Archive for the 'business intelligence' Category

Super Crunchers: predictive analytics is not enough

Tuesday, April 29th, 2008

Ian Ayres, the author of Super Crunchers, gave a keynote at Fair Isaac’s Interact conference in San Francisco this morning.   He made a number of interesting points related to his thesis that intuitive decision making is doomed.   I found his points on random trials much more interesting, however.

In one of his examples on “The End of Intuition”, a computer program using six variables did a better job of predicting Supreme Court decisions than a team of experts.  He focused on the fact that the program “discovered” that one justice would most likely vote against an appeal if it was labeled a liberal decision.    By discovered we mean that a decision tree for this justice’s vote had a top level decision as to whether the decision was liberal, in which case the program had no further concern for any other information.  (more…)

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Adaptive Decision Management

Tuesday, April 15th, 2008

hr-dashboard.jpg

In this article I hope you learn the future of predictive analytics in decision management and how tighter integration between rules and learning are being developed that will  adaptively improve diagnostic capabilities, especially in maximizing profitability and detecting adversarial conduct, such as fraud, money laundering and terrorism.

Business Intelligence

Visualizing business performance is obviously important, but improving business performance is even more important.  A good view of operations, such as this nice dashboard[1], helps management see the forest (and, with good drill-down, some interesting trees). 

With good visualization, management can gain insights into how to improve business processes, but if the view does include a focus on outcomes, improvement in operational decision making will be relatively slow in coming.

Whether or not you use business intelligence software to produce your reports or present dashboards, however, you can improve your operational decision management by applying statistics and other predictive analytic techniques to discover hidden correlations between what you know before a decision and what you learn afterwards to improve your decision making over time.  (more…)

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