The current surge of enthusiasm around big data has produced a predictable backlash. Some of it, like Gary Marcus's New Yorker post "Steamrolled by Big Data,"is insightful and well-reasoned (even though I have my quibbles with some of his points). This is not surprising, since he's a neuroscientist as well as a writer, and so quite comfortable with data.
Unfortunately, some other prominent commentators clearly aren't. David Brooks has taken up big data in his New York Times column recently, and literary lion Leon Wieseltier posted last month in The New Republic about "What Big Data Will Never Explain." Now, these guys are entitled to write about what whatever they like, but if they want to be taken seriously when discussing data they really should stop the kinds of elementary mistakes they've been making so far. Their errors of understanding and fact weaken their credibility and turn off quantitatively adept readers.
So as a public service here's a short list, written for non-quant-jock pundits, of things to keep in mind always when writing about data and its uses.t