As more and more organizations integrate their disparate data sources into "one single source of truth" by leveraging integrated enterprise resource planning (ERP) systems, operational data stores (ODS), data warehouses and federated solutions, it becomes quickly apparent that the anticipated big-bang ROI never materializes. Campaign management applications, supply chain management systems and financial management suites are often layered on top to provide effective environments for the storage, access, semi-analysis and visualization of data. Scorecards and dashboards also provide significant insight into the health of an organization by tracking key performance indicators (KPIs). In reality, however, this is only the first step in understanding, quantifying and managing the business. Much work and effort still needs to be invested in understanding the business drivers and their critical success factors. Predictive analytics with its portfolio of statistical and data mining models can play a significant role in identifying and understanding the critical business drivers and root causes that facilitate realization of the big-bang ROI.

In my March column we profiled a portfolio of predictive analytic models that could be leveraged to improve understanding of the business drivers (see Figure 1 in my March column). The business analysis challenges were divided into five distinct categories: classification, clustering, association, estimation and description. This month, I will focus on the classification category and the decision tree models specifically.

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