What’s the Difference Between Business Intelligence and Predictive Analytics?

Business intelligence (BI) tools are designed to simplify data analytics for business leaders who don’t have the time to moonlight as a data scientist. But sometimes, just understanding the terminology of business analytics can present a roadblock to using BI to grow your business in a strategic way.

A small business that uses business intelligence tools without understanding which metrics to track or what to do with the data they’re tracking will end up with a powerful tool but no way to harness it. 

Similarly, a business that has a clear goal for their data analysis–such as making a critical business decision or plotting a course for their business over the next several years–but lacks understanding of the basic concepts that inform that data analysis will likely struggle to connect the dots.

That’s why it’s important to gain a basic understanding of data analytics terminology before attempting to use data analysis to inform business decisions.

For example, what is the difference between business intelligence and predictive analytics?

While business intelligence and predictive analytics have some similarities and overlap, grasping the differences between the two terms is important to understanding the overall concepts behind them, and how to use them to improve your business decisions.

In this article we’ll look at the significance of the two terms along with their key differences.

Business intelligence is an umbrella term that includes data analytics, and predictive analytics is a type of data analytics (along with descriptive analytics, diagnostic analytics, and prescriptive analytics.) In other words, predictive analytics is a type of business intelligence in the same way that trigonometry is a type of mathematics.

If you’re performing predictive analytics, you’re engaged in business intelligence. But just because you’re using business intelligence tools doesn’t necessarily mean that you’re performing predictive analytics.

To further explore and clarify the difference between business intelligence and predictive analytics, let’s have a quick refresher on each term.

Business intelligence is a blanket term for all of the tools and techniques that go into analyzing data for the purpose of improving business decisions and performance.

Predictive analytics is a type of data analytics–along with descriptive analytics, diagnostic analytics, and prescriptive analytics–that analyzes big data with the goal of answering the question “What will happen next?” 

Common predictive analytics tools and techniques include graph analysis, data modeling, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

Here is a graphic to further illustrate the difference between business intelligence and predictive analytics.

Now that we understand the difference between predictive analytics and business intelligence, let's look at how you can best use predictive analytics to help guide your business in the right direction.

Every business, whether it’s a solopreneur or large corporation, should be using some form of business intelligence to make decisions. This business intelligence could be as simple as tracking month-over-month revenue on a spreadsheet, or as complicated as using enterprise resource planning software along with a powerful BI system.

Luckily, you don’t need enterprise-level software to run some simple predictive analytics functions. But because predictive analytics relies on artificial intelligence and machine learning algorithms and models (unlike basic descriptive analytics), you will need business intelligence software to effectively perform predictive analytics. Advanced data science isn't the kind of thing you want to try with a calculator or spreadsheet.

Ready to get started with data analytics at your business? Here’s a short video to help you get started on the right foot:

The good news is that business intelligence software comes in different shapes and sizes, and is designed to be accessible for businesses of any size. There’s even free BI software for small businesses on a very limited budget, or businesses that want to test drive a BI system before investing in it.

Because predictive analytics is advanced analytics, it’s also helpful to hire a full-time data analyst to take on this task. You may already have a citizen data scientist in your organization who can pivot to take on this role, even if it’s not in their job description right now.

Once you have your BI tool up and running, and someone to manage it, here are a few examples of how various business units can benefit from predictive analytics:

• A sales team can use predictive analytics to predict which region is primed for the biggest growth and then expand staffing resources for that region.

• A retailer can use predictive analytics to take the guesswork out of deciding which products to stock for the next quarter.

• A customer service department can use predictive analytics to predict when and why customers decide to leave, and what to do to change those outcomes.

• A project management team can use predictive analytics to forecast which projects are in danger of failing, missing deadlines, or going over budget and take corrective actions to avoid those outcomes.

Next steps to learn more about business intelligence tools

Ready to gain new insight into your business using business intelligence and predictive analytics? GetApp has you covered. Our Business Intelligence Buyers Guide has all the information you need before starting your BI software buying journey, and our Category Leaders in Business Intelligence highlights 15 top options in BI software based on verified user reviews.

GetApp’s Category Leaders in Business Intelligence (Source)