Data Governance

Our Research

We study what good data governance is worth and how companies steer it. Our insights emerge in close exchange with the people who are responsible for these questions day to day: CDOs, CIOs, and CEOs from banking, insurance, technology, industry, and energy in Switzerland and Germany. The starting point was a series of 19 interviews. Since then, we have continued to deepen these topics in ongoing conversations with our practice partners. This keeps our work close to real-world challenges and ensures that our instruments are applicable in everyday business. Two research strands shape our current work.

1. Making the value of data governance measurable
Companies know that data governance matters, but they can neither measure its value nor justify it internally. The shortage is not one of data, but of a language that makes this value visible. That is exactly the language we develop: following the Design Science Research approach, we build concrete instruments and test them in real company contexts, in co-creation with our partners, tested and calibrated over several cycles.

Sample research questions:

• How can the value of data governance be measured so that it supports investment decisions?
• Which value dimensions are measurable at all, and from which level of maturity?
• How does the business case differ by industry, maturity level, and company type?
• What tends to move budget in practice: the damage avoided or the value created?

2. The interface between data governance and AI governance
As AI is deployed on a broad scale, the question of value becomes more urgent and, for the first time, quantifiable. Companies build AI governance without maturing the data governance beneath it. This matters, because the two share a common foundation: ownership, data quality, data lineage, and access are the same mechanisms for data governance and AI governance, regardless of whether a human or a model makes the final decision. We study this shared zone systematically.

Sample research questions:

• Which data governance foundations are needed for AI initiatives to be viable and scalable?
• Where do data governance and AI governance overlap, and where do they usefully complement each other?
• How can both forms of governance be aligned so that they reinforce one another?
• How can companies assess whether their data foundation supports their AI ambitions, and focus their efforts where it pays off?

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

Beyond that, our work thrives on exchange with practice. All of our insights come from conversations with the people responsible that are facing these questions. This close contact keeps our research close to real-world challenges and gives our partners access to current insights, benchmarks, and instruments.

If these topics are of interest to you, we would be glad to hear from you.