Data Governance: To DAMA, or Not To DAMA
This article was co-authored with Jesper Persson from Nimbleway
To DAMA, or not to DAMA, that is the question
When companies embark on a Data Governance journey, one of the first and most consequential questions they must ask themselves is: Which framework, if any, should we choose and adopt?
As two seasoned Data Governance specialists, with a footing in Denmark and Sweden respectively, we’ve seen this question come up time and again. It’s as if we’re the proverbial Prince Hamlet, staring across the strait of Kronborg, wondering, “To DAMA, or not to DAMA.” So, recently we decided to put our heads together and write an article about the pros and cons of using the DAMA Data Governance framework, and how it can be augmented to work even better.
… and just like in the good old days of 16- and 1700s, the Dane and the Swede are starting out on opposite sides of the argument. Hopefully we won’t need to call on reinforcements.
Introduction: A Case for Data Governance
Imagine a global manufacturing company, “NordMach,” grappling with performance issues in its supply chain. Recently, customer complaints about delayed orders have skyrocketed, impacting both the company’s reputation and bottom line. When the operations team investigates, they uncover a common refrain from across the organisation: “We have a data problem.” Leadership is frustrated, asking questions like, “Why is our inventory information inconsistent?” and “Who is accountable for this mess?” Without clarity around their data, they struggle to make informed decisions that could improve performance and satisfy customers—and find themselves falling short in realising the benefits of their high-profile digitalisation journey and their AI efforts.
For other companies, the need for data governance may emerge from regulatory compliance risks, the complexities of large ERP migrations, or strategic planning challenges. But regardless of the specifics, establishing clear accountability and structured processes around data is crucial for turning these challenges into growth opportunities.
Problem Statement: Addressing the Data Challenge
In response to its data issues, NordMach’s leadership team decides to act. In many companies, such initiatives are handed to IT departments or architects, who are expected to devise solutions, often focusing on technical aspects. However, some organisations recognise the need for a broader, more integrated approach—linking data governance with process governance, strategic planning, operational excellence, and risk management.
Given NordMach's relatively immature data governance capabilities, it’s understandable that the team feels pressured for time and may look to frameworks like DAMA for a quick start. DAMA is widely recognised as a structured approach to data governance, providing a common language and best practices that can bring teams into alignment.
But while frameworks like DAMA provide useful structure, they are ultimately just tools. Their success depends on how well they are applied and adapted to meet specific business needs. Different models have different strengths, and adopting one without a careful assessment of its gaps may lead to pitfalls. It’s essential to ask: What does this model provide? What does it leave out? And how can we bridge those gaps to ensure a model aligns with business strategy?
This article explores whether NordMach should adopt DAMA as its primary governance model. If so, what are the potential pitfalls, and does it require additional models or practices to ensure governance aligns with NordMach’s strategic goals? By evaluating DAMA’s benefits and limitations, we hope to offer a balanced perspective on its suitability for organisations like NordMach.
To address these considerations, we’ve structured a dialogue between Rasmus and Jesper, who are both real individuals actively working in this field. For the sake of this article, we’ve taken up contrasting stances to illustrate the key perspectives that emerge in data governance discussions. In reality, we find ourselves in agreement on most points but believe this format helps clarify common industry debates.
Where to Start: DAMA’s Value as a Launchpad
Rasmus: "When companies like NordMach are starting out their Data Governance journey it can be quite overwhelming to find out what to do and where to start. In these cases, a structured framework such as the model from DAMA can be a lifeline – especially since there are not that many independent Data Governance frameworks in the world today. It is very well recognised globally and offers a solid foundation for where to start. Rather than ‘reinventing the wheel’, DAMA gives a common language and established best practices, which can help align teams quickly and effectively."
Jesper: "I see your point, Rasmus, but I also see a major risk: with a framework that is so, well, technically focused, e.g., look at the famous DAMA wheel—it’s comprehensive in every technical aspect but conspicuously silent on the business side, it’s all too easy for this to turn into an ‘IT problem.’ IT will start delivering tangible results—yes—but those results may lack direct alignment with business priorities. If DAMA is implemented without anchoring it in NordMach’s business context, there’s a risk it will drift into theoretical discussions on data architecture, data models, system integrations etcetera. Business owners might start to lose interest—or even withdraw from the dialogue—because they’re not seeing a clear line to business benefits. They may end up feeling that it’s safer to be in the audience than to engage actively. And in a time of budget cuts, this lack of business ownership could eventually lead to the program being deprioritised."
Rasmus: " That’s a fair critique. The Data Governance section of the DAMA Book of Knowledge is probably one of the fluffiest ones. Also, I have never been a big fan of the honestly insanely convoluted description of decision-making forums and boards, as well as Roles and Responsibilities.
For instance, did you know that there are seven - Seven! - different types of Data Stewards in the listed DAMA model. All the way from CDOs to coordinators have some kind of Data Steward label on them. And let’s be honest, few organisations are good at working across their organisational ‘silos’, so if you don’t have a model that is good at creating coherence, this can lead to endless discussions about roles and responsibilities, which delays progress.”
Jesper: "Exactly. In my experience, you sometimes need to ‘Put the fish on the table’, as the saying goes. In this case, to be starting without role clarity risks creating a ‘Potemkin facade’ of governance—roles may look defined on paper but lack real authority and impact.
A business-centric approach doesn’t have to boil the ocean. By setting a clear scope based on immediate business needs, management can prioritise goals they understand and want to achieve. In turn, IT and architects can use DAMA-inspired frameworks to build solutions that fit into this business-aligned governance model."
Rasmus: "But what’s the alternative, then? Without a structured approach like DAMA, how do we make sure we’re covering all the critical aspects of data governance? We can’t afford to overlook foundational elements just because they seem technical. Where would you start instead?"
The Path Forward: Business-Aligned Governance with DAMA as a Support
Jesper: "Rather than framing this as a purely technical initiative, let’s approach it as a business governance issue that happens to about data. By starting with a business-centric perspective, we ensure that data governance aligns with NordMach’s strategic goals and integrates into the broader governance ecosystem.”
Rasmus: "I like this approach — it lets IT and architects contribute their DAMA-aligned structures in ways that directly support business outcomes. But you know what, it’s not straightforward. A business-centric approach requires a skilled team with a strong background in both business and data governance.
Finding people who can navigate this business-driven governance model effectively might be a challenge. I think it is safe to say that many IT professionals, consultants included, are more confident and comfortable navigating the technical side of data, rather than the business governance integration.”
Jesper: "True. But given the alternative—an overly technical focus that risks becoming a siloed IT effort—investing in the right expertise is worthwhile. By defining clear business-driven roles and mandates at the start, we’re less likely to conflict with existing governance structures or create redundant efforts. It’s more challenging, but it builds a sustainable governance model aligned with business goals."
Rasmus: "It’s a good point. I’ve seen cases where, if DAMA-driven governance isn’t anchored in business strategy, the business pulls back, and governance ends up looking like an extra expense. But with a business-centred approach, data governance can become a real contributor to growth rather than a cost. And that’s ultimately what we’re aiming for.”
Key Takeaways and Proposed Actions for Effective Data Governance
As NordMach and similar organisations begin their data governance journeys, it’s essential to balance foundational frameworks with a pragmatic, business-centric approach. Here are our key takeaways and recommended actions:
Start with Business Goals Anchor data governance within clear business objectives rather than letting technical frameworks drive the agenda. Identify the tangible business issues, then scope governance to address these directly. This approach helps avoid data governance becoming just another IT project.
Treat Frameworks as Tools, Not Solutions Frameworks like DAMA provide structure, especially in organisations with low data maturity. However, use these frameworks flexibly, adapting them to serve business priorities. Avoid fixating on technical components that may detract from delivering real business value.
Clarify Roles and Mandates Early Effective data governance relies on clear and well-defined roles and responsibilities. If these remain ambiguous, the initiative risks becoming a “Potemkin facade” with little practical impact. Address roles and mandates at the outset with a clear, manageable scope, and remember: don’t try to “boil the ocean.” A focused approach will create accountability without overwhelming teams.
Invest in Business-Centric Governance Expertise Implementing business-driven governance requires a blend of data governance and business governance expertise. Few professionals have the background needed to integrate these governance models effectively, so consider developing internal capabilities or hiring consultants with this hybrid expertise to prevent governance overload.
Plan for Adaptability and Evolution Governance is an evolving effort. As the business changes, so must governance structures and data needs. Aim for a scalable model that evolves with the company’s strategic context, using DAMA elements where appropriate but always rooted in business objectives.
By following these guidelines, NordMach—and other organisations at a similar stage—can start their data governance initiatives with a foundation that is practical, business-aligned, and sustainable.