Big Data and IP Law: Risk Assessment and Fostering Data-Driven Transactions

Abstract

This master thesis is devoted to the hot topic discussed in both scientific and business circles: Big Data. This concept is approached from the perspective of IP law, in general, and, more specifically, from perspectives of database law, copyright law and law on confidentiality. The main purpose of the research is to suggest ways of risk assessment and possible business strategies for parties involved in Big Data research and its business exploitation. Methods applied in the course of research are legal dogmatic, comparative, empirical and economic. Comparative method is the central one for the study, because I compare EU and US legal regimes, as well as various legal mechanisms existing under IP law. In general, my research has demonstrated that US legal order provides better conditions for both business and scientific endeavours to exploit Big Data. Furthermore, confidentiality law is better suited to satisfy needs of those investing in Big Data, in comparison with database and copyright laws. Alternative solutions to make Bid Data projects more attractive for investments can be also found in open source and creative commons social initiatives. Most importantly, as legislative solutions dealing with Big Data are almost lacking at the moment, the best decision will be to apply contractual tools to fill lacunas in the legislation.