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Data Governance: Finding common ground for interdisciplinary research

The sharing and re-use of digital data can foster innovation and increase economic and social welfare. However, economic and legal obstacles as well as strategic uncertainties often seem to prevent organizations from sharing the data they control. The “Data Governance” project therefore aimed at elaborating on data governance models that help actors to share and re-use their data. 

Approach: To reach this aim, the research team screened literature reflecting the respective legal debate (esp. in data protection law and intellectual property law), the economic discussion (esp. in open innovation), as well as the political science perspectives (esp. on governance and regulation). Furthermore, the research team conducted first informal desk studies on existing data governance structures in different social contexts, such as in the automotive and advertisement industry, the health sector, and smart cities.

Results: However, instead of finding successful data governance models that effectively help actors to share and re-use their data, the research team found a highly heterogeneous and ambiguous terminology used both in theory and practice. This made it impossible to compare the discussed data governance phenomena with respect to their success characteristics. Therefore, the research team went several steps back from its initial goal and started to elaborate on a common research basis by defining, first, what the aim and challenges of successful data governance actually are. Subsequently, the group elaborated first “archetypes” of data governance models, which should serve as a starting point for more detailed conceptual and empirical work in the future.

Fig. 1: Data Governance Layers 

According to the proposed common research basis, the aim of successful data governance is to reconcile the conflicting interests in sharing and re-using data, while all involved actors have to coordinate – which is the first challenge – on different governance layers, i.e. the normative, process and technological layer. The second challenge is that the interests in sharing and re-using data or not (i.e. what value data have and what risks their processing entails) become concrete only in the course of the data processing. Thus, value and risks of data can be determined in advance only to a very limited extent, which makes resolving this conflict of interest a fairly dynamic (and tricky) process in practice. Research on data governance, esp. its coordination efforts, finally means providing an evidence-based basis for any regulative attempt. This methodical insight is important, in particular, for legislators who seek to regulate the sharing and re-use of data, such as the European Commission through its European data strategy.

Based on a literature review of research on existing models for sharing intellectual property rights, such as patents, the research team finally transferred these models to the data economy in order to define five data governance archetypes. While the research team was aware that the governance models discussed in the intellectual property law debate do not per se fit data governance and that the generated archetypes are very generic, they provide a pretty concise overview of the main characteristics of data governance structures between entities that share (or do not share) data.

Fig. 2: Data Governance Archetypes


Alina Wernick

Associated Researcher: Data, actors, infrastructures

Maximilian von Grafenstein, Prof. Dr.

Associated Researcher, Co-Head of Research Programme

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Journal articles and conference proceedings

Grafenstein, M. v., Wernick, A., & Olk, C. (2019). Data Governance: Enhancing Innovation and Protecting Against Its Risks. Intereconomics, 54 (4), 228-232. DOI: 10.1007/s10272-019-0829-9 Publication details

Other publications

Lectures and presentations

Data-Driven Economy Challenges and Opportunities.
Data Governance and Smart Cities (Session: Regulation and Governance). Intereconomics / IW (German Economic Institute). Hamburgische Landesvertretung, Berlin, Deutschland: 17.06.2019 Further information

Max von Grafenstein

Data Governance - Elaborating on a Research Concept
Towards health futures: digital innovation, infrastructure, and entrepreneurship on bio data (Session: Propositions for research on bio data). Freie Universität Berlin. Einstein Center for Digital Futures, Berlin, Germany: 07.03.2019 Further information

Alina Wernick

Organisation of events

Workshop session
Workshop# 182: Data Governance for Smarter City Mobility at Internet Governance Forum 2019. From 28.11.2019 to 28.11.2019. Estrel Berlin, Berlin, Germany. Co-Organised by: Alina Wernick, Maximilian von Grafenstein, Li-hsien Chang, Natalie Kreindlina, Christopher Olk (International) Further information

Li-hsien Chang, Natalie Kreindlina, Alina Wernick, Maximilian von Grafenstein

Data Governance: Between Concepts and Case Studies
02.07.2019. Humboldt Institute for Internet and Society, Berlin, Germany (International)

Natalie Kreindlina, Alina Wernick, Christopher Olk, Maximilian von Grafenstein

Who holds a stake in Smart City Data?
From 15.04.2019 to 15.04.2019. Humboldt Institute for Internet and Society, Berlin, Germany (International)

Alina Wernick, Christopher Olk, Maximilian von Grafenstein