Five years ago, a team of researchers from Google announced a remarkable achievement in one of the world’s top scientific journals, Nature. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of influenza across the US. What’s more, they could do it more quickly than the Centers for Disease Control and Prevention (CDC). Google’s tracking had only a day’s delay, compared with the week or more it took for the CDC to assemble a picture based on reports from doctors’ surgeries. Google was faster because it was tracking the outbreak by finding a correlation between what people searched for online and whether they had flu symptoms.Not only was “Google Flu Trends” quick, accurate and cheap, it was theory-free. Google’s engineers didn’t bother to develop a hypothesis about what search terms – “flu symptoms” or “pharmacies near me” – might be correlated with the spread of the disease itself. The Google team just took their top 50 million search terms and let the algorithms do the work.The success of Google Flu Trends became emblematic of the hot new trend in business, technology and science: “Big Data”. What, excited journalists asked, can science learn from Google?As with so many buzzwords, “big data” is a vague term, often thrown around by people with something to sell. Some emphasise the sheer scale of the data sets that now exist – the Large Hadron Collider’s computers, for example, store 15 petabytes a year of data, equivalent to about 15,000 years’ worth of your favourite music.But the “big data” that interests many companies is what we might call “found data”, the digital exhaust of web searches, credit card payments and mobiles pinging the nearest phone mast. Google Flu Trends was built on found data and it’s this sort of data that interests me here. Such data sets can be even bigger than the LHC data – Facebook’s is – but just as noteworthy is the fact that they are cheap to collect relative to their size, they are a messy collage of data points collected for disparate purposes and they can be updated in real time. As our communication, leisure and commerce have moved to the internet and the internet has moved into our phones, our cars and even our glasses, life can be recorded and quantified in a way that would have been hard to imagine just a decade ago.Cheerleaders for big data have made four exciting claims, each one reflected in the success of Google Flu Trends: that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what 14december2014data science
causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”.Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge University, they can be “complete bollocks. Absolute nonsense.”
