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1 of 1 people found the following review helpful
4.0 out of 5 stars Mapping the interface between predictive analytics and business, 12 Nov 2014
By 
Dr Ross Gayler (Melbourne, VIC Australia) - See all my reviews
This review is from: Predictive Analytics, Data Mining and Big Data (Business in the Digital Economy) (Hardcover)
There are plenty of books available on predictive analytics (and data mining and big data - depending on your tolerance for hype), so why should you buy this one? (That's why you are reading this review. Right?)

This book is about predictive analytics as a tool to aid business (in the broadest sense). If that's not your interest this book is not for you. The point is that if predictive analytics is approached as an isolated technical exercise it won't deliver the expected business benefits. In order to be successful the analytics needs to be done with proper recognition of the business context and the business needs to be an informed consumer of analytics. You'd think that would be obvious, but project failures due to a poor interface between the business and the analytics are distressingly common – hence the need for a book like this.

The intended readership appears to be the people on either side of the business/analytics boundary (with a bit of a bias towards the business side). For a reader with an analytics background (but no business experience) the overviews of modelling techniques will probably be superfluous, but the sections on analytics, organisation, culture, ethics, and legislation may be essential eye-openers. On the business side, I would expect the reader to be a middle manager responsible for an analytics-based project or establishing an analytics function (or possibly a business owner who is willing to invest the effort to understand the problem).

There is nothing in this book that is new or revolutionary. It's not that kind of book and that's not the problem it's addressing. Rather, it's more a consciousness-raising check-list. There are a wide range of issues that need to be addressed on the business/analytics interface. This book touches pretty much all of them without giving definitive answers (because the best choice for any specific project is always going to be very context dependent). Consequently, the reader is expected to go off and do other reading in response to the issues raised. The book supports this with extensive appendices, notes, and reading lists.

Steve is well qualified to write this book. He has been a hands-on, pragmatic model builder for many years and has built models for a wider variety of organisations than most. Consequently he has extensive first hand experience of the problems that can arise on the interface between the business and analytics.

In summary, this is not a detailed technical, how-to book. Rather, it is a big picture, “What are we trying to achieve and where does it fit in the organisational context?” book. I would recommend it for anyone who has a hands-on involvement in a business predictive analytics project unless they are an old trouper who has already fallen into every potential trap. The reader must be prepared to do subsequent work, because this book will point out the issues but it's up to the reader to develop the solutions.

(Disclaimer: I know the author. We have been crossing paths at industry conferences for the last 20 years.)
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2 of 2 people found the following review helpful
5.0 out of 5 stars Excellent book for business moving into PA, 7 July 2014
This review is from: Predictive Analytics, Data Mining and Big Data (Business in the Digital Economy) (Hardcover)
A powerful insight into the world of predictive analytics that doesnt pull it's punches when discussing the downsides, problems and issues that can be encountered when using predictive analytics. Also loads of good stuff that will help you understand the power of analytics and how it can help improve decision making. Well worth a read.
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