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I'm a partner in the advanced analytics group at Bain & Company, the global management consulting firm. My primary focus is on marketing analytics (bio). I've been writing here (views my own) about marketing, technology, e-business, and analytics since 2003 (blog name explained).

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April 16, 2014

Book Review: "Big Data @ Work", by Tom Davenport

I've just finished Big Data @ Work: Dispelling The Myths, Uncovering The Opportunities, by Tom Davenport, the author of Competing On Analytics.  

The book marks a watershed moment in the Big Data zeitgeist. Much of the literature on the topic to this point has been more evangelical, telling us how analytics will make us all taller, smarter, and more handsome.  But the general sense for me has been of stories that are "way out there" for most organizations.  This latest book is much more about how to realize these visions with tactical, practical prescriptions across a range of issues.

Perhaps the most important of these dimensions is having a clear idea of the challenges or opportunities for which Big Data might be a part of the solution.  In Chapter Two, Davenport presents a very helpful series of use cases for using Big Data in several industry applications, including business travel, energy management, retail, and home education. He pushes further to examine the relative readiness of a number of different industries and business functions, including marketing and sales (which are the particular focus of my own upcoming book, Marketing and Sales Analytics). In Chapter Three he builds on these examples and sector assessments to offer a framework for shaping business strategies that leverage Big Data.  He suggests cost reduction, time reduction, new offerings, and decision support as broad objectives for focusing Big Data initiatives, and then further suggests a useful distinction between discovery-oriented application of Big Data (say, for sorting out emergent patterns of behavior to address) and production-oriented usage (applying Big Data to personalize experiences based on which emergent patterns might be worth the effort).

This "ends" focused approach to applying Big Data, in contrast to an "If I build it (my giant Hadoop Cluster) they will come" is an extremely valuable perspective to have introduced at this point in the evolution of this trend, and Davenport has wrapped it in a clean, well-organized package of specific advice executives interested in this space can profit from.

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