Business Intelligence vs. Business Analytics: What's The Difference?

But what is the difference between these solutions and which one is right for your business needs? The distinctions between BI, data analytics, and business analytics are subtle, and to make things more confusing, the terms are often used interchangeably. Before we clarify the differences, let’s begin with some simple definitions.

What is BI?

Business intelligence is an infrastructure that helps in the process of collecting, storing, and analyzing data from business operations. BI provides comprehensive business metrics, in near-real-time, to support better decision making. You can create performance benchmarks, spot market trends, increase compliance, and improve almost every aspect of your business with better business intelligence. Learn more about business intelligence and why it matters to your business.

What is business analytics?

A subset of BI, business analytics (BA) refers to the process of taking your company’s raw data and turning it into useful information, including identifying trends, predicting outcomes, and more. Some common methodologies in business analytics are:

What is data analytics?

Data analytics is the technical process of mining data, cleaning data, transforming data, and building the systems to manage data. Data analytics takes large quantities of data to find trends and solve problems. Data analytics is not just confined to business applications—it’s used across disciplines, from the government to science. 

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What is the difference between business intelligence, business analytics, and data analytics?

Clearly, all of these processes leverage data to improve your business, but let’s push a little further to understand the nuances between BI, business analytics, and data analytics.

Business intelligence vs. business analytics

The major difference between business intelligence and business analytics is the questions they answer. 

Business intelligence focuses on descriptive analytics 

BI prioritizes descriptive analytics, which provides a summary of historical and present data to show what has happened or what is currently happening. BI answers the questions “what” and “how” so you can replicate what works and change what does not. 

Business analytics focuses on predictive analytics 

Business analytics, however, prioritizes predictive analytics, which uses data mining, modeling, and machine learning (ML) to determine the likelihood of future outcomes. BA answers the question “why” so it can make more educated predictions about what will happen. With BA, you can anticipate developments and make the changes necessary to succeed. 

Applying BI and BA in the real world 

Let’s illustrate these differences with real-world applications of BI and BA. In this example, you sell homemade jewelry through an online store. Business intelligence provides helpful reports on the past and current state of your business. BI tells you that sales of your blue feather earrings have spiked in Utah in the past three weeks. As a result, you decide to make more blue feather earrings to keep up with demand. 

Business analytics asks, “Why did sales of blue feather earrings spike in Utah?” By mining your website data, you learn that a majority of traffic has come from a post by a Salt Lake City fashion blogger who wore your earrings. This insight helps you decide to send complimentary earrings to a few other prominent fashion bloggers throughout the US. You use the previous sales information to anticipate how many earrings you will need to make and how many supplies you will need to order to keep up with demand if the bloggers were to post about the earrings.

Business analytics vs data analytics

The difference between business analytics and data analytics is a little more subtle, and these terms are often used interchangeably in business, especially in relation to business intelligence. 

Data analytics is a broad umbrella for finding insights in data 

Data analytics can refer to any form of analysis of data—whether in a spreadsheet, database, or app—where the intent is to uncover trends, identify anomalies, or measure performance. Additional mathematics or IT skills can help data analysts do everything from managing a database of subscribers to calculating yields for a potential investment. 

Business analytics focuses on identifying operational insights 

Business analytics focuses on the overall function and day-to-day operation of the business. A business analyst would deal less with the technical aspects of analysis and more with the practical applications of data insights. Some job responsibilities might include creating a streamlined workflow or choosing the best vendors. 

Applying BA and data analytics in the real world 

Let’s return to our online jewelry store example. A data analyst would look at how people are using your website, identify trends in traffic, analyze visitor demographics, and maybe even create a system for tracking how customers click through different pages. A business analyst would deal more with the practical applications of this data and how it can help you make decisions for purchasing ads, creating new products, and updating your website.

Determine your business intelligence and analytics needs

Trying to decide if business intelligence or business analytics is better is not a helpful way to look at data management. In reality, a business needs both business intelligence and business analytics—descriptive and predictive analytics—to succeed. Plus, people throughout the business world often use these terms to mean a variety of things, so when choosing the type of technology, tools, and talent you want to invest in, you should focus less on BI vs. BA and more on what you need the data system to do and who will be using it. Developing a business intelligence strategy is an important first step in implementing a BI solution. Ask important questions, such as:

  • Who are the key stakeholders? Who will be using this system?
  • What departments need business intelligence and what will be measured?
  • What support do content authors and information consumers need?

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