Big Data has big problems

Writing in the Financial Times, Tim Harford (The Undercover Economist Strikes Back, Adapt, etc) offers a nuanced, but ultimately damning critique of Big Data and its promises. Harford's point is that Big Data's premise is that sampling bias can be overcome by simply sampling everything, but the actual data-sets that make up Big Data are anything but comprehensive, and are even more prone to the statistical errors that haunt regular analytic science.

What's more, much of Big Data is "theory free" -- the correlation is observable and repeatable, so it is assumed to be real, even if you don't know why it exists -- but theory-free conclusions are brittle: "If you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down." Harford builds on recent critiques of Google Flu (the poster child for Big Data) and goes further. This is your must-read for today.

Test enough different correlations and fluke results will drown out the real discoveries.

There are various ways to deal with this but the problem is more serious in large data sets, because there are vastly more possible comparisons than there are data points to compare. Without careful analysis, the ratio of genuine patterns to spurious patterns – of signal to noise – quickly tends to zero.

Worse still, one of the antidotes to the ­multiple-comparisons problem is transparency, allowing other researchers to figure out how many hypotheses were tested and how many contrary results are languishing in desk drawers because they just didn’t seem interesting enough to publish. Yet found data sets are rarely transparent. Amazon and Google, Facebook and Twitter, Target and Tesco – these companies aren’t about to share their data with you or anyone else.

New, large, cheap data sets and powerful ­analytical tools will pay dividends – nobody doubts that. And there are a few cases in which analysis of very large data sets has worked miracles. David Spiegelhalter of Cambridge points to Google Translate, which operates by statistically analysing hundreds of millions of documents that have been translated by humans and looking for patterns it can copy. This is an example of what computer scientists call “machine learning”, and it can deliver astonishing results with no preprogrammed grammatical rules. Google Translate is as close to theory-free, data-driven algorithmic black box as we have – and it is, says Spiegelhalter, “an amazing achievement”. That achievement is built on the clever processing of enormous data sets.

But big data do not solve the problem that has obsessed statisticians and scientists for centuries: the problem of insight, of inferring what is going on, and figuring out how we might intervene to change a system for the better.

Big data: are we making a big mistake? [Tim Harford/FT]

(Image: Big Data: water wordscape, Marius B, CC-BY)