Data Governance

Our research shows that Data Governance in practice rarely fails due to a lack of willingness, but because organizations cannot demonstrate what it costs to do nothing, and what is gained by doing it right. In 19 interviews with decision-makers from banking, insurance, technology, industry, energy, and law in Switzerland and Germany, one finding emerged consistently: not a single organization has a functioning KPI framework to justify Data Governance investments.

The core problem is not a lack of data, but a lack of language that makes its value visible.

We are therefore developing three complementary instruments to make this value measurable:

1. Cost Calculator: What does poor Data Governance cost? Compliance fines, data quality errors, manual corrections, failed AI projects. These costs are real, but often invisible. This instrument quantifies them, providing a solid foundation for investment decisions.
2. Value Tracker: What does good Data Governance deliver? Time-to-market, AI readiness, new data products, data-sharing capabilities. Offensive benefits that are too rarely measured or communicated in practice.
3. Potential Calculator: What is the next step worth? A synthesis of both instruments: a context-specific business case tailored to industry, maturity level, and data intensity.

The goal is to give executives and governance professionals a tool that allows them to position Data Governance not as a cost factor, but as a strategic resource.

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.

These research themes form the foundation of our bilateral projects with companies and institutions. We support practice partners with scientifically grounded approaches drawn directly from this field of research.