The ever-increasing production of data has long lead to its own name : Big Data. Referring to a commonly renown definition, the phenomenon behind this term can be determined by three characteristics: volume, variety, and velocity. Recently added to these characteristics, are veracity and value. In particular, these last characteristics make it clear that the usability of Big Data also depends on a normative assessment: The value of data depends on the purpose for which the data is going to be used. This, in turn, given that this data relates to at least one identifiable individual, depends on the data protection laws. In order to fully exploit the value of Big Personal Data, it is therefore necessary to develop the required safeguards to effectively and efficiently mitigate data protection risks.
In the past five years, we developed, at the Alexander von Humboldt Institute for Internet and Society, an objective-normative scale in order to precisely measure data protection risks. On this normative basis, it is possible to adjust the statutory data protection instruments (such as data anonymization or pseudonymization, an individual’s information and/or consent) in order to fully exploit the innovative potential of Big Data.