Business Intelligence vs Business Analytics: Making the Right Data Choice

• Business
• June 12, 2025

by Parth Pandya

BI helps businesses understand what happened in the past and what is happening in the present, while BA gives them the power not only to predict the future but also to understand why things happened the way they did, what contributed to it, and what can be done to change the outcome in the future. We’ve curated this blog for decision-makers to help you better understand Business Intelligence, Business Analytics, their key differences and similarities, combining BI and business analytics, and more.

There is data everywhere.

Every deal closed. Every customer interaction. Every operational process. 

Every system is generating data. Your ERP, CRM, HRIS, SCM, POS, and more. Your email campaigns, website analytics, chatbots, NPS surveys, and even the sensors on your inventory or store floor.

But raw data alone is just noise.

It only becomes powerful when orchestrated into insight, foresight, and powerful business decisions.

That’s where businesses leverage data science services specifically for business intelligence and analytics.

Both do help in decision intelligence; however, they serve different purposes.

This blog breaks down the difference between business intelligence and business analytics in simple terms.

Before we jump right into BI vs BA, let’s first learn more about Business Intelligence or BI.

What is Business Intelligence?

Business intelligence (BI) is a process of collecting, managing, analyzing, and visualizing raw organizational data to gain useful insights. These insights are used by decision-makers to make better decisions regarding business operations and strategies. BI has come a long way from its precursors, data management systems and decision support systems (DSS), which emerged in the 1960s and 70s.

BI is no longer just about reports, charts, and dashboards.

It’s the first step toward replacing gut-feel decisions with data-driven business management.

The role of business intelligence is to tell “what and how it happened” (descriptive analytics). 

The summary of historical and present organizational data helps you track performance, spot trends, and understand the current state of your business.

The popular business intelligence techniques enterprises or business intelligence tools use to get the summary of organizational data are data mining, data analysis, Extract Transfer Load (ETL), descriptive analysis, data visualization, querying, Online Analytical Processing (OLAP), and more.

BI gives the decision-makers the answers for:

• Want to know past sales?
• Want to monitor inventory across regions?
• Want to compare last quarter’s sales performance?

BI, rather, business intelligence platforms like Tableau, Microsoft Power BI, Qlik Sense, Looker, and more, provide instant answers to all such questions, all in one place.

The organizational departments that use business intelligence are sales, finance, accounting,  marketing, HR, customer services, executive leadership, and operations.

Some real-world business intelligence examples are:

• Lotte implemented customer experience analytics to address its high Shopping Cart Abandonment Rate (SCAR), despite attracting millions of daily site visitors.
• Coca-Cola Bottling Company saved 260 hours a year by automating the manual reporting processes with a self-service BI implementation. It also resolved the issue of restricted and real-time access to sales and operations data.
• Charles Schwab Corporation used a BI solution to unify data from all its U.S. branches into a single platform. This centralized view enabled branch managers to detect shifts in client investment needs and empowered leadership to quickly assess regional performance. 

All you need is to either hire a business intelligence engineer or partner with a company offering data visualization services or hands-on experience with BI tools.

Learn more about Cloud business intelligence.

What is Business Analytics?

Business analytics is a process of analyzing data (structured, unstructured, and semi-structured) to discover insights and predictions that can be used to improve business decision-making. The approach involves the use of statistical methods, quantitative methods, computing technologies, predictive modeling, forecasting, and more to turn the raw data into actionable information.

BA goes deeper than just “what happened” and “how it happened” of BI. It helps organizations to shape and secure their future.

Business analytics plays a critical role in helping organizations to make informed decisions, improve operations, gain a competitive edge, anticipate challenges, mitigate risks, and more.

Here is how:

Business Analytics comes in four levels or types:

1. Descriptive — What happened?
2. Diagnostic — Why did it happen?
3. Predictive — What might happen next?
4. Prescriptive — What’s the best action to take?

The higher the level, the smarter the business decisions.

The business analytics techniques used across all these levels, catering to the specific requirements, are data management, data mining or KDD (knowledge discovery in data), data warehousing, data visualization, reporting, identifying root cause, forecasting, statistical analysis, text mining, and more. 

BA is what decision-makers invest in when they want answers for:

• Which customer is likely to leave?
• How much stock will we need for the festive season, which is five months from now?
• What would be the impact of changing the pricing strategy during the festive season?

Or any such question that gives them an idea of what the future holds and how they can make data-backed decisions that can give them better control of the predicted future.

Finance, sales, supply chain, marketing, HR, operations, and other business departments benefit from the use of business analytics.

Some impactful real-world business analytics examples include:

• Microsoft used business analytics, specifically workspace analytics, to assess the impact of an office relocation on collaboration and productivity. After the move, meeting travel time dropped by 46%, saving around $520,000 a year. Teams also met more often, showing that smart workspace planning can improve collaboration and save time.
• PepsiCo uses predictive analytics through its cloud-based data and analytics platform, Pep Worx, to target the right consumers with the right products. For example, it identified 24 million U.S. households likely to buy Quaker Overnight Oats and targeted them through specific retailers—driving 80% of the product’s first-year sales growth. 
• Uber improved customer support by using ML and NLP with its Customer Obsession Ticket Assistant (COTA)—a tool designed to improve customer service capabilities. After A/B testing its upgraded version, COTA v2, the company saw a 7% drop in average ticket handling time and better resolution accuracy—leading to higher customer satisfaction and significant cost savings.

Enterprises use various business analytics tools like QlikView, Sisense, SAS, Splunk, KNIME, Big Data Analytics, and more.

Learn about the impact of data science on businesses.

Business Intelligence vs Business Analytics: Key Differences

BI focuses on providing real-time information about day-to-day operations, while BA empowers organizations to predict future trends, forecast outcomes, and make informed decisions about what lies ahead. Although BI and Business Analytics are sometimes used interchangeably, they serve distinct purposes and offer different benefits.

Let’s explore the key differences between BI and BA in detail.

​Similarities between Business Intelligence and Business Analytics

Both business intelligence and analytics transform data into insights that businesses can use to make informed decisions, drive growth, and stay competitive. While often used interchangeably, BI and BA serve distinct yet complementary roles in data analysis and strategic decision-making.

Here are some similarities between BI and BA:

• Both aim at improving performance and efficiency across the business.
• Both support decision-making at various levels, from day-to-day operations to high-level strategy.
• Both complement each other; in fact, BI forms a solid foundation for BA.
• Both aim to reduce risk and uncertainty in business decisions, helping leaders act with confidence.
• Both involve gathering data from multiple sources (CRM, ERP, social media, etc.) to create a unified view of the business, breaking down data silos.
• Both emphasize data visualization, making complex insights accessible through dashboards, charts, and reports that non-technical users can understand.
• Both help monitor KPIs, track trends, and ensure alignment with strategic goals.
• Both benefit from automation (e.g., report scheduling, predictive alerts) to save time and reduce manual work.
• Ultimately, both contribute to strategic growth by transforming raw data into actionable intelligence, positioning the company for innovation and agility.

In a nutshell, business intelligence and analytics help interpret data for action. But when you need to go further, like automating insights and modeling complex scenarios, that’s where data science comes in.

Learn how data science turns data into dollars with smart business decisions.

BI vs BA: Which One Is Right for Your Business?

If you are an enterprise that has been struggling to track departmental performance, and manual spreadsheets just aren’t cutting it anymore, Business Intelligence (BI) could be the right choice. But if you’re trying to find the root cause of trends, uncover patterns, or simulate “what-if” scenarios to support long-term planning, then Business Analytics (BA) may be the better choice for your business right now.

The decision would be directly influenced by whether you are trying to track the metrics or want to transform them.

Let’s understand this better with some practical BI and BA use cases.

When to choose Business Intelligence

If your sales team exports weekly numbers from your proprietary CRM into Excel, marketing pulls campaign data from multiple ad platforms, and finance updates revenue figures manually. The reports are scattered, inconsistent, and late. 

Choose BI: to centralize data into real-time dashboards and eliminate manual reporting.

If you are a real estate decision maker who is looking to automate the recurring reports from different departments—finance, operations, and leasing—to help you track metrics like rental income, occupancy rates, ROI, and property expenses.

Choose BI: to not only overcome slow updates, errors, and conflicting numbers during reviews, but also to gain a centralized, real-time view of revenue trends, investment performance, and more.

If you are a global dental clinic and are looking to help your staff have better access to patient examinations, schedules, key metrics, daily tasks, and more for better coordination and patient experience.

Choose BI: to provide them a centralized staff portal that ingests the data from various sources and gives them a visual overview of daily operations.

When to choose Business Analytics

If you’re looking to diagnose customer churn in your SaaS business, which has increased over the past quarter.

Choose BA: to understand why the churn occurred. Use cohort analysis to segment users by signup date or behavior, apply logistic regression to identify key factors driving churn, and leverage survival analysis to estimate customer retention over time.

If you’re noticing a drop in on-time deliveries over the past month, you need to uncover the root cause.

Choose BA: to analyze route efficiency, shipment weight patterns, and vendor performance. Use regression analysis to determine how distance, traffic patterns, or carrier type influence delays, and identify operational gaps for optimization.

If your product development team is looking to enhance the product features by prioritizing them based on user behavior and feedback.

Choose BA: to analyze the feature usage frequency and depth, use cluster analysis to group users by behavior and needs, and mine NPS and sentiment data to link feedback with usage patterns.

If you’re unsure how business analytics can help address the challenges your business is currently facing, consider consulting experts in data analytics services to identify the best-fit BA solutions for your needs.

How about combining BI and BA?

BI and BA can coexist to provide the enterprise benefit of a more comprehensive strategy to make the most out of the data they have. Using BI and BA together gives you full-spectrum visibility. It empowers your business to benefit from the BI’s descriptive power along with BA’s predictive power. It means, 

Let’s take a scenario: commercial bank aiming to optimize risk while effectively monitoring daily performance. 

In a BI + BA approach, dashboards would give a live display of loan disbursements, deposit inflows, and branch scorecards. It can even trigger alerts if withdrawal volumes spike or deposit growth stalls. Further, advanced analytics models (e.g., logistic regression, decision trees, Monte Carlo simulations) continuously score each customer segment’s default probability. Those risk scores seamlessly feed back into the BI dashboard. It gives customer relationship managers one source of truth for both performance and predictions.

Concluding Thoughts on BI vs Business Analytics

Both Business Intelligence and Business Analytics play crucial roles in helping organizations harness their data, but they serve different purposes. BI focuses on understanding what has happened by organizing and visualizing historical data, enabling companies to monitor performance and make informed decisions.

On the other hand, Business Analytics uses statistical analysis and predictive modeling to forecast future trends, uncover opportunities, and drive strategic growth.

Choosing between BI and BA depends on your business goals, current data maturity, and the specific insights you need. For many enterprises, the best approach is a combination of both, leveraging BI to maintain operational clarity while applying BA to innovate and stay ahead of the competition.

FAQs on Business Intelligence vs Business Analytics