Big Data

The upbeat 'Big Data for All: Privacy and User Control in the Age of Analytics' by Omer Tene  & Jules Polonetsky in Northwestern Journal of Technology and Intellectual Property (forthcoming) argues -

We live in an age of “big data”. Data have become the raw material of production, a new source for immense economic and social value. Advances in data mining and analytics and the massive increase in computing power and data storage capacity have expanded by orders of magnitude the scope of information available for businesses and government. Data are now available for analysis in raw form, escaping the confines of structured databases and enhancing researchers’ abilities to identify correlations and conceive of new, unanticipated uses for existing information. In addition, the increasing number of people, devices, and sensors that are now connected by digital networks has revolutionized the ability to generate, communicate, share, and access data. Data creates enormous value for the world economy, driving innovation, productivity, efficiency and growth. At the same time, the “data deluge” presents privacy concerns which could stir a regulatory backlash dampening the data economy and stifling innovation. In order to craft a balance between beneficial uses of data and in individual privacy, policymakers must address some of the most fundamental concepts of privacy law, including the definition of “personally identifiable information”, the role of individual control, and the principles of data minimization and purpose limitation. This article emphasizes the importance of providing individuals with access to their data in usable format. This will let individuals share the wealth created by their information and incentivize developers to offer user-side features and applications harnessing the value of big data. Where individual access to data is impracticable, data are likely to be de-identified to an extent sufficient to diminish privacy concerns. In addition, organizations should be required to disclose their decisional criteria, since in a big data world it is often not the data but rather the inferences drawn from them that give cause for concern.