Predictive analytics consists of two major components - advanced analytics and decision optimization. Decision optimization will be shelved for future discussion while we focus on advanced analytics, with its comprehensive portfolio of sophisticated statistical techniques and data mining algorithms. In my January 2006 column, a framework was presented to characterize business challenges into five major analysis categories: classification, clustering, association, estimation and description. The next phase aligns model types with the appropriate business analysis with a final drill down to the specific algorithms.

Figure 1 provides a comprehensive view of the relevant model types and algorithms. This framework ensures that business analysis needs drive the solution methodology rather than the choice of algorithms dictating the business analysis options. This avoids the problem of "algorithm nirvana," where myriad analysis options can lead to an analysis-paralysis syndrome. The profile for each of the model types below discusses respective assumptions and constructs. To maintain consistency throughout the discussion, the term case will be used to describe a basic entity of information such as a specific customer, object or activity with all their associated attributes such as demographics, purchase behavior and market drivers.

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