In my fifth podcast episode I spoke with Ruud Kuil, a data management expert with over 16 years of experience in data governance and master data management. Ruud has trained several professionals in the DAMA DMBoK framework and helped countless organizations gain control over their data.
In our conversation, he shares his vision on data quality, practical examples, and concrete advice for managers who want to get started with data quality improvement.
1. Think big, start small
Ruud emphasizes that awareness at management level essential: “Senior management must understand what data is and what poor data quality means for the organization.” Still, he recommends starting small.
“The goal is world domination, but you start with five attributes, not two hundred.”
One of his practical examples: an organization that started with only 5–10 attributes within one product groupWithin six months, they were able to demonstrate that improved data quality delivered measurable value. This built the necessary trust and enabled rapid scaling.
2. Make the value tangible
Without a business case, data quality often remains an abstract concept. Ruud gives a striking example:
“An incorrect email address can mean an invoice is not sent, needs to be re-examined and physically printed. For one organization, this meant €100,000 per year in waste.”
By calculating and visualizing such concrete examples, you create support among stakeholders and enthusiasm within teams.
3. Start with a baseline measurement
For managers who want to get started, Ruud's advice is clear:
- Perform a baseline measurement. Talk to employees at all levels and ask: What keeps you awake at night? What data problems are costing you time or frustrating you?
- Define business rules. Ruud often collects 30–50 concrete rules which he can test against the data. This provides insight into data quality and priorities.
4. Role and responsibility: no empty titles
Many organizations appoint data owners or data stewards without a clear mandate. Ruud states:
A title without clear responsibilities is pointless. Define the role, determine the time commitment, and link it to concrete results.
5. Metadata management as a foundation
Another important point of attention: metadata managementModern data catalogs provide more insight than ever into the definitions, origins, and uses of data.
Without metadata, you're blind. Start early, otherwise you'll lose momentum and fall behind.
6. Data management = culture change
Finally, Ruud emphasizes that data management a long-term trajectory is:
"This isn't a six-month project, but a 5- to 10-year culture shift. You have to give people time to adapt to a different way of working."
Conclusion
Improving data quality requires a long breath, but it delivers enormous value. By starting small, making data transparent and transparent, and actively involving stakeholders, you build a solid foundation for the future.
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