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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