Business Analytics: Moving From Descriptive To Predictive Analytics
I recently met with two organizations that had very impressive business intelligence (BI) implementations. However, they struggled to understand how predictive analytics could augment their existing BI capabilities. The purpose of this blog is to clarify the differences between business intelligence (a.k.a. descriptive analytics) and predictive analytics (a subset of data science) and highlight their complementary natures. In particular, you will learn that predictive analytics:
- Can provide more insightful and actionable answers to the organization’s most common questions, (“Who are my most valuable customers?” “What are my most important products?” “What are my most successful campaigns?”) than those generated by business intelligence alone.
- Can provide more future-looking answers and recommendations to questions that cannot be addressed at all by business intelligence.
Business Intelligence (Descriptive Analytics) Versus Predictive Analytics
Figure 1 is a pretty common way to view the worlds of business intelligence and predictive analytics. Business intelligence is the world of descriptive analytics: retrospective analysis that provides a rearview mirror view on the business—reporting on what happened and what is currently happening. Predictive analytics is forward-looking analysis: providing future-looking insights on the business—predicting what is likely to happen (usually associated with a probability) and why it’s likely to happen.
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.
Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes.
What’s Different About BI and Predictive Analytics? The Answers…
Maybe the easiest way to understand the differences between business intelligence and predictive analytics is to look at the answers they can generate. For example, business intelligence allows you to answer questions about the demographics or characteristics of your customers, products, stores, etc., and answer questions about the performance of your business across a number of different dimensions (see Figure 2).
On the other hand, predictive analytics allow organizations to go beyond the answers generated by BI by providing more predictive answers and recommendations to many of the same questions (see Figure 3).
Sample BI versus Predictive Analytics Answers
Below is a more detailed list of the types of answers that predictive analytics can answer that not only enriches the answers provided by business intelligence, but also provides answers to questions that business intelligence could not previously address.
Predictive analytics takes the questions that business intelligence is answering to the next level, moving from a retrospective set of answers to a set of answers focused on predicting performance and prescribing specific actions or recommendations.
For example, if we change the three key business questions that we asked earlier (most valuable customers, most important products, most successful campaigns) to a future tense, then you can see that we need a predictive analytics approach that is completely different from the conventional BI approach (see table below).
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.
 These three questions can be answered with more granularity, relevance, and actionability through the integration of currently non-accessible detailed transactional “dark” data (now available in much larger volume and detail) and new sources of internal (consumer comments, work orders, technician notes) and external (social media, blogs, newsfeeds) unstructured data.
 Per Dr. Pedro Desouza, the concepts of Big Data and Predictive Analytics get used interchangeably. They shouldn’t. “Big Data” provides data at a very low level of granularity with enough points that can still be statistically meaningful. Before the era of Big Data, business analysts needed to aggregate data in order to have enough points to predict with reasonable confidence. Now we can develop predictions at the individual customer level, without the need to aggregate the data, for example, to the store level.