The value of predictive analytics seems obvious: who wants to “drive looking out of the rear view mirror”?<\/p>\n
Several recent books<\/a>,\u00a0articles<\/a> and blog postings<\/a>\u00a0point to a resurgence of interest in the topic, but what the term actually means is — as usual in our industry —\u00a0subject to some debate.\u00a0 I’ve tended to use the term to refer to predictive models, overlapping with the term “data mining”, a sentiment echoed by David Loshin<\/a> in this post<\/a>. Professor Ian Ayres has collected a fun selection<\/a> of simple predictive models on his web site, for everything from predicting life expectancy<\/a> to the success of a book title to.<\/p>\n Despite the obvious uses of this type of predictive analytics in organizations (here’s a 2004 article<\/a> 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.<\/p>\n 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 business people aren’t necessarily ready to believe the predictions!<\/p>\n <\/p>\n Another type of predictive analytics probably has a rosier future. James Taylor<\/a>\u00a0defines<\/a>\u00a0the 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<\/a> “analytic disciplines used to improve customer decisions” and lays out his point of view on how it relates to BI and data mining<\/a>.<\/p>\n As usual, I have to partly disagree: BI has always been “actionable” — otherwise nobody would ever have spent money implementing it —\u00a0and I\u00a0personally view traditional BI and predictive analytics as different levels of sophistication, rather than being fundamentally different concepts.<\/p>\n Here’s an example of how this kind of predictive analytics can help with “next best action” marketing<\/a>\u00a0and Seth Grimes believes<\/a> that it’s going to be next on the shopping list of existing BI players:<\/p>\n “So what are the next targets for the <\/em>analytics<\/em> companies? Predictive analytics…”<\/em><\/p><\/blockquote>\n Well, what do you predict?<\/p>\n","protected":false},"excerpt":{"rendered":" The value of predictive analytics is obvious: who wants to “drive looking out of the rear view mirror”? But in practice, predictive analytics hasn’t been widely implemented. What might change in the future? 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