The value of predictive analytics seems obvious: who wants to “drive looking out of the rear view mirror”?

Several recent books, articles and blog postings point to a resurgence of interest in the topic, but what the term actually means is — as usual in our industry – subject to some debate.  I’ve tended to use the term to refer to predictive models, overlapping with the term “data mining”, a sentiment echoed by David Loshin in this post. Professor Ian Ayres has collected a fun selection of simple predictive models on his web site, for everything from predicting life expectancy to the success of a book title to.

Despite the obvious uses of this type of predictive analytics in organizations (here’s a 2004 article that outlines marketing applications, for example), it has not been implemented widely. There are many reasons for this, including lack of BI maturity, the need for deep expertise, distrust of “black box” solutions that can’t “explain” the prediction, etc.

But perhaps the biggest reason is that people simply don’t seem to think it works in real life: simple models are too simplistic to be used outside of vendor demos, and even the most sophisticated models and technology soon break down in today’s fast-changing businesses. The cost and effort of implementing something that would actually be useful seem to outweigh the possible gains — especially because, as in the cartoon below, business people aren’t necessarily ready to believe the predictions…

Another type of predictive analytics probably has a rosier future. James Taylor defines the term more widely, encompassing “a variety of mathematical techniques that derive insight from data with one clear-cut goal: find the best action for a given situation” including “analytic disciplines used to improve customer decisions” and lays out his point of view on how it relates to BI and data mining.

As usual, I have to partly disagree: BI has always been “actionable” — otherwise nobody would ever have spent money implementing it – and I personally view traditional BI and predictive analytics as different levels of sophistication, rather than being fundamentally different concepts.

Here’s an example of how this kind of predictive analytics can help with “next best action” marketing and Seth Grimes believes that it’s going to be next on the shopping list of existing BI players:

“So what are the next targets for the analytics companies? Predictive analytics…”

Well, what do you predict?

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by lowes1 on September 12, 2010

How companies use real-time Predictive Analytics data to plan for the future.

Fortune magazine reports that in a tough global economy, sloppy decision making and “going with your gut” can get you punished–swiftly. That’s why leading companies are increasingly turning to a new management discipline called predictive analytics to compete and thrive. Rather than relying on intuition when pricing products, maintaining inventory or hiring talent, managers are using data, analysis and systematic reasoning to improve efficiency, reduce risk and increase profits.

In simple terms analytics means using quantitative methods to derive insights from data, and then drawing on those insights to shape business decisions and, ultimately, improve business performance. Thus predictive analytics is emerging as a game-changer. Instead of looking backward to analyze “what happened?” predictive analytics help executives answer “What’s next?” and “What should we do about it?”

Accenture research shows that high-performance businesses have a much more developed analytical orientation than other organizations. They are five times more likely than their low-performing competitors to view analytical capabilities as core to the business. Our research shows that there are big rewards for organizations that embrace analytics decision making.

Some of the most famous examples of analytics in action come from the world of professional sports, where “quants” increasingly make the decisions about what players are really worth. Consider these examples from the business world:

Best Buy was able to determine through analysis of member data that 7% of its customers were responsible for 43% of its sales. The company then segmented its customers into several archetypes and redesigned stores and the in-store experience to reflect the buying habits of particular customer groups.

Olive Garden uses data to forecast staffing needs and food preparation requirements down to individual menu items and ingredients. The restaurant chain has been able to manage its staff much more efficiently and has cut food waste significantly