How Big Data Creates False Confidence (nautil.us)

Mr D from 63 shares an article from Nautilus urging skepticism of big data: "The general idea is to find datasets so enormous that they can reveal patterns invisible to conventional inquiry... But there's a problem: It's tempting to think that with such an incredible volume of data behind them, studies relying on big data couldn't be wrong. But the bigness of the data can imbue the results with a false sense of certainty. Many of them are probably bogus -- and the reasons why should give us pause about any research that blindly trusts big data."
For example, Google's database of scanned books represents 4% of all books ever published, but in this data set, "The Lord of the Rings gets no more influence than, say, Witchcraft Persecutions in Bavaria." And the name Lanny appears to be one of the most common in early-20th century fiction -- solely because Upton Sinclair published 11 different novels about a character named Lanny Budd.

The problem seems to be skewed data and misinterpretation. (The article points to the failure of Google Flu Trends, which it turns out "was largely predicting winter".) The article's conclusion? "Rather than succumb to 'big data hubris,' the rest of us would do well to keep our skeptic hats on -- even when someone points to billions of words."

Comments

Big Data is not a substitute for Critical Thinking

[Etcetera]

Getting folks in the Bay Area to realize that is still an unsolved problem. Maybe they have an AI team working on it.

In all seriousness, I saw this a lot when working within a monitoring team, and in consulting I've done for other orgs. Big Data is great for vast, multi-dimensional analysis of massive amounts of data, but it's not a substitute for domain knowledge about *WHAT* you're monitoring, critically thinking about what you're looking for and what types of failure modes might occur, and simple(r) heuristics for triggers.

Trend analysis is very useful as an adjunct, for example, but within a server monitoring context it's not a *substitute* for having hard limits on, say, CPU load, or HTTP response time, or memory usage.

Somehow, people managed to come to conclusions and make good decisions even before we had terabytes of raw data being sifted through by statistical algorithms to come up with a result.

To place it into a broader cultural context, I see this in parallel with "data fetishisation" where nothing at all can be possibly true unless Science. And Data. Hipster praying at the altar of data.gov as some sort of left-wing (or Millennial) shibboleth for smug certainty when the basics -- the entry-level, basic 101 class of domain knowledge for the field -- is being forgotten.

I'm all for bringing in new tech and new analytic techniques, but you can't look at it as a panacea for failing to understand what's going on in your domain on a philosophical level.

Nassim Taleb wrote of this three years ago

[iMadeGhostzilla]

"Well, if I generate (by simulation) a set of 200 variables — completely random and totally unrelated to each other — with about 1,000 data points for each, then it would be near impossible not to find in it a certain number of “significant” correlations of sorts. But these correlations would be entirely spurious. And while there are techniques to control the cherry-picking (such as the Bonferroni adjustment), they don’t catch the culprits — much as regulation didn’t stop insiders from gaming the system. You can’t really police researchers, particularly when they are free agents toying with the large data available on the web.

I am not saying here that there is no information in big data. There is plenty of information. The problem — the central issue — is that the needle comes in an increasingly larger haystack."