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Big Data is everywhere. By analysing statistical trends gleaned from masses of information provided over the internet, Big Data promises to revolutionise both the way we provide and consume services and how policymakers allocate resources. Google claims that through the use of medical data-mining, on software such as Flu Trends, it could potentially save 10,000 lives a year if we would be more willing to set aside privacy concerns, while everything from massive infrastructural projects to supermarket stocking will massively benefit from a precise, real-time analysis of consumer behaviour. Some even predict that Big Data can provide even greater insights than simply accurate metrics, with programmes under development to mark university papers, assess the fairness of arts criticism and even predict potential areas of political unrest and destabilisation.

Yet amid all the hype, some commentators have expressed scepticism over what Big Data can and cannot show us. Despite its initial successes, Google Flu Trends has struggled with reliability as its datasets have grown wider and varied – and, it is argued, it is a problem confronting many similarly hyped projects. For some, the analysis offered by Big Data has not yet demonstrated it can properly adapt to the unpredictability of certain human behaviours or judgements. Even famed political analyst Nate Silver came unstuck when he tipped Brazil as clear favourites for this year’s World Cup and attempts to apply data mining techniques to translation and other qualitative judgements have thrown up very dubious results. So far, it is claimed, it offers an advance on existing statistical models rather than replacement; yet others counter that as techniques and algorithms grow more sophisticated many of these issues will be soon overcome.

Is the excitement generated by Big Data based simply on its potential or has it already started to deliver on its promised data-driven revolution? Will the benefits offered by data mining outstrip the fears we may be heading towards a ‘dictatorship of data’? How should we effectively balance concerns over privacy and accuracy with data’s possibly innovative uses? Are there any obvious limitations about how and where it can be applied? Will data help us better interpret the world or profoundly change it?