Overview

Information is the enemy of intelligence.
Donald Hall

In the recent past there has been a growing recognition that we are suffering from what has sometimes been called a data deluge. In Chapter 2 we outlined a data maturity hierarchy, which suggested that we turn data into intellectual capital through successive, and successively sophisticated, refinements. Data are turned into information through grouping, summarizing, and OLAP techniques such as dimensioning. But too much information can contribute to the overwhelming effect of data deluge. Further, information, which, as we can see, is data organized for decision making, can be further refined. By processing information through the lens of numerical and statistical search algorithms, data mining provides a facility to turn information into knowledge. Data can be organized along many dimensions of potential analysis. But to find the subset of dimensions that are most important in driving the outcome or phenomenon under investigation requires the kind of automated search algorithms that are incorporated in SQL Server 2000. This chapter provides detailed examples of how to use the Analysis Server data mining functionality to carry out typical outcome or predictive modeling (classification) and clustering (segmentation) tasks.

The chapter begins with a review of how to go about setting up an OLAP cube to perform preliminary data scanning and analysis as a first step to data mining. It shows how both the data mining model and the OLAP cube model are different representations of the same data source and how Analysis Manager stores both...