Overview

We are drowning in information but starving for knowledge.

John Naisbett

In the area of data mining, we could say we are drowning in algorithms but too often lack the ability to use them to their full potential. This is an understandable situation, given the recent introduction of data mining into the broader marketplace (also bearing in mind the underlying complexity of data mining processes and associated algorithms). But how do we manage all this complexity in order to reap the benefits of facilitated extraction of patterns, trends, and relationships in data? In the modern enterprise, the job of managing complexity and identifying, documenting, preserving, and deploying expertise is addressed in the discipline of knowledge management. The area of knowledge management is addressed in greater detail in Chapter 7. The goal of this chapter is to present both the scientific and the practical, profit-driven sides of data mining so as to form a general picture of the knowledge management issues regarding data mining that can bridge and synergize these two key components to the overall data mining project delivery framework.

In the context of data mining, knowledge management is the collection, organization, and utilization of various methods, processes, and procedures that are useful in turning data mining technology into business, social, and economic value. Data miners began to recognize a role for knowledge management in data mining as early as 1995, when, at a conference in Montreal, they coined the term Knowledge Discovery in Databases (KDD) to describe...