Research Handbook in Data Science and Law
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Research Handbook in Data Science and Law

Edited by Vanessa Mak, Eric Tjong Tjin Tai and Anna Berlee

The use of data in society has seen an exponential growth in recent years. Data science, the field of research concerned with understanding and analyzing data, aims to find ways to operationalize data so that it can be beneficially used in society, for example in health applications, urban governance or smart household devices. The legal questions that accompany the rise of new, data-driven technologies however are underexplored. This book is the first volume that seeks to map the legal implications of the emergence of data science. It discusses the possibilities and limitations imposed by the current legal framework, considers whether regulation is needed to respond to problems raised by data science, and which ethical problems occur in relation to the use of data. It also considers the emergence of Data Science and Law as a new legal discipline.
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Chapter 14: Data localisation measures and their impacts on data science

Helena Ursic, Ruslan Nurullaev, Míchel Olmedo Cuevas and Paweł Szulewski


Data has become an object for governments all around the world to pursue. Those countries that can access, use and control data will be the rulers not only of the digital realm, but also of the real world. Cross-border flows of information and unlimited access to data are the main facilitators of the emerging digital economy. How easily data can be obtained, how expensive it is, and to what legal rules it must adapt are all critical questions for everyone involved, not least for data scientists. This chapter provides an overview of the measures to restrict the use of data put in place by various countries by adopting various legislative measures with the common characteristic of encumbering cross-border data transfers. This chapter furthermore looks into the drivers of these localisation measures and how these measures impact data science. The authors propose a policy framework for a balanced approach to data localisation, which takes into account the needs for data science.

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