Explaining the Value of Business Analytics: Clarifying driving factors in a decision support system

There are many ways to use data mining and predictive modeling to find patterns in large volume of data and transform them into actionable information. I have found that a combination of data mining and predictive modeling techniques are necessary to separate the clusters of data and clarify the driving factors. For the purposes of this posting, a vector is a multidimensional variable that uses mathematics to predict the probability of an event. When a vector (or predictive modeling) is used in conjunction with a clustering technique (separate data into groups of similar characteristics) the result is a decision support system that separate the clusters in the data and clarifies the driving factor.

The best visualization to explain this concept is in the area of graphics. The graphic to the left illustrates how a vector can clarify an image using vector and cluster analysis. This methodology can be used in any industry. The predictive modeling science and computer technology allow companies to build this capability efficeintly throughout an enterprise using products like Microsoft SQL 2005 Server Analysis Services, or SAS Enterprise Miner.