Data, no matter how big or small, is only as good as those people–or algorithms–deciphering it. Even when Big Data can give us an answer to a problem, it doesn’t necessarily give us the root of the problem. When it’s read well, it’s a good complement to other methods of research; when read poorly, it can be used to create faulty policy: From Tim Harford’s latest Financial Times piece:

“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 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.’

Found data underpin the new internet economy as companies such as Google, Facebook and Amazon seek new ways to understand our lives through our data exhaust. Since Edward Snowden’s leaks about the scale and scope of US electronic surveillance it has become apparent that security services are just as fascinated with what they might learn from our data exhaust, too.

Consultants urge the data-naive to wise up to the potential of big data. A recent report from the McKinsey Global Institute reckoned that the US healthcare system could save $300bn a year – $1,000 per American – through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes.

But while big data promise much to scientists, entrepreneurs and governments, they are doomed to disappoint us if we ignore some very familiar statistical lessons.”