On the flight back from the London Big Data Week, tasked with writing an article about the rise of identity theft, I had a rare moment of insight where the two topics suddenly presented themselves as two aspects of the same phenomenon.
One of the primary values of working with Big Data, after all, is that one may use insights drawn from one aspect of consumer behaviour to drive innovation and insight into another. At a pretty basic level, for instance, the purchasing history of your customers can be paired with data on the way in which they navigate your website, and the insights gained can be used to drive improvement in each field.
The thing is, this process is almost identical to that performed by identity thieves: comparing apparently discrete data sets to undermine the anonymity of both sets. This process can be usefully understood through the DIKW (Data, Information, Knowledge, Wisdom) Pyramid: at the lower levels of the pyramid, your customers might not care that you know their social security number or email address. However, if you are using analytic techniques to generate data on the behaviour of individuals, those same individuals are going to start to feel creeped out.