Gain control of data governance for your data and AI platform in 5 steps

The consequences are familiar. Dashboards are not trusted, AI use cases struggle to scale and teams work in silos. The issue is rarely the technology itself. Cloud environments, storage layers, pipelines and AI services can often be operational within just a few weeks.

In a previous article, we explained how to organise data governance from an organisational perspective. However, a governance framework alone is not enough. The real challenge begins when governance becomes part of the day-to-day operation of a data and AI platform.

How do you ensure that a platform is not only technically sound, but also actively used? Based on our experience in the field, we share five practical steps that can help.

This article series consists of four articles

1. Why you can no longer afford to wait with data governance
2. Effective data governance in 3 steps
3. (Current article) Gain control of data governance for your data and AI platform in 5 steps
4. Data governance in Azure Databricks

Step 1: Identify the data that matters most

A common mistake in data governance is trying to govern everything at once.

Instead, focus on data that:

• is directly used for decision-making, operations or AI applications;
• is business-critical, for example for financial reporting or compliance.

Starting with a clear use case keeps governance practical and relevant. It allows you to demonstrate value more quickly and build support for further expansion.

At Ennatuurlijk, for example, the initial focus was on three domains: operational costs, investments and commercial data. This clear scope quickly demonstrated where the data platform added value in day-to-day operations.

Step 2: Make ownership visible within the platform

A platform only works effectively when it is clear who is responsible for what. Without ownership, questions about definitions and quality remain unresolved between teams, reducing the value of the platform.

In practice, we distinguish three essential roles:

• Data owner: responsible for the meaning, quality and access to data.
• Data steward: manages definitions and monitors data quality from a business perspective.
• Data custodian: responsible for technical delivery and reliability.

For every dataset or data product, clearly define who fulfils each role and make this visible within the platform, for example through a data catalogue. This ensures everyone knows where questions, change requests and access requests should be directed.

In organisations with multiple business domains, this prevents governance from becoming a shared responsibility that ultimately belongs to no one.

Step 3: Make definitions, quality and access explicit

Trust disappears when teams interpret the same data differently.

A well-known example is the definition of an “active customer”. Finance, sales and marketing often use different definitions. The dashboards are technically correct, but the figures do not match.

Make the following explicit:

• Definitions: what exactly does a KPI or business term mean?
• Data quality: what standards apply to completeness and timeliness?
• Access: who can use which data and for what purpose?

This does not need to be overly complex. The goal is to ensure that agreements are easy to find and apply where people actually work.

At Heerema, sensor data from crane vessels only became truly usable once engineers enriched measurements with business context and defined their own quality thresholds. These agreements were built directly into the platform, resulting in an average 30% reduction in time lost due to data quality issues.

Step 4: Create visibility into data lineage and impact

Within a data and AI platform, you need to understand where data originates and what impact changes may have.

This requires:

• Lineage from source system to dashboard or AI model;
• Documentation of source systems, refresh frequency and ownership;
• Visibility into dependencies between data products, dashboards and models.

This increases trust in data, accelerates onboarding and helps prevent unexpected issues within analytics and AI solutions.

Step 5: Embed governance into platform processes

Data governance only works when it becomes part of the way data is developed, managed and used.

Embed governance into existing processes:

• New use cases start with ownership and definition checks;
• Data products comply with agreed quality standards;
• Changes include an impact assessment;
• Access follows a standardised approval process.

This allows governance to support growth rather than slow it down.

Common pitfalls

In practice, we regularly see organisations:

• start with tooling instead of responsibilities;
• make governance too theoretical;
• assign ownership exclusively to IT;
• add metadata and lineage only after implementation;
• focus on documentation rather than adoption.

Conclusion

A data and AI platform only delivers real value when data is reliable, understandable and well organised. This does not require a large-scale governance programme. What it does require is a practical approach that makes governance part of the platform itself.

By focusing on the most important data, making ownership visible, documenting definitions, creating transparency through lineage and integrating governance into platform processes, you can build a platform that is not only technically successful but also widely adopted across the organisation.

Want to know where your organisation stands today? Start with our data maturity scan and discover which next step will have the greatest impact for your organisation.

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