Predictive Analytics: Looking to the Horizon

Executives often clamor for “visibility across the enterprise.” What they mean is they want to see more useful information from every corner of the business for decision-making.

But traditional business intelligence (BI), such as end-user query and reporting, basic OLAP (on-line analytical processing), etc., provides mountains of data on what has already happened. In other words, the things we can no longer affect.

What has been missing for years is information about the things we can still change. It’s an odd way to do business. BI seemingly gives the business only eyes in the back of its proverbial head.

This is changing, though. Predictive analytics software from vendors such as SPSS, a provider of predictive analytics software and solutions, and SAS, a BI vendor, is helping business managers spot commonalities, trends and associations among customers that they never would have seen before.

This, in turn, allows them to refine their selling efforts. And it’s doing it in real time, which means companies can react quickly and take advantage of those trends rather than finding out about them after the fact.

The growing market for predictive analytics (as much as $3 billion by 2008, according to some analysts) is putting BI eyes in the front of the proverbial business head. And training those eyes on the horizon.

In other words, BI is becoming less about storing and regurgitating data, and more about creating knowledge. All it takes is the desire to learn more than you ever thought possible about your customers and the right tools to extract the information.

Peepers on the Past

Traditional BI started out as a means to use historical data collected over a period of time to predict trends. Analysts would spin through a mountain of data and use their business knowledge to determine future strategies. This methodology is still the most popular today.

While typically better than pure “seat -of-the-pants” guessing, this method is still limited by the knowledge and/or interests of the users. In some cases, it follows the adage “research proves what the researcher set out to prove.” Business users start with an assumption and then use data gleaned through BI to support the conclusion.

It also smacks of a military truism: “Generals always fight the last war.” Traditional BI allows business users to gain great insight into what worked (and didn’t work) in the past in order to make decisions.

Unfortunately, it doesn’t allow for a change in market conditions, customer base or other factors that might influence the future. Like the French digging trenches at the beginning of World War II, you may be setting a strategy for conditions that no longer exist.

The thing to keep in mind when looking at historical data is the farther away an object is from a viewer, the smaller it appears. In the telescope of history, the farther away your data is from the present, the less significant it is to what’s happening today.

Venturing into the Unknown

The advantage predictive analytics provides is the ability to go beyond your assumptions and discover things you wouldn’t otherwise know. The key is the ability to recognize patterns or associations between seemingly disparate things.

One famous example (that may or may not be apocryphal) is the association between diapers and beer.

According to the story, a convenience chain looking at sales data noticed that when men purchased diapers, often they would also purchase beer. The supposition was that, if men were sent to the store by their wives for diapers, they felt they should pick up something for themselves as well.

This led to the conclusion that placing diapers closer to beer in the store will increase beer sales.

Whether the story really happened isn’t important. What’s important is that diapers and beer are not an association most people would make based on pure business knowledge alone.

Baby food, diaper wipes, formula, yes, but beer? In this case it was probably luck. Predictive analytics will find that association, and hundreds of others like it, because it doesn’t use human assumptions. It simply looks for statistical patterns and tells you what it finds.