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Data-driven? Start with data awareness.

How do you create true data awareness within a large organization? In this ninth episode of the podcast, Frans Klaassen MBA / CDMP his unique approach, insights and lessons learned from increasing data literacy globally and implementing sustainable data governance in a corporate environment.

What is data awareness?

According to Frans, data awareness doesn't start with technology, but with people. Or as he puts it: "It starts with explaining the impact of your actions on the rest of the data chain."

A simple anecdote illustrates this perfectly: a remarkably large amount of toilet paper was being used at a refinery. The cause? Operators were quickly clicking through input screens without realizing that their actions were being recorded as deviations further down the process. By explaining this, awareness was created. Small insights, big impact.

How do you create data awareness?

Frans introduced a broadly supported programme with:

• Peer-to-peer learning & Data Analytics Networks

• Reach: 9,000 employees in 44 countries in 6 months

• Practical examples, playbooks and videos

• Champions who function as ambassadors

"You have to make data fun and understandable. That way you get the whole organization on board."

By fostering broad support rather than top-down management, a community-driven culture emerged. Initiatives were fueled by intrinsic motivation and supported by centralized resources.

Tools & approach

• Automatic reporting on the source: accelerates processes and prevents manipulation.

• Data Quality Handbook: living document full of practical examples.

• Data Governance as an enabler, not a compliance obligation.

• Dynamic KPI Reporting: focus on 10 KPIs at a time, replace them when they remain green for 3 months.

“Make sure that data governance doesn't feel like an obligation, but rather a necessity to do good work.”

👥 Governance & overcoming resistance

Frans addresses the well-known challenge: resistance at the intermediate management level. His solution:

• Taking time for 1-on-1 conversations

• Drinking coffee instead of PowerPoint

• Listening, explaining, and connecting with practice

You don't have to get everyone on board right away. Start with a few who truly believe.

💡 Tips for organizations that want to start:

1. Start small – test with a pilot group

2. Anchor consciousness in culture – repeat, show value

3. Invest visibly in people – training, recognition

4. Make data fun and understandable – use stories

5. Create a consistent language – prevent proliferation of definitions

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

What is metadata really?

According to Shannon M Els Metadata is much more than simply "data about data." It's context. Without metadata, data is meaningless. You can't make decisions about information without the right context, just as you can't put on a coat in 25 degrees without knowing whether it's Celsius or Fahrenheit.

Definition according to Shannon: Metadata is information about data which determines the context in which data can be understood, classified and used.

Metadata is what allows you to know what 42 means—degrees Celsius, Fahrenheit, age, speed? Without metadata, data is blind.

Start simple, don't make it complex

Many organizations make metadata unnecessarily complex. Shannon emphasizes that it is usually not complex is, but a lot ofStart with a basic set: name, definition, source, owner, and quality requirements. And importantly: drink coffee with your colleagues.

Complexity has become an excuse for stagnation. Start with what you know. Ask your colleagues what they really need.

The triangle of data governance

Shannon introduces the data governance triad: metadata, data quality, and master data. These three form the core of every data-driven organization:

• Metadata gives meaning

• Quality creates trust

• Master data is the reference point

As soon as you touch one of these, you automatically have to consider the other two. They are inextricably linked.

"Without metadata, there's no data quality. Without data quality, there's no trust. Without master data, there's no clarity."

The metamodel: the backbone of your organization

A powerful insight from the conversation is the importance of a metamodelThis is the structured blueprint in which you specify which types of metadata objects you manage (such as tables, definitions, reports, rules, data delivery agreements).

This model makes it possible to:

• Clearly define responsibilities

• Testing maturity within your organization

• To use systems (such as Collibra) more effectively

Without a metamodel, metadata management is like building without a foundation.

The human side: metadata starts with coffee

Data governance is a human endeavor. Shannon repeatedly emphasized the importance of informal contact and human relationships:

• Look for people who are already taking responsibility

• Go for coffee, listen to frustrations and needs

• Label roll pass once you know who already fulfills them (informally)

The best way to start governance? Have coffee with the people who are already complaining about data quality. That's where your structure begins.

The Modeling Authority

Especially in larger organizations, there is a clear Modeling Authority Essential: a group or individual who oversees the quality and consistency of metadata. Ideally, this is an experienced information analyst or enterprise architect with knowledge of modeling, processes, and governance.

Conclusion: Metadata is not an IT party

Metadata is at the heart of how organizations manage their information. It's not just for the IT department; it belongs to everyone. By starting small, listening carefully, and structuring effectively, you can make a difference.

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

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

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.

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

From Compliance to Opportunity: The Real Value of Strategy

In many organizations you still see that they approach compliance as a mandatory number – ticking off rules, delivering audit reports and moving on. But, as Vincent Lassauw stilt:

“Compliance is also an opportunity to improve your processes, work in a more customer-focused manner, and develop new initiatives.”

Rather than simply complying with regulations, compliance should be a catalyst for business improvement. This requires strategic thinking: not just at the board level, but throughout the entire organization.

The foundation: assessments and maturity scans

According to Vincent, every data quality strategy starts with an honest picture of where you stand. maturity assessments and stakeholder interviews Essential. They help answer questions like:

• How (mature) is our data strategy?
• How anchored is data quality in our processes?
• Who are our data owners, and who experiences the data consequences?

Without those insights, you can't set targeted goals. And without goals, no strategy.

Data strategy and data quality strategy: separate or integrated?

An important nuance in the conversation is the relationship between a broader data strategy and a specific data quality strategyVincent is clear:

A data quality strategy isn't an optional extra. It's an integral part of your data strategy. Without reliable data, you can't make data-driven decisions.

Data quality acts as a critical success factor within a broader data strategy – for example, when operationalizing KPIs, building dashboards, or modeling AI applications.

The Data Quality Management System (DQMS)

An important instrument for anchoring is the Data Quality Management System (DQMS)Such a system formalizes the continuity of data quality assurance by:

• Setting up measurements and KPIs for data quality
• Defining the roll such as data stewards, business owners and auditors
• Describing the processes for monitoring, root cause analysis and corrective actions
• Providing for education and support building

Crucial here is the systematic approach: the DQMS not only offers control over the present, but also agility towards the future.

Continuous improvement & adaptability

One of Vincent's strengths: you're never done with data quality. Not just because your organization changes, but also because the outside world changes—from legislation to technology.

Especially in the age of AI, strategic agility essential:

Let your strategy guide your decisions around AI—not the hype. Only then can you create and manage value.

This means you must allow room for recalibration. Strategy isn't a static document, but a living management tool.

Success factors according to Vincent

The conversation with Vincent also produces a number of implicit conditions for success:

• Creating awareness at all levels of the organization
• Organizing ownership – governance boards, sponsors, domain managers
• Clearly define the scope – wide enough for impact, narrow enough for focus
• Building business cases – make the value of data quality visible and measurable

Conclusion: strategy as a starting point, not as a final item

Vincent demonstrates that sustainable data quality begins with a well-considered and supported strategy. It requires a balance between people, processes, and technology. And the courage to make choices – even when they're painful.

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

In the sixth episode of The Sonny Side of Life I started talking to Nico Kohlberg and René Wiertz on how they use the metro model and the standards framework as a foundation for data quality management.

The central question: How do you use the standards framework and the Metro Model to not only improve data quality but also embed it in the organizational culture?

From fact sheet to foundation

René explained his research into data cleansing led to the development of a fact sheet, now available to everyone through DAMA Netherlands. This fact sheet provides guidance: definitions, objectives, PDCA cycles, and the relationship to other data quality elements.

The standards framework acts here as spine: it provides procedural and substantive requirements (often based on ISO standards) that help to clearly define processes and roles.

The Metro model as a talking picture

Nico and René use it Metro model as talking picture to make complex data quality issues understandable for non-technical stakeholders.

“With the Metro Model, we can truly take the business with us: which processes are we affecting, which roles are crucial, and what are the benefits?” – Nico

This allowed them to start at UWV with five priority elements, including issue management, monitoring and defining roles and responsibilities.

From SharePoint to data-driven culture

Remaining practical is a common thread in their approach. They use SharePointNot high-end, but effective for visibility and support. In addition, they are building a business glossary and they work on process modeling in a single framework, so that data and business processes are seamlessly integrated.

Their goal?

“Not just data-driven, but data-driven work: using data with common sense.” – Nico

Culture, communication & collaboration

Technology alone is not enough. Awareness, small steps and the continue to demonstrate the value are crucial:

• Start small: tackle the biggest data pain.
• Show the added value: “What's in it for me?” must always be answered.
• Work together: internally with data owners and process architects, externally via platforms such as DAMA.

Why This Matters

The story of Nico and René shows how organizations improve data quality structural can improve by using the standards framework and Metro model not only as theory, but as architecture for change.

Want to know more? Listen to the full episode here.

And would you like to download the materials discussed and stay informed about new episodes? You can download them for free at databewuster.com.

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