The Role of Data Governance in AI: Landscape in the Netherlands

By Ross Henderson & Robin Buitendijk, Harnham Netherlands

The transformative power of technology has entered an exciting phase with artificial intelligence (AI) and machine learning (ML) actively reshaping how organisations operate and make decisions.

As we embrace these innovations, a crucial factor often dictates whether they deliver real value. Without high-quality, well-managed data, even the most sophisticated AI or ML model will struggle to produce reliable insights. It appears increasingly clear that the long-term success of AI initiatives is closely tied to one foundational element: data governance.

Often overlooked in the race to innovate, data governance is now emerging as a foundational requirement not just for compliance, but for building scalable, ethical, and trusted AI systems 

What is Data Governance?

Data governance refers to the structured approach to managing data quality, accessibility, security, and usability within an organisation. It sets the policies, processes, and responsibilities that define how data is used and protected, ensuring that it’s accurate, consistent, and compliant with evolving regulations.

In essence, good data governance means you can trust your data. And if you can’t trust your data, you can’t trust your AI.

Why It Matters for AI

AI models are only as good as the data they’re built on. Without trustworthy data, algorithms can produce biased, inaccurate, or misleading outcomes. In fields like finance, healthcare, and public services, the risks of poorly governed AI are significant, ranging from reputational damage to legal liability.

As Robin Buitendijk, Data Governance Specialist at Harnham, puts it:

“Data management and data governance are the pillars of a successful AI. Building an AI without them is like building a house without pillars. As the Dutch market increasingly prioritises data-driven innovation, ensuring strong data governance is no longer optional—it’s essential for sustainable growth.”

Where the Netherlands Stands Today

The Dutch market is steadily keeping pace with AI adoption, mirroring broader European trends while setting its own ambitious benchmarks. According to recent industry insights, 88% of organisations in the Netherlands report using AI technologies, well above the European average. However, only 35% have applied a robust measurement strategy to assess impact, suggesting a maturity gap when it comes to governance.

Mischa Luyf, IT, Data & Transformation Executive, sees a clear shift:

“In the Dutch market, Data Governance is shifting clearly from a compliance necessity towards becoming the critical foundation for AI initiatives, especially when scaling trustworthy AI. Companies increasingly recognise that solid Data Governance isn’t just supportive—it’s essential.”

Julien Hoornweg, Enterprise Data Architect, adds:

“I notice that organisations across Europe increasingly prioritise the ethical use of AI, which is a positive step. The next challenge is integrating AI governance, data governance, and data management operations into a unified framework, rather than isolated efforts, with data architecture serving as the glue that solidifies this alignment.”

Steven Veldstra, AI Data Strategy and Management Specialist, offers a cautionary note:

“AI should be an integral part of a company’s Data Strategy, placing it squarely within the Data Management domain. Without the adoption of adequate AI-focused data management guidelines, regulations, and policies, AI usage poses significant risks to organisations without them knowing it.”

Insights from Data Leaders

Mark Lemmen, a seasoned data governance expert, highlights the evolving perception of governance:

“More and more organisations are realising that strong data governance is a critical prerequisite for reliable and ethical AI applications. AI is accelerating data governance adoption as companies take data quality, data ownership and security more seriously and increasingly recognise that companies in control of data and processes achieve higher efficiency and profitability. In every sector, organisations that handle their data best will eventually become the leaders in the specific industries.” 

Lex den Doop, Trainer & Consultant in Data & AI Governance, sets the stage with a thought-provoking observation:

“Organisations typically lack a holistic and integrated approach to governance of data, processes, and AI.”

He continues by introducing the LEXIM framework:

“If governance is the foundation for data, process, and AI, what is the foundation for governance? A Digital Twin of your organisation according to LEXIM is the foundational environment and structure for applying governance to business-critical data, thereby delivering value from data by design and default.” 

Meanwhile, Norbert Hofmeester, Digital Transformation Expert, focuses on the rise of agentic AI:

“Agentic AI thrives on the interplay of rich context and clear processes, yet it’s data governance that ensures data quality remains the heartbeat of it all. Context and process info give agentic AI the wings to act smartly, while data quality, guided by solid data governance, keeps it grounded and trustworthy.” 

What Comes Next?

From my perspective, the future looks bright for the Netherlands. 

The country is well-positioned to lead in ethical and scalable AI, but to realise this potential, data governance must be embedded in organisational culture, strategy, and operations. AI’s evolving nature and its potential to amplify bias presents new risks that demand targeted governance.

Research suggests that effective governance of AI includes priorities like transparency, fairness, and explainability, grounded in trusted pillars such as data quality, stewardship and compliance.

Data and analytics leaders should begin by asking:

• Do we have clear oversight of our data and AI models?
• Are we equipping our teams with the knowledge to make responsible decisions?
• Are we balancing innovation with risk management?

Organisations that can confidently answer ‘yes’ are better prepared to deliver AI that is trustworthy, responsible and aligned with their goals.

Interested in exploring how Harnham can help your organisation strengthen its data governance for AI success? Get in touch with our team.

Sources:

Harvard Business Review
Forbes Council: Before You Can Trust AI and ML
Forbes Tech Council: Beyond the Algorithm
Data Governance & Quality
Gartner Research: AI