Gary Marcus has a nice little comment on Big Data (here). He makes three important points. First that it's not all that clear what 'Big Data' means, though it seems to involve doing something with very very large data sets and that it's currently the really BIG thing. Second, that there are some things that for some problems, looking for patterns in the data can be quite successful. To quote Gary: "Big Data can be especially helpful in systems that are consistent over time, with straightforward and well-characterized properties, little unpredictable variation, and relatively little underlying complexity." And third, that we've seen this before. As Gary notes, this is a reprise of the overhype that sunk strong AI (a fad that in its hay day was as immodest and on the make as Big Data is now). Or as Gary says it: "In fact, one could see the entire field of artificial intelligence as an inadvertent referendum on Big Data, because nowadays virtually every problem that has ever been addressed in A.I. -- from machine vision to natural language understanding -- has also been attacked from a data perspective. Yet most of the problems are unsolved, Big Data or no."
Scientists like to think of themselves as immune to fads. Nope. Big Data is the current Big Thing. Gary's piece makes many of its (unjustified) pretensions evident. It's a good read.