We are a commune of inquiring, skeptical, politically centrist, capitalist, anglophile, traditionalist New England Yankee humans, humanoids, and animals with many interests beyond and above politics. Each of us has had a high-school education (or GED), but all had ADD so didn't pay attention very well, especially the dogs. Each one of us does "try my best to be just like I am," and none of us enjoys working for others, including for Maggie, from whom we receive neither a nickel nor a dime. Freedom from nags, cranks, government, do-gooders, control-freaks and idiots is all that we ask for.
Academic Mayer-Schönberger and editor Cukier consider big data the new ability to crunch vast collections of information, analyze it instantly, and draw conclusions from it. Big data is about predictions: math applied to large quantities of data in order to infer probabilities. Because big data allows us to analyze far more data, we will move beyond expecting exactness and can no longer be fixated on causation. The authors state, The correlations may not tell us precisely why something is happening, but they alert us that it is happening.
I can't tell you how many meetings we've had discussing what we're doing with "Big Data".
If I could get away with it, I'd ask "what, exactly, do you mean when you say Big Data, because it means many different things and the fact is we've been involved with Big Data long before computing was part of our industry. It's just 'bigger' now because we have collections of information which extend over longer time periods and broader swathes of information. Most of it is so inconsequential, it's hardly worth adding as an input and really adds little to the discussion."
But then again, I am a believer in complexity, in which little things have long term impacts. I just don't believe you can necessarily determine, over the massive amount of small inputs, which one will ultimately have the largest impact - because it's impacted by so many other little ones, and you have to start discounting these things at some point.
It's one reason I'm not a fan of Malcolm Gladwell. He's a good writer and it's good stuff, but it's not really adding value. The Tipping Point, in particular, was misinterpreted in more ways than you can possibly imagine.
Fine, as long as you're not going to do something about it. If you're going to take action, it's better to discern cause and effect as best you can. For example, if the amount of rainfall in Kinshasa correlates with the mortality rate in Shenyang, and you intend to act on this information, it's best to know which is cause and which is effect, because acting on either without knowing which is cause and which is effect could aggravate the mortality rate in Shenyang, or cause a drouth in Kinshasa, or both, none of which you intended your action to produce, and which comes as a big surprise. (Bloviation partially supported by a grant from the Big Data Studies Program of the National Science Foundation, Washington, D.C.)