Today large amounts of data are available to use for research on human behaviour: social media, data from online social networks, vast amounts of digital text, sensory information from personal hand-held and other devices, information from search engine usage and other online services, etc. The industry that relies on collecting, combining, selling and analysing digital footprints for all kinds of purposes ranging from simple targeted advertising to risk assessments and mass surveillance is developing with lightning speed. (Van Es and Schäfer, 2017). However, such data could increasingly be used to address larger societal issues of social interactions and relations, inequality, education, healthcare, political participation, and more. The advances in the use of such data in social sciences offer the possibility to answer questions that were beyond research in the past, and this new generation of large-scale, complex, and usually unstructured data requires new forms of data analysis and scientific applications. Some also suggest that as a consequence of the data revolution that we are already living in, a major paradigm shift in science is expected with far-reaching consequences to how research is conducted and knowledge is produced (Mayer-Schönberger and Cukier, 2013; Meyer and Schroeder, 2015). While the course of development in the data-driven industries and research seems to be unambiguous for the future in terms of its expected impact on business and how societies function in general, today the possibilities are still frequently overestimated by some ‘positivistic prophets’ – coming mostly from outside academia. In addition to presenting the main arguments of the papers in this section, the purpose of this editorial is to highlight a few of those issues and challenges that may shape the future of social sciences and of those who pursue in it, in relation to the new data landscape. After briefly elaborating on the definitions of Big Data, the focus will move to the question of epistemology; the changing dynamics among various fields of sciences; the new divides in access to data; and the main ideas behind the critical approach that social sciences might follow to find their right place in the puzzle.