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The Key Difference Between Business Intelligence and Advanced Analytics

By Peter Parker | Posted on March 19, 2020 | Posted in AI/ML, Data & Analytics

Information, as they say, is power.

This is particularly true for today’s enterprises, where staying competitive requires gaining real insight from data. And not only that, but using that data to make accurate predictions about where your business—and the market its in—is headed.

Given this, it's worthwhile to break down the differences between the two primary forms of data insight: business intelligence and advanced analytics.

Click here to learn more about how advanced analytics can help your business stay ahead of the competition.

Business intelligence

In a sentence, business intelligence is data that tells you what has already happened. 

It answers questions like:

  • How many widgets did you sell last quarter in a particular region?
  • How are your resources being used internally?
  • Is there a capacity for growth?

It provides the information you can use to forecast where and how you want to invest your time and resources in the coming months. 


Business intelligence can also help measure you against your competitors by revealing inefficiencies that force you to lag in the market—information like where the hiccups are in your workflows.

This type of intelligence is not new, of course. Still, the arrival of the cloud has made it possible to dig deeper into more information at a cost far cheaper than traditional on-premises data storage.

Advanced analytics

Where business intelligence is all about today, advanced analytics is all about the future—utilizing predictive analytics for things like testing theories without risk, anticipating changes in customer behavior, and forecasting pricing changes.

Making this work requires a massive amount of data. Not just structured data from known sources, but streaming data, semi-structured data, and completely unstructured data.


These types of data on their own may not be valuable, but in volume, they can be used for the unearthing of more granular information to drive strategic decisions.

The more data you have, the more opportunities for employing regression and behavior categorization to help you understand the different forces on your business and, eventually, new revenue streams.

Integrating advanced analytics into your business

Any advanced analytics initiative your enterprise takes on needs to be built upon a solid foundation of business intelligence. In other words, you need to have the scaffolding in place to utilize data before you can dig deeper.

Another key element is data democratization, which means building out your data landscape in a way that is not top-down. Whereas IT traditionally acted as the gatekeeper for analytics, data democratization allows for a wide range of teams—analysts, field teams, sales—to access the data they need to run their reports.

This freedom to explore and test analytics programs without fighting through a traditional top-down structure not only makes your enterprise more agile, but it also accelerates how the various segments of your business can put analytics to work.

Finally, advanced analytics is only as good as the data it has access to, which means using solutions such as data lakes and data warehouses to capture and manage data.

Learn more about how you can leverage advanced analytics for your enterprise. Read our in-depth guide on advanced analytics.