For many people, data governance conjures up associations with rules, control, and restrictions. But at its core, governance is about management: providing direction, formulating ambitions, and taking responsibility. In this long read, inspired by an extensive conversation with Mark van der Veen MMIT-Trilingual, Vice President at DAMA NL, we explore how organizations can effectively shape data governance. We'll address leadership, stakeholder management, education, and the need for data literacy in the AI era.
🔎 Data governance is managing, not controlling
In many organizations, governance is seen as something you "have to manage" or a way to manage risks. But governance is essentially about something much more fundamental: driving change and realizing ambitions with dataThis requires more than policy; it requires leadership.
Governance isn't an abstract control tool. It's a way to achieve organizational goals with data.
The Metro Model, a visual aid developed by the DAMA Netherlands community, helps clarify this nuance. The model visualizes the layers of data management: from strategy and governance to tools, processes, and culture. It makes the discussion about data quality and responsibility more accessible to the business.
🧭 Leadership is not about job titles, but about behavior
Leadership in data quality doesn't have to come from a hierarchical role. On the contrary: true leaders show initiative, feel responsible, and initiate action. Mark describes how people from working groups within DAMA, without formal roles, demonstrate leadership by developing tools such as the Data Quality Management System.
You don't have to be a manager to demonstrate leadership. Above all, you have to want something and get it started.
These leaders play a key role in raising awareness and driving collaboration around data quality. Governance therefore requires social skills: knowing who your stakeholders are, and how to involve them in your ambitions.
🤝 Stakeholder management: working together on shared responsibility
Effective data governance requires collaboration between different roles in the organization, from data owners to process managers and end users.
Stakeholder analysis is crucial here. Who has an interest in which data? What are their goals? And what agreements should we make to prevent friction? The Metro Model helps with the structural involvement of these stakeholders in governance and consultation structures.
It's all about simple questions: What do you want to do with this data? What do I do with it? And should we make an agreement about this?
🧠 No data literacy, no governance
With the rise of AI, the urgency for data literacy is also growing. Employees need to know not only that data exists, but also understand, analyze and communicate withThis applies not only to data specialists, but to everyone in the organization.
Mark explains that DAMA NL therefore also focuses on education, for example by:
- The development of a bachelor's degree program in Data Management & BI (in collaboration with Utrecht University of Applied Sciences)
- Factsheets and tools for data management
- Serious Business Games, such as Data moles and Data dynamics, to work on data quality in a playful way
"You can only use data effectively if you understand what you're reading. And you only truly understand it if you can talk about it."
📦 From inspiration to implementation
All knowledge, models and tools from the discussion, including the full Data Quality Management System factsheet and the Metro model, are available in the free community of More data-awareHere you will find, among other things:
- ✅ All episodes of the podcast
- ✅ Tools and factsheets from the DAMA NL working groups
- ✅ Updates on new content, events and working groups
Ask your questions!
🎁 Ask all your questions for free via databewuster.comEvery Friday at 3:00 PM.
🚀 Or do you want it Guided implementation of the Metro Model within your organization? Schedule a no-obligation consultation now via powerbison.io/dataquality
