Among the numerous implications of digitalization, the debate about ‘big data’ has gained momentum. The central idea capturing attention is that digital data represents the newest key asset organizations should use to gain a competitive edge. Data can be sold, matched with other data, mined, and used to make inferences about anything, from people’s behavior to weather conditions. Particularly, what is known as ‘big data analytics’ — i.e. the modeling and analysis of big data — has become the capability which differentiates, from the rest of the market, the most successful companies. An entire business ecosystem has emerged around the digital data asset, and new types of companies, such as analytical competitors and analytical deputies, are proliferating as a result of the analysis of digital data. However, virtually absent from the big data debate is any mention of one of its constitutive mechanisms — that is, dataveillance. Dataveillance — which refers to the systematic monitoring of people or groups, by means of personal data systems in order to regulate or govern their behavior — sets the stage and reinforces the development of the data economy celebrated in the big data debate. This article aims to make visible the interdependence between dataveillance, big data and analytics by providing real examples of how companies collect, process, analyze and use data to achieve their business objectives.
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