I just finished Tom Davenport's latest book, “Big Data @ Work” and, not surprisingly, liked it a lot. Indeed I've pretty much enjoyed everything I've read by the author. A Harvard-trained sociologist, Davenport is a methodologically-sound researcher. His deep interviews and surveys of executives and data scientists set a standard for excellence in an industry where marketing bravado generally supersedes scientific rigor. And though Davenport's writing is often not as provocative, as, say, that of Viktor Mayer-Schoeberger and Kenneth Cukier, authors of “Big Data, a Revolution That Will Transform How We live, Work, and Think”, I almost always find him on target from the practitioner's perspective. Steady, methodical, unspectacular – and spot on.

Davenport the scientist serves himself and readers well when he responds to his 2011 research inquiries to overcome a personal bias that big data's little different than the analytics he's already investigated so thoroughly. “I eventually concluded, as a result of this research, that are are real differences between conventional analytics and big data, though you wouldn't always know that from reading other books and articles about the topic.” – e.g. “Data Science for Business”.

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