WS MoreOrLess: Big Data
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What is the main topic discussed in this episode?
This is the short edition of More or Less, first broadcast on the BBC World Service.
Hello and welcome to More or Less on the BBC World Service. I'm Tim Harford.
What is meant by 'big data' and why is it generating hype?
Add all that information together and it's called big data. It is immensely valuable to a lot of people for good and possibly for ill.
Now back to health and the real gold rush in this as in so many other areas is all about big data.
One area where big data is about to make quite a big difference.
A lot of people are buzzing with excitement about the promise of what they're calling big data. Big data itself is a vague term. It sometimes refers to the vast data sets produced by scientific instruments, such as radio telescopes or the Large Hadron Collider. But another meaning of big data, the one which interests us for the next few minutes, is the digital information we're constantly producing as a by-product of searching online, tweeting, posting to Facebook, paying by credit card or wandering around with a mobile phone constantly revealing our location. It looks like computers processing huge data sets are going to give us all the answers that social scientists, marketers and spies could possibly want.
But here on More or Less, we want to make the case... for caution.
There's this enormous mythology that somehow the larger the data the closer it is to truth and I think it's at that level of mythology that we need to be most careful and most critical.
This is Kate Crawford, an academic and a researcher at Microsoft. We'll hear more from her later but first a couple of cautionary tales. Five years ago, a team of researchers from Google announced an impressive discovery. They'd found a way to track the spread of influenza across the United States by analysing what we search for on the internet.
Google Flu Trends was a lot faster at detecting the spread of the flu than traditional surveillance systems that required monitoring at hospital's
This is David Laser, Professor of Political Science and Computer and Information Science at Northeastern University. Google Flu Trends could give you flu case figures within 24 hours. The official figures from the US authorities took up to a fortnight. Google Flu Trends was fast, cheap and effective. It was also theory-free. Instead of developing some model of what people with flu might search for, the Google team just looked at historical correlations between flu and their top 50 million search terms. Then they let the algorithms do the work.
And this created a great deal of attention. There were headlines. And I think it has been held up as one of the exemplars of the potential of big data.
But there was a problem.
It started going off kilter, and systematically so, which was a bit odd.
How might big data transform healthcare and surveillance?
In the season 2011 to 2012, Google Flu Trends overestimated the flu by 50%. By the following year, it was predicting two cases of flu for every one that actually materialised.
If you say that there are more than twice as many cases as there really are, that's a big miss.
So what went wrong? Perhaps it was TV coverage about a flu epidemic that scared healthy people into searching online. Or perhaps Google search itself got too clever for Google flu trends, automatically suggesting search terms and changing what people ended up looking for. No one's sure what happened, which is part of the problem. Without a theory for why people were searching for flu terms, Google could only spot patterns, and patterns weren't enough. No doubt Google flu trends will bounce back. But unless we learn the lessons of this episode, we will find ourselves repeating it. I've been looking into this as part of my day job at the Financial Times, and what worries me is that for all the genuine promise of these new data sets, we risk forgetting some very old statistical lessons.
Google Flu Trends has already shown that finding patterns isn't enough. Knowing what causes those patterns matters too. And every time I hear people boasting about the size of their data sets, it reminds me of an old statistical story.
The battle is on.
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