PASS Webcast: Using OLAP to Optimize and Maintain Predictive Analytics Models

I will be delivering a Webcast for the PASS Virtual Chapter.
When: Friday, December 10, 2010 12:00 noon ET
Predictive Analytics (PA) models are created to help us make better decisions by providing best guesses when an exact answer doesn’t exist. In reality an exact answer hardly exists in an enterprise. We currently rely on computers to provide us pure facts and use our human intelligence to deal with the ambiguity. However, as enterprises become more complex driven by competition-related goals, business decisions will further extend well beyond the closed-system confines of well-defined closed-system processes into the wild open-system of the real world. Ambiguity will scale to proportions unwieldy to human brains.Therefore, over the coming years there will be a proliferation of PA models embedded throughout a typical enterprise significantly affecting the decisions of information workers. How this will affect what is required from IT is difficult to predict. But what is certain is that there will be an arms race in the speed at which organizations adapt to remain competitive.

The question is, can PA models adapt in an effective and cost-efficient manner? Simply retraining the PA models with fresher facts will not be good enough if the factors leading towards an event have changed. If the models cannot adapt, it is no different than a doctor prescribing treatments based on outdated knowledge.

We can ease this problem the same way we’ve eased other problems where the complexity is such that we need to formally analyze the situation. That is, building OLAP cubes to easily analyze prediction performance patterns. We can use these cubes to:

  • Optimize the PA models by isolating the subsets of circumstances in which it falters.  Analyze, validate, and optimize PA models.
  • Monitor prediction performance over time, Manage and Monitor the performance of the PA models in a Performance Management style.
  • Act as a data source to provide PA results to the end user in a robust manner. Ex: Present arrays of ranked possibilities instead of just one answer.


Over the years I’ve implemented a number of PA applications that go beyond the simple target-marketing or “if you like this, you’ll like that” scenarios. In complex PA scenarios where PA is being applied to a complex system or the players involved in what is being predicted are actually trying to undermine your predictions (ex: credit card fraud), the sophistication of the PA must be taken up a notch or two.

Figure 1 – An update of an old slide showing for human and machine intelligence can meet in the middle with PA.



About Eugene

Business Intelligence and Predictive Analytics on the Microsoft BI Stack.
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