Book Image

IBM SPSS Modeler Cookbook

By : Keith McCormick, Abbott
Book Image

IBM SPSS Modeler Cookbook

By: Keith McCormick, Abbott

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.
Table of Contents (11 chapters)
10
Index

Define business objectives by Tom Khabaza

Business objectives are the origin of every data mining solution. This may seem obvious for how can there be a solution without an objective? Yet this statement defines the field of data mining; everything we do in data mining is informed by, and oriented towards, an objective in the business or domain in which we are operating. For this reason, defining the business objectives for a data mining project is the first key step from which everything else follows.

The importance of business objectives in data mining

It is possible to describe data mining or other analytical activities without reference to business context but to do so is to omit a crucial component. It's because business knowledge is central to every step of the data mining process. There are two reasons for this:

  • Data necessarily provides a narrow or limited view of the world—the real world is always much richer than the data we collect about it—business knowledge is...