Competence Center Data Governance Value

Data governance rarely fails for lack of will. It fails because no one can show what poor governance costs and what good governance delivers. This is exactly the gap we close. The Competence Center Data Governance Value makes the value of data governance visible, measurable, and budget-ready.

In almost every company we talk to, AI governance is currently taking shape. At the same time, our research shows that the foundation beneath it is often missing. And that foundation is data governance.

Two paradoxes

1. The first paradox lies within data governance itself. Nearly everyone responsible for it says it is strategically important. Yet it is treated and funded like an obligatory exercise: a compliance topic with no budget of its own, no metrics, and no language that makes its value visible.
2. The second paradox comes with AI. Companies build AI governance without considering the data beneath it. It emerges wherever regulation demands it, while the fundamentals stay unresolved: Who owns the data, is its quality sound, where does it come from, and who may use it?

The value of data governance and the shared zone
Both paradoxes share the same root: the value of data governance has never been made visible, and that is exactly what is changing with AI. Because data governance and AI governance stand on the same ground. The questions of who owns the data, whether its quality is sound, where it comes from, and who may use it arise in the same way whether a human or a model makes the final decision. Whoever builds AI on ungoverned data inherits its gaps and makes them bigger, because now an automated decision depends on them. What good data governance was always worth becomes visible for the first time through AI, and can be quantified for the first time.

We research both sides
This brings AI governance to the centre of our work, alongside the classic questions about the value of data governance. We examine the underlying mechanisms, their levels of maturity, and above all their interfaces. Because where data governance and AI governance meet is where it is decided whether companies create value from their data or inherit risk.

Why now? Three external drivers make the question urgent:

• Regulation: FADP, FINMA requirements, DORA, and the EU AI Act call for transparency, traceability, and a documented data basis.
• AI scaling: The broad use of AI turns data quality and traceability from optional into essential; without them, large AI investments are structurally at risk.
• Data silos: Fragmented data and IT landscapes slow down decision quality and innovation.

Our work: four work packages
From this research, we are developing four building blocks that build on one another and make the value of data governance tangible:

1. Baseline Assessment (WP1): Where does a company stand? We assess maturity and existing governance mechanisms, uncover the gaps, place the company within a comparative framework, and treat AI governance as a cross-cutting lens. Free of charge and non-binding; the building block we are currently working on with our partners.
2. Value Assessment (WP2): What does good data governance deliver? Time-to-market, AI readiness, new data products, data-sharing capability… These effects are real, but they are rarely measured and even more rarely credited to data governance. The Value Assessment makes them visible and traces them back to where they arise.
3. Cost Assessment (WP3): What does poor data governance cost? Compliance fines, data-quality errors, manual corrections, failed AI projects… These real costs mostly stay invisible. The Cost Assessment quantifies them and creates a solid basis for investment decisions.
4. Potential Assessment (WP4): What is the next step worth? A synthesis of the Value and Cost Assessments into a business case, calibrated by industry, maturity level, and data intensity.

Our goal is to give leaders and governance owners a tool with which they can position data governance no longer as a cost factor, but as a strategic resource, and justify it internally.

How we prioritise: by value and by cost
The Potential Assessment positions every identified governance gap along two axes: the value achievable by acting (WP2) and the damage avoided by not acting (WP3). This yields a clear investment priority:

• Immediate need to act (high value, high cost): top priority, maximum ROI.
• Opportunity investment (high value, low cost): worthwhile, but not urgent.
• Pure damage control (low value, high cost): necessary only for compliance.
• Low potential (low value, low cost): only if resources are left over.

What practice partners receive

• Your own business case: empirically calibrated, tailored to industry, maturity level, and archetype.
• Peer benchmarking: Where do you stand compared to similar companies, and which investments pay off?
• Early access to validated instruments: the Value, Cost, and Potential Assessments before public publication.
• Prioritised investment roadmap: Which governance gap has the greatest value lever and the highest ROI?
• Scientific co-authorship: visibility in the academic and practitioner community (optional).

How the collaboration works
The instruments emerge in co-creation with our partners: they are built, tested, refined, and calibrated in the partners' own context. Partners bring their question and receive, in return, tools that work in their reality. Confidentiality is a given: partners are named in external materials only with explicit consent, and otherwise anonymised. We are glad to discuss the terms of a partnership in person.

Research meets practice
Our work thrives on exchange with practice. All of our insights come from conversations with the people responsible, those facing exactly these questions. This close contact keeps our research close to real-world challenges and, at the same time, gives our partners access to current insights, benchmarks, and instruments they could not develop on their own.

Would you like to make the value of your data governance visible? Whether for a professional exchange or a collaboration, we would be glad to hear from you.