Reframing Open Big Data

Recent developments in the techniques and technologies of collecting, sharing and analysing data are challenging the field of information systems (IS) research let alone the boundaries of organizations and the established practices of decision-making. Coined ‘open data’ and ‘big data’, these developments introduce an unprecedented level of societal and organizational engagement with the potential of computational data to generate new insights and information. Based on the commonalities shared by open data and big data, we develop a research framework that we refer to as open big data (OBD) by employing the dimensions of ‘order’ and ‘relationality’. We argue that these dimensions offer a viable approach for IS research on open and big data because they address one of the core value propositions of IS; i.e. how to support organizing with computational data. We contrast these dimensions with two other categories that stem from computer science and engineering, namely
‘big/small’ and ‘open/closed’ to address the complex interplay between people and data, social interaction and technological operations. Thus conceived, this paper contributes an alternative approach for the study of open and big data as well as laying the theoretical groundwork for its future empirical research.