The first book for managers and technical professionals that teaches data mining in an accessible way and that explains how data mining drives next-generation customer relationship strategies.

Data Mining Explained helps technically-proficient managers and IT professionals use powerful data mining technologies to solve important business challenges, most importantly to identify and better serve customer needs. Written by data mining experts, Data Mining Explained describes how companies in general and those in key vertical markets can design and build effective technical marketing and sales strategies and operations using data mining.

Data Mining Explained makes vital and increasingly mainstream concepts and technologies accessible to a wide range of readers new to the topic. Readers will learn how data mining can help them find relationships and patterns, such as customer buying habits, within the huge stores of data they gather every day. Data Mining Explained helps readers understand how data mining is defining next-generation e-commerce and customer relationship management (CRM) and can revolutionize how organizations engage their customers.

Teaches an increasingly mainstream technology to managers and technical professionals
Explains how data mining unites customer relationship management (CRM) and business intelligence
Describes how to develop a data mining strategy

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It's a good book if you want to have an overview of data mining and get some ideas about how to use it and it covers a quite a broad perspective and is very much uptodate. I would maybe have prefered a book which was more like a reference guide for practical every-day-use.

1 of 1 found the following review helpful:

Easier than technical DM books but more informative than business booksAug 15, 2007
By E. Schwartz
Much data mining literature is aimed either at marketers who do not even aspire to understand what the technology does, or technical practitioners who have graduate-level knowledge of math, computer science, and statistics. Data Mining Explained manages to straddle this fence, combining the quick-and-easy readability of a business book with the practical implications of a technical tome.

Readers of this book will learn what questions data mining can answer, what analytical techniques it can entail, how data mining projects can be managed, and how data mining has been used successfully in various industries. There are no complicated equations, but high-level algorithmic concepts like the difference between top-down and bottom-up clustering are thoroughly explained. The management advice includes a general project methodology and is augmented by the illustration of a typical project plan (in which it's interesting to note that <40% is spent on model prototyping, evaluation and implementation). There is also specific advice for handling typical issues like missing or bad data, intercorrelated features, and small target populations.

By far the most useful sections are Chapters 8-10 on Data Mining Techniques (divided into "Knowledge Discovery" and "Predictive Models") and Chapters 11-13 on Data Mining Management (divided into "Avoiding Pitfalls," "Overcoming Obstacles," and "Managing Projects to Success"). (Skip the case studies at the end. They're too general to be useful.)

One small drawback: the book has several obvious annotation errors and some evidence of discontinuity from cut-and-paste reorganization. My library book had some pencil edits and index corrections made by previous readers, and I added some of my own. But this is a small nuisance within a generally positive reading experience.