Data governance is one of those terms that can spark both nods of agreement and eye-rolls in the same room. On paper, it’s about ensuring quality, consistency, security, and compliance. In practice, it can either enable teams to trust and use data, or slow them down with endless policies, committees, and approval cycles.

For companies building toward a modern data platform, the question is not Do we need governance? (the answer is always yes), but rather How much governance, and when?

The case for early Governance

Even at the start of the journey, some governance is essential:

• Clear ownership: Who owns which data sources, and who to ask for context?
• Basic standards: Naming conventions, data definitions, and agreed KPIs.
• Access rules: Guardrails around sensitive data to stay compliant.

Without these, platforms quickly become data swamps, where even the best pipelines can’t save teams from confusion and mistrust. Early governance lays the foundation for scaling.

The risk of too much, too soon

Going full Gartner mode with a 200-page governance framework and a fleet of data stewards in a company that’s still centralising it’s sources is a recipe for frustration. Over-engineering governance can:

• Slow delivery: Teams get bogged down in process rather than execution.
• Create bottlenecks: Every new dataset or report requires approvals, delaying insights.
• Waste resources: Appointing stewards and committees before there is enough data maturity to justify them.

In short, too much governance too early can paralyze the very delivery needed to prove the value of data in the first place.

Striking the balance: Practical Governance

The real question is: what is just enough governance for your stage of maturity?

• Early stage (building foundations)

Focus on lightweight standards: glossary of key metrics, ownership mapping, and access policies. No committees, just clarity.

• Growth stage (scaling platform & users)

Introduce data stewards or champions, build simple governance workflows (dataset approvals, change logs), and expand your glossary into a business dictionary.

• Mature stage (enterprise-level complexity)

Formalise governance councils, implement stewardship roles, and align with compliance and audit frameworks. At this point, more structure adds value rather than friction.

The COST question

Every governance decision has a cost: additional people, slower cycles, or stricter controls. The key is asking:

• Will this governance practice improve trust and adoption of data?
• Does it reduce risk in proportion to the effort?
• Does it enable or block delivery?

If the cost outweighs the benefit, its not the right time to implement.

In closing

Governance is not all-or-nothing. It should evolve with your data platform maturity, starting small, proving value, and expanding as complexity demands. Go too heavy too soon, and governance becomes a handbrake. Keep it practical, and it becomes a steering wheel, guiding your data strategy without slowing delivery.