any significant involvement from DBA teams.
    • Storage, compute, cloud service, and data transfer monitoring and alerts (via notification & hard stop) are provided by Snowflake so compute credits can be managed by businesses very easily.

4. Support for Semi-Structured Data: Snowflake allows the storage of structured and semi- structured data.

    • Snowflake supports reading and loading of CSV, JSON, Parquet, AVRO, ORC, and XML files.
    • Snowflake can store semi-structured data with the help of a schema on read data type called VARIANT.
    • As data gets loaded, Snowflake parses the data, extracts the attributes, and stores it in a columnar format.
    • Snowflake supports ANSI SQL plus Extended SQL. You can query data using simple SQL statements. Snowflake extended SQL is very feature-rich and adds many useful libraries to help you become more productive.

 VARIANT datatype to store Semi-Structured Data

5.Time Travel and Fail Safe: As part of a continuous data protection lifecycle, snowflake allows you to access historical data (table, schema, or database) at any point within the defined retention period.

    • Time Travel allows Querying, cloning, and restoring historical data in tables, schemas, and databases based on the retention period. This retention period is adjustable between 0 to 90 days based on the Snowflake edition.
    • This feature can help in restoring data objects that might have been accidentally deleted or for duplicating or backing up data from key points in the past.
    • Fail Safe is a data recovery service that can be utilized after all other options have been exhausted.
    • It provides a 7-day time window during which Snowflake can retrieve prior data. This time begins after the Time Travel retention period expires.
    • Both these features require additional data storage and hence incur additional storage costs as well.

6.Continuous Data Loading: Snowflake has a Serverless component called Snow pipe, which can be integrated with external object storage like S3 or Azure Blob.

    • It facilitates rapid and automated data ingestion into Snowflake tables. It allows for immediate loading of data from files as soon as they become available.
    • It doesn’t require manual specification of a warehouse because Snowflake automatically provides the necessary resources for its execution.
    • Once set up, a Snow pipe automatically reads files that arrive in the source location and loads them into target tables without any manual execution or predefined schedule.
    • Snow pipe closely works with the other 2 objects called stream and task and these objects capture the data changes and their combination can help build micro-batch or CDC solutions.
Loading Files from Amazon S3 to Snowflake using Snow pipe

These are the few major distinguishing features of Snowflake Cloud Data Warehouse. Snowflake offers many other features that have made it a go-to Cloud Data Warehouse solution for countless enterprises.

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Workflows: A full-fledged orchestration tool in Databricks platform

Introduction:

Databricks workflows is a component in Databricks platform to orchestrate the data processing, analytics, machine learning and AI applications. Workflows have evolved so much that we don’t require a third-party external orchestration tool. Depending on external tools for orchestration adds complexity in managing and monitoring capabilities.

Basic features like scheduling, managing dependency, git integration to advance level capabilities like retires, duration threshold, repair and conditional tasks are available in Databricks workflows.

A task is a unit of execution in the workflow. Workflow supports a wide range of tasks like notebooks, SQL and Spark scripts, Delta Live Tables pipelines, Databricks SQL queries, and dbt jobs. As it supports all varieties of tasks, a workflow can be sufficient to orchestrate an end-to-end pipeline for a subject area.

Databricks job:

A job in Databricks workflow is to group multiple tasks into one for better management and reusability. For each job we can set different types and sizes of compute clusters, notifications and triggers based on requirement. Databricks clusters add reliability and scalability to jobs. Databricks jobs automatically generate the lineage providing upstream and downstream tables for that job.

Jobs can be managed from Databricks UI or Databricks REST API. REST API opens a whole set of capabilities to easily integrate with any outside tool.

For example, a data engineering team can create a job for ETL, a data science team can create a job for their ML models and finally, an analytics team can create a dashboard refresh. All these jobs can be tied together into a single parent workflow, reducing complexity and better management.

A company dashboard or report can only be built using data that was processed by different teams in an organization. So, each team’s job is dependent on the preceding jobs. Since all jobs are dependent on one another, we can either set dependence on preceding jobs or schedule jobs at fixed times or set file-based triggers that can be set on external storage services like ADLS.

Notable features:

The Retry Policy, as shown in the picture below, allows you to set the maximum number of retries and a defined interval between the attempts.

Repair job is a very useful feature for developers while testing a job or for production failures. When we repair a job, it doesn’t run from the beginning, it will re-trigger the pipeline from the failed activity. In contrast, the Re-run feature will run the pipeline from the beginning of the task.

Provides a graphical interface matrix view to monitor the workflow at task level.

Databricks workflows are also integrated with Git. Using Databricks REST API we can streamline the deployment process of the workflows by a CI/CD pipeline.

Like any other component in Databricks, workflows also come with access control. There are four types of access levels available.

Databricks workflow integrates with popular tools like Azure Data Factory, Apache Airflow, dbt and Five Tran.

Notifications:

Orchestration tool cannot be complete without notifications/alerts, data bricks workflows provide various types of notifications. Email based notifications which sends email containing information of start time, run duration, status of the job. Other supported integrations are Microsoft Teams, Slack, PagerDuty and a custom webhook.

Control flow:

Control flow mainly contains two functions.

  1. a) Run Job, triggers a task based on preceding task status. Run if dependencies contain 6 different types of options.
  1. b) If/else condition triggers a task based on the job parameters or dynamic values.

To summarize, Databricks workflows evolved as an alternative to the other external orchestration tools. Advanced features and capabilities make it a no-brainer to opt for Databricks workflow over external tools for managing Databricks pipelines.

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Snowflake Database: An Effective Cloud-Native Data Warehousing Platform

What is a Snowflake?

Snowflake is a cloud-based data warehouse that was created by three data warehousing experts in 2012 who formerly worked at Oracle Corporation. The Snowflake data warehouse is a cloud-based Analytical data warehouse that is offered as Software-as-a-service. Snowflake architecture is different from traditional data warehousing technologies like SQL Server, Teradata, Oracle, and cloud data warehouses like AWS Redshift and Google Big Query.

How is Snowflake Special?

Data warehouses typically use either Shared Disk or Shared Nothing architecture. Multiple nodes are used in Shared disk architectures to access data shared on a central storage system, while a portion of the data is stored in each node/cluster in a Shared Nothing architecture. Snowflake combines both architectures and creates a hybrid architecture.

Snowflake employs a centralized storage layer for data persistence that is available to all computing nodes. Snowflake also uses Massively Parallel Processing (MPP) clusters to process queries, with each node storing a fraction of the whole data locally.

Snowflake’s data architecture consists of three layers:

  1. Storage
  2. Compute/Query Processing
  3. Cloud Services

Each layer can scale independently and includes built-in redundancy.

 Fig: Snowflake architecture showing the different layers.

How does it work?

Storage Layer: Snowflake stores data in databases. A database is a logical group of objects consisting primarily of tables and views organized into schemas. Snowflake supports structured relational data in the form of tables using standard SQL data types. Additionally, Snowflake’s variant data type stores semi-structured non-relational data such as JSON, parquet, etc. ANSI standard SQL is used to perform data-related tasks for all datatypes.

Snowflake uses secure cloud storage to maintain data. Snowflake converts the stored data into a compressed format and encrypts it using AES 256 encryption.

Compute Layer: This is the layer where queries are executed using resources provisioned from a cloud provider. Unlike conventional data warehouses, Snowflake creates independent compute clusters called virtual warehouses that can access the same data storage layer without compromising performance.

To create a virtual warehouse, we can simply give it a name and specify a size. Snowflake automatically handles the provisioning and configuration of the underlying computational resources. There is no downtime when scaling up or down a virtual warehouse. Anytime a virtual warehouse is resized, the extra resources are available for use by any subsequent queries. Snowflake’s architecture also enables read/write concurrency without any resource contention. For instance, separate virtual warehouses can be used for loading and querying simultaneously. As all virtual warehouses access the same data storage layer, inserts and updates are immediately available to other warehouses.

Cloud Services Layer: This layer manages the entire system. It authenticates users, secures data, manages sessions, and performs query compilation and optimization. This layer also coordinates the data storage updates and access, to ensure all virtual warehouses can see the latest data instantaneously once a transaction is completed. A vital part of this layer is the metadata store as it enables many features like time travel, zero-copy cloning and data sharing. Snowflake maintains the services layer using resources distributed across multiple zones to ensure high availability.

Connecting to Snowflake is pretty easy using clients such as the JDBC or ODBC drivers. Snowflake also provides a web interface and a command-line client.

What do you need to manage Snowflake?

Most of the criteria that traditional data warehouses use to adjust performance are eliminated by Snowflake. You only need to virtual warehouses, databases, and tables, load data, and run queries. Snowflake handles everything else.

How much does Snowflake cost?

Pricing is based on usage. Just pay for the computational and storage resources that are used. Storage costs are determined by the amount of compressed data stored in database tables, and the data retained to support Snowflake’s data recovery features. Compute prices are based on the warehouse size and how long the warehouse runs.

The purpose of this post is to provide a broad overview of data analytics stacks. Please get in touch with us if our analytics service and skills-as-a-service pique your interest.

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Best Practices for Designing Effective Power BI Dashboards

Introduction:

Developing a compelling Power BI dashboard extends beyond mere aesthetics; it’s about delivering actionable insights. Let’s delve into pivotal best practices to shape dashboards that are not only visually appealing but also offer substantial value.

Define Clear Objectives:

Before delving into the design process, articulate the dashboard’s purpose. Establishing the key questions, it should answer ensures a focused design that resonates with Power BI users, meeting their specific needs.

Simplify and Declutter:

Maintain a clean and clutter-free dashboard with a minimalistic approach. This enhances user experience, allowing them to concentrate on critical data points without unnecessary distractions, thereby optimizing the efficacy of your Power BI visualization.

Choose the Right Visualizations:

Visualizations serve as storytellers within Power BI dashboards. Opt for those that effectively convey your message. While classics like bar charts, line graphs, and pie charts are reliable, explore alternative options based on the nuances of your data.

Consistent Design and Branding:

Create a cohesive visual identity by ensuring consistency in color schemes, fonts, and branding elements. This fosters a professional appearance and reinforces your organization’s identity within the Power BI platform.

This picture showcases a Power BI dashboard designed with various visuals serving different purposes. The use of a consistent font and color scheme makes it look clean and attractive, helping to tell the full story.

Prioritize Data Quality:

Maintain data integrity by ensuring accuracy and currency. Regularly clean and organize your data to prevent inaccuracies. Remember, in Power BI, the adage “garbage in, garbage out” holds, making data quality a cornerstone of effective dashboard design.

Optimize for Performance:

Prioritize speed by trimming unnecessary calculations and limiting resource-intensive visuals. This optimizes Power BI dashboard loading times, providing a seamless user experience, and ensuring efficient data consumption.

Enable Interactivity:

Engage your audience within the Power BI platform by leveraging interactive features such as drilldowns and filters. Empower users to explore the data independently, fostering a sense of ownership over insights.

Documentation and Training:

Facilitate user understanding by incorporating tooltips and guides within the Power BI dashboard. This is especially crucial for users new to the platform, ensuring a smooth onboarding experience and maximizing the potential of your visualizations.

Regularly Review and Update:

Maintain relevance by regularly reviewing your Power BI dashboard’s performance against objectives. Update it as needed, ensuring that it evolves with the dynamic nature of your business. A constantly evolving dashboard ensures continued value within the Power BI ecosystem.

Conclusion:

Effective Power BI dashboard design strikes a delicate balance between simplicity and functionality. By incorporating these tips, you’ll create visually appealing dashboards and empower users with valuable insights, facilitating informed decision-making within your organization. Power BI becomes a transformative tool when these best practices are woven into the fabric of your dashboard design.

Enterprise-wide Analytics implementation for a Pharmaceutical Business

The Pharmaceutical business generates vast amounts of data from various sources, including sales, transactions, inventory management systems, customer interaction, supply chain, manufacturing, and marketing campaigns. One of our clients encountered several data-related challenges such as data fragmentation, inefficient data movement, and lack of data orchestration. To overcome those obstacles and unlock the value of their data, we decided to move to Azure Cloud and implemented Azure Data Factory.

Problem

The client struggled with disparate data sources that hindered their ability to gain a unified view of customer behavior, sales performance, and inventory management. Additionally, manual data movement processes were time-consuming, error-prone, and difficult to scale. The data was scattered across multiple systems, making it difficult to gain a holistic view of the business, also with the increasing number of data breaches, the client needed a secure and compliant way to store and manage its data. The data was growing exponentially, and the client’s existing infrastructure was unable to handle the scale and complexity of the data. So, the client required a solution architecture that could automate data integration, provide seamless data movement and facilitate data orchestration for efficient analytics and decision-making.

Data Solution

DataTheta utilized Azure Data Factory, a flexible and scalable data integration platform that successfully handled their data-related challenges, the key tenets of the solution include

  1. Data Movement: Azure Data Factory to ingest data from various sources such as Sales databases, inventory management systems, and marketing platforms. Data Factory facilitated efficient data movement across on-premises and cloud storage systems.
  2. Data Transformation: Using Azure Data Factory’s data flow feature, we performed data transformations, cleansing, and enrichment operations to ensure data quality and consistency. This included mapping data from different sources, applying business rules, and performing aggregations.
  3. Data Orchestration: Azure Data Factory was used to create and manage data pipelines and data flows. We automated the end-to-end data integration process including scheduling, dependency management, and error handling to ensure the seamless execution of data workflows. The was now centralized and accessible to the entire organization, enabling teams to make data-driven decisions.
  4. Integration with Other Azure Services: Azure Data Factory was integrated with various Azure services such as Azure SQL Database and Azure Storage for data storage and analytics. It can be integrated with Azure key vault for secrets and key management and Azure Logic App for Specific use cases.
  5. Data presentation Layer: Data aggregated using the data pipelines were well presented using Power BI to various teams such as the sales group, supply chain group, production planning, etc., to effectively utilize the information about various operations.

 Data factory Implementation Along with other Azure services (ref: Azure.com)

Data factory Implementation Along with other Azure services (ref: Azure.com)

Implementation and Benefits

The implementation of Azure Data Factory and other Azure Services yielded significant benefits,

  1. Unified View of Data: Azure Data Factory facilitated the integration of data from multiple sources, providing clients with a unified view of customer behaviour, sales performance, and inventory management. This enabled the client to make informed decisions based on accurate and up-to-date information.
  2. Automated Data Workflows: Azure Data Factory automated the end-to-end data integration process, reducing manual effort and improving operational efficiency, ensuring timely data movement and transformation.
  3. Scalability and Flexibility: Azure Data Factory offered scalability to handle large volumes of data and flexibility to accommodate changing business requirements, Client was able to scale resources based on demand, ensuring efficient data processing and reducing costs.
  4. Data Security: Azure Cloud provided robust security features, including encryption, identity and access management, and threat detection.
  5. Data Quality and Consistency: By leveraging Azure Data Factory’s data transformation capabilities, the client improved data quality and consistency. The platform allowed us to apply data cleansing rules, perform validations and enforce data integrity, ensuring reliable insights and analytics.
  6. Time and Cost Saving: Azure Data Factory reduced the time required for data integration and movement, resulting in faster access to data and accelerated analytics. The automation capabilities of Data Factory also led to cost savings by minimizing manual work and optimizing resource utilization.

Value Creation: DataTheta’s solutions powered by Azure Data Factory from Azure Cloud provided the client with a comprehensive solution for managing and analyzing its data. The client was able to overcome its data-related challenges and achieve significant benefits. Datatheta’s architecture and solutions capability enabled the integration of various data sources seamlessly enabling a rich ecosystem for data processing and analytics. The rich data visualization provided information democratization among various business groups.

Monitoring Key Performance Indicators for fact-based business decision making

Must-know information about data analytics, data stacks and business value realization for a decision-maker.

In our day-to-day life, we monitor a lot of indicators about physical health, financial health, mental health, and more. Similarly, there is a multitude of indicators that aid enterprises in understanding their current and target business operation states. If you are in the journey of capturing these KPIs, you need to know the data sources and software ecosystem that render the error-free, information-rich, actionable KPIs from the available data. This exercise of KPIs building is more of a data analysis, which is like baking.

  • Well curated and good quality ingredients or “data” for expected results (or outcome)
  • The proportional blending of various ingredients or “math applied on the data” gives the best bake (information-rich KPIs)
  • Appropriate tools or “data infrastructure” makes the baking process easier
  • “Baking Skill” cannot be replaced with the best ingredients and bakery equipment – “the human intellect advantage”

Using unclean ingredients for the baking makes the pastry unconsumable, similarly, the unclean data need to be processed appropriately before bringing to the baker’s table. The ingredient quantity and the oven temperature bring out the crispy cookies, similarly when the math well applied to the data brings the error-free and acceptable KPIs.

Using unclean ingredients for the baking makes the pastry unconsumable, similarly, the unclean data need to be processed appropriately before bringing to the baker’s table. The ingredient quantity and the oven temperature bring out the crispy cookies, similarly when the math well applied to the data brings the error-free and acceptable KPIs.

Good data infrastructure coupled with competent data analysis brings the dependable KPIs for making your business data-driven.

Let us discuss about data stack and data definition,

Data Stack: Data stack is a set of software units that helps to move the data from different data sources (from SAP, CRM, HRMS, Financial Systems, etc), loads into a new unified destination, clean the data, and set it ready for data visualization (for business users) and consumption of data scientists (for advanced use cases). You can learn more details here.

Data Definition: Data definition is simply defined as how various data points (variables) are arithmetically processed to get a final value that helps in making a business decision. Let me demonstrate this with an example.

In the below data set, the sales of outlets are captured, the product visibility in the storefront enables easy access of the product and more sales eventually. But some necessary items such as fruits and vegetables though moved to less visible areas also generates enough sales. If you create a new KPI concerning product placement/visibility in a Type I supermarket in Tier 1 location, that will help the sales acceleration. This needs more questions to be answered about the product attributes, day of sale, and current product visibility.

In the below data set, the sales of outlets are captured, the product visibility in the storefront enables easy access of the product and more sales eventually. But some necessary items such as fruits and vegetables though moved to less visible areas also generates enough sales. If you create a new KPI concerning product placement/visibility in a Type I supermarket in Tier 1 location, that will help the sales acceleration. This needs more questions to be answered about the product attributes, day of sale, and current product visibility.

A statistical and mathematical calculation that renders the new KPI for the business users in an error-free and recurrent decision making of product placement in various cities in different types of supermarkets is termed as data definition (some practitioners term this as data augmentation or concoction).

A statistical and mathematical calculation that renders the new KPI for the business users in an error-free and recurrent decision making of product placement in various cities in different types of supermarkets is termed as data definition (some practitioners term this as data augmentation or concoction).

What to consider as a decision-maker?

Your company should set up its infrastructure with a central database that harbours the data for analysis (by your business users and data scientists) and reporting.

This paves the path to the data-driven business. Yes, you have a single version of data that gives the necessary information for your business operation. These data need to be cleaned and packed in different boxes that can be accessed by different groups.

Ok, but where to begin?

The first and foremost is the C-suite support. This goes without saying.

What could be the potential use case in your industry? In most setting the best one to start is with business intelligence projects rather than a data science project.

After deciding the use cases, you need to work out the data stack (or data infrastructure). Then who will handle the data and the governance of the data within your organization. This potentially answers the questions:

  1. Who is the data owner?
  2. Who has the privilege to access what type of data?

Your data infrastructure decision is very much dependent on the type of data you have (structured/unstructured) and the use cases that you have decided to work on.

Few more things to consider:

  1. Vendor dependence: Ensure in any case that you are not dependant on vendors. When your data volume increases and the data consumers grow up the cost will escalate substantially. Be wise while stitching a contract with your vendor.
  2. Automation: Automation is helpful. Play this with caution. Test the system thoroughly before the deployment.
  3. Data Science: Don’t venture into data science projects initially. Start with KPIs or BI visualization projects. Data Science projects require skilled stakeholders to develop, implement and deploy. This also has a longer development lifecycle that includes model performance monitoring and model versioning.
  4. Adoption: If you already have a BI tool that the team is comfortable using, build your data presentation layer on it.

The general layout of the BI project is as follows:

Data Loading:

Data loading is the process of moving the data from the source systems such as ERP, CRM, HRMS, other third-party applications to the data warehouse. Here we have two options for data loading:

  1. Make: We use schedulers to schedule the load jobs and write custom code to connect with the sources. DataTheta prefers Apache Airflow, an open-source tool as a scheduler to execute code written in Python.
  2. Buy: The services such as Fivetran, Matillion, Stitch. These are saas products with competitive pricing.

So, how to choose between these options:

  • Do you have large amounts of data? Or What is your next one-year projection?
  • Do you want to have the data in your own data centre due to its trade/regulatory confidentiality?
  • Do you have machines (PLC, DCS, SCADA) as your data source?

If your answer is “yes” for any one of the above, then you need to go with the “make” decision. If you have any other situation than the above quoted, write to DataTheta to obtain our opinion.

Should I build my data engineering team?

There are multiple factors you need to consider:

Speed to data consumption: How fast your team wants to consume the data. If you have ample time in hand, then plan for your first data engineer hire. If you are moving now new to the business intelligence space it is advisable to outsource the data engineering work as the workload will be less than 3 weeks for the initial projects.

Skill Availability: The skill availability is less due to the skyrocketing demand in the market. When I am drafting this article, the count of job openings is 14,000+ in India and high in other markets too.

Cost: The cost of hiring 3+ years of experience data engineer will cost around 80kEU to 110kEU per annum in Europe and 110K USD per annum in the US. Moreover, the workload will be more at the initial days of the project. Hence outsourcing makes perfect sense in most cases.

Cost: The cost of hiring 3+ years of experience data engineer will cost around 80kEU to 110kEU per annum in Europe and 110K USD per annum in the US. Moreover, the workload will be more at the initial days of the project. Hence outsourcing makes perfect sense in most cases.

Data Storage:

What is the amount of data your business will aggregate in the next year? The expected volume of data is the deciding factor for the selection of a database.

There are regular databases and massively parallel processing (MPP) databases.

If your data table does not exceed 5 – 8 million records, then you may opt for a regular SQL database. We recommend PostgreSQL for various reasons. If your business stores more data then you may consider Snowflake, Redshift, or BigQuery. If you have multiple petabytes of data, you need to consider the Data Lake architecture. Is this word new to you? Read more about Data Lake here.

Data Transformation:

After the data arrives in the central database, it is imperative to break it into clean, small, useful chunks and harbor it in different tables. This process is called data transformation and aggregation. There is a multitude of tools available to do this job.

Datatheta uses Pentaho for the data transformation jobs. Dbt is another tool worth the mention. It is an open-source tool and reduces the repetitive jobs of a data engineer. The CRUD procedure automation and keeping tab of the table lineage are useful features apart from the data testing and version control.

Data Visualization:

Data visualization is the critical component of a BI project. Mere visual appeal is not the game decider. Instead, the following need to be considered:

  1. Cost
  2. How much it can support customization for report consumers?
  3. Ease of use
  4. Handle extended size tables

At DataTheta, we tried Power BI, Tableau, Metabase, and Highcharts.

Metabase is an open-source tool. If you have less than 50 users to access the dashboard and the users know about reading the information content from data, then this is for you. If Metabase is hosted in your server then it is free to use, yet powerful.

If you are a bigger organization with more than 100 end users and require centralized control of the dashboard, then Power BI is the best option. The other tools such as Tableau and Qlik are also good to explore.

We tried Highcharts, this comes in a perpetual license model. If you have an internal team to handle the BI, this is a low-cost alternative to the Power BI and Tableau.

Cloud Service Provider:

The cloud service provider plays an important role in your business intelligence journey. If you prefer to stick to an on-premise data centre, you need to rethink the decision. Cloud services are useful and efficient in various aspects.

AWS, Azure, and Google Cloud are the market leaders in this space. If you are already utilizing the service of any of these providers, consider building the data stacks with them. You may negotiate a better deal based on your existing relationship. This article has covered the subject comprehensively.

It is important to know how the entire data ecosystem works. More important is deriving business value from data-driven decision-making. Data literacy is the important outcome of these efforts. This can be achieved by:

  1. Cherry-pick the best use case that suits your business.
  2. Understand the information content present in your data. Then, connect all the necessary data sources to a single database.
  3. Choose and set up the data stack suitable for your use case and organization culture. If you are not sure, it is helpful to talk to people who have done that.

This is an ever-growing field, and the technology evolves faster, so allow the right people to support your data analytics journey.

This article is created to give an overall picture of the data analytics stacks. If you are interested in our analytics service and skills-as-a-service reach out to us. You are welcome to write your comments and queries.

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