Big Data as statistical masturbation

Big Data as statistical masturbation by Rick Searle.

From the post:

It’s just possible that there is a looming crisis in yet another technological sector whose proponents have leaped too far ahead, and too soon, promising all kinds of things they are unable to deliver. It strange how we keep ramming our head into this same damned wall, but this next crisis is perhaps more important than deflated hype at other times, say our over optimism about the timeline for human space flight in the 1970’s, or the “AI winter” in the 1980’s, or the miracles that seemed just at our fingertips when we cracked the Human Genome while pulling riches out of the air during the dotcom boom- both of which brought us to a state of mania in the 1990’s and early 2000’s.


The thing that separates a potentially new crisis in the area of so-called “Big-Data” from these earlier ones is that, literally overnight, we have reconstructed much of our economy, national security infrastructure and in the process of eroding our ancient right privacy on it’s yet to be proven premises. Now, we are on the verge of changing not just the nature of the science upon which we all depend, but nearly every other field of human intellectual endeavor. And we’ve done and are doing this despite the fact that the the most over the top promises of Big Data are about as epistemologically grounded as divining the future by looking at goat entrails.

Well, that might be a little unfair. Big Data is helpful, but the question is helpful for what? A tool, as opposed to a supposedly magical talisman has its limits, and understanding those limits should lead not to our jettisoning the tool of large scale data based analysis, but what needs to be done to make these new capacities actually useful rather than, like all forms of divination, comforting us with the idea that we can know the future and thus somehow exert control over it, when in reality both our foresight and our powers are much more limited.

Start with the issue of the digital economy. One model underlies most of the major Internet giants- Google, FaceBook and to a lesser extent Apple and Amazon, along with a whole set of behemoths who few of us can name but that underlie everything we do online, especially data aggregators such as Axicom. That model is to essentially gather up every last digital record we leave behind, many of them gained in exchange for “free” services and using this living archive to target advertisements at us.

It’s not only that this model has provided the infrastructure for an unprecedented violation of privacy by the security state (more on which below) it’s that there’s no real evidence that it even works.

Ouch! I wonder if Searle means “works” as in satisfies a business goal or objective? Not just work in the sense it doesn’t crash?

That would go a long way to explain the failure of the original Semantic Web vision despite the investment of $billions in its promotion. With the lack of a “works” for some business goal or objective, who cares if it “works” in some other sense?

You need to read Serle in full but one more tidbit to tempt you into doing so:

Here’s the problem with this line of reasoning, a problem that I think is the same, and shares the same solution to the issue of mass surveillance by the NSA and other security agencies. It begins with this idea that “the landscape will become apparent and patterns will naturally emerge.”

The flaw that this reasoning suffers has to do with the way very large data sets work. One would think that the fact that sampling millions of people, which we’re now able to do via ubiquitous monitoring, would offer enormous gains over the way we used to be confined to population samples of only a few thousand, yet this isn’t necessarily the case. The problem is the larger your sample size the greater your chance at false correlations.

Searle does cite Stefan Thurner, which we talked about in Newly Discovered Networks among Different Diseases…, who makes the case that any patterns you discover with big data are the starting point for research, not conclusions to be drawn from big data. Not the same thing.

PS: I do concede that Searle overlooks the non-healthy and incestuous masturbation among and between business management, management consultancies, vendors, and others with regard to big data. Quick or easy answers are never quick, easy, or even satisfying.

I first saw this in a post by Kirk Borne.

Comments are closed.