Four years after the original Nature paper was published, Nature News had sad tidings to convey: the latest flu outbreak had claimed an unexpected victim: Google Flu Trends. After reliably providing a swift and accurate account of flu outbreaks for several winters, the theory-free, data-rich model had lost its nose for where flu was going. Google’s model pointed to a severe outbreak but when the slow-and-steady data from the CDC arrived, they showed that Google’s estimates of the spread of flu-like illnesses were overstated by almost a factor of two.

The problem was that Google did not know – could not begin to know – what linked the search terms with the spread of flu. Google’s engineers weren’t trying to figure out what caused what. They were merely finding statistical patterns in the data. They cared about correlation rather than causation. This is common in big data analysis. Figuring out what causes what is hard (impossible, some say). Figuring out what is correlated with what is much cheaper and easier. That is why, according to Viktor Mayer-Schönberger and Kenneth Cukier’s book, Big Data, “causality won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning”.

But a theory-free analysis of mere correlations is inevitably fragile. If you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down. One explanation of the Flu Trends failure is that the news was full of scary stories about flu in December 2012 and that these stories provoked internet searches by people who were healthy. Another possible explanation is that Google’s own search algorithm moved the goalposts when it began automatically suggesting diagnoses when people entered medical symptoms.

Google Flu Trends will bounce back, recalibrated with fresh data – and rightly so. There are many reasons to be excited about the broader opportunities offered to us by the ease with which we can gather and analyse vast data sets. But unless we learn the lessons of this episode, we will find ourselves repeating it.

Big data: are we making a big mistake? – FT.com Fantastic essay by Tim Harford. I have a feeling that again and again the big data firms will insist that the data can “speak for itself” — simply because data gathering is what they can do.

There should be a lesson here also for those who believe that Franco-Moretti-style “distant reading” can transform the humanities. The success of that endeavor too will be determined not by text-mining power but by the incisiveness of the questions asked of that data and shrewdness of conclusions drawn about it. What T. S. Eliot said long ago — “The only method is to be very intelligent” — remains just as true in an age of Big Data as it was when Eliot uttered it.