Book Review – “Big Data @ Work” by Thomas Davenport

It is quite rare that somebody admits they were wrong about a major trend in IT which was overseen in the past. Quite rare. Fortunately, Thomas Davenport is not that kind of person – on the contrary. In the preface of his new book (“Big Data At Work) published by Harvard Business Review Press, he actually admits that he initially dismissed the concept as being just another technology hype. And you can hardly blame him – there are many gurus or specialists or journalists who still think that the “big data” concept represents another form of selling clound and analytical services. Promoted, of course, by the big IT companies who happen to endorse the concept quite actively.

From this perspective, Harvard Business Review Press has done some justice to the hype surrounding the concept. “Big Data at Work” was in a sense a long waited for book – people were maybe familliar with the concepts, but wanted maybe to know more about:

– how big data is implemented and used by various companies (the famous “case study” approach patented by the Harvard Business Review (one of the biggest business case studies publishers in the world by the way);

– why the author changed his mind on why big data is important;

– how is this new concept going to shape the future of the commercial analysis.

Interestingly enough, Thomas Davenport manages to capture in his latest book all these topics – and a few others. FIrst of all, the definition of big data as a “catch-all” (undefined data analysis requests) seems to fit quite well what is going on in the field. Big data is a bit misterious, especially because it  encompasses all the analysis that is not done via the traditional decision making channels and tools. And here “Big Data at Work” goes nicely with lots and lots of examples.

The HBR Press book is also easy to read (but not superficial). As it happens, I have read most of other Davenport’s books – and one thing they do well is to explain very briefly and clearly the main data analysis concepts that the reader should know. From his previous book “Keeping Up With Your Quants” I still keep at hand the list of the main statistical measures definitions that every researcher should know – that one is the clearest and most succint set of definitions I found so far for the main statistical indices.  “Big Data at Work” contains useful stuff too – for example look at the page 88 for the “Traits of Data Scientists”, which should be kept in mind when the big data models are created (and so forth). If you are a hacker, it is absolutely Ok, but then how far should this take you if you do not benefit the support of a trusted advisor?

The last (but not the least), “Big Data at Work” summarizes nicely the technologies available today for capturing, analysing and presenting the informational lingos. You might no value it too much – until you visualize the ROI table which shows that companies who put their big data at work get a substantiallly higher return for their shareholders. As many executives still do not really grasp the realities of this brave new analytical world, reading such an introductory book might give you a small competitive edge on them. And we all need this nowadays I guess…