Data Governance

Data governance in sectoral and inter-sectoral ecosystems

Our research explores how data governance functions within sectoral and inter-sectoral ecosystems. Across nearly all industries, new forms of collaboration are emerging in which data are shared, combined, and jointly utilized. In many sectors, from financial services and healthcare to energy, open data spaces are being established that enable the exchange of sensitive data among companies, start-ups, and public institutions. The more open these ecosystems become, the more crucial it is to understand how control, responsibility, and trust can be effectively organized.

We develop theoretical and practice-oriented design principles for sectoral and cross-sectoral data governance — that is, governance mechanisms that enable openness across organizational and industry boundaries. We distinguish between three dimensions:

  1. Structural mechanisms: shared standards, interfaces, and trust frameworks that ensure interoperability.
  2. Procedural mechanisms: roles, responsibilities, and liability logics that allow coordination without a central authority.
  3. Relational mechanisms: trust, reputation, and certification that foster cooperation.

Our goal is to provide organizations and policymakers with guidance on how to effectively balance openness and control in data-driven ecosystems.

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