“No matter what, you still need business intelligence to know what really happened in the past, but you also need predictive analytics to optimize your resources as you look to make decisions and take actions for the future.”

The blog clearly explains the complementary nature of Business Intelligence (a.k.a. descriptive analytics) and Predictive Analytics despite the various differences between the two.  See excerpt below.

Business Intelligence verses Predictive Analytics

 

Business intelligence looks for trends at the macro or aggregated levels of the business, and then drills up, down, or across the data to identify areas of under- and over-performance. Areas may include: geography, time, products, customers, stores, partners, campaigns, or other business dimensions.

Business Intelligence is about descriptive analytics (or looking at what happened), slicing-and-dicing across dimensional models with massive dissemination to all business users.

Predictive analytics, on the other hand, builds analytic models at the lowest levels of the business—at the individual customer, product, campaign, store, and device levels—and looks for predictable behaviors, propensities, and business rules (as can be expressed by an analytic or mathematical formula) that can be used to predict the likelihood of certain behaviors and actions[2].

Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes.

View Full Post – Business Analytics: Moving From Descriptive To Predictive Analytics

Source – EMC