Data accuracy and governance are often presented to boards as sprawling disciplines, wrapped in frameworks, tooling, and technical language. That complexity can obscure a simpler truth. Applying Occam’s razor, the most effective lens is this: if the organisation cannot trust its data, it cannot trust its decisions. Everything else is secondary.
At board level, data accuracy is not a technical hygiene issue. It is a question of operational integrity and strategic credibility. Governance, in turn, is not about policies sitting in repositories. It is about whether the organisation has the discipline and accountability to ensure that data remains reliable across its lifecycle.
Strip it back. There are only three questions that matter.
First, can we rely on the data used to make material decisions? If revenue forecasts, customer insights, or risk models are built on inconsistent or outdated data, the organisation is exposed. Not in theory, but in immediate commercial terms. Poor data accuracy leads to mispriced products, flawed strategy, and misplaced investment. The board should view this as a direct threat to performance, not a back-office concern.
Second, do we know where our critical data resides and how it flows? Governance fails quietly when data estates become opaque. If the organisation cannot clearly articulate where personal and operationally sensitive data sits, who uses it, and for what purpose, then control is already weakened. In that state, compliance becomes reactive, and risk accumulates in places that are not visible until something breaks.
Third, is accountability for data ownership real or performative? Many organisations claim to have data owners, stewards, and governance forums. The test is whether those roles have authority and are held to account. If no one is clearly responsible for data quality at source, issues propagate downstream and multiply. By the time they surface, they are expensive to correct and reputationally damaging.
From a governance perspective, effectiveness is best judged not by the volume of controls, but by their ability to sustain trust in data over time. Boards should be cautious of over-engineered frameworks that promise completeness but deliver little in practice. Complexity often signals a lack of clarity. A simpler model, well enforced, will outperform an elaborate one that is inconsistently applied.
There is also a tendency to separate data accuracy from risk management. That is a mistake. Inaccurate data is itself a risk multiplier. It distorts reporting, masks emerging issues, and undermines assurance. A control environment built on weak data cannot provide credible oversight. The board should therefore expect data accuracy to be embedded within the broader risk framework, not treated as a parallel initiative.
Technology will be presented as a solution, and it has a role. However, no tooling can compensate for weak governance. Systems can automate validation and flag anomalies, but they cannot create accountability or enforce discipline. Those are organisational choices. Investment decisions should reflect that balance. Technology should enable governance, not substitute for it.
The forward view is equally important. As organisations increase their reliance on data driven models, including artificial intelligence, the cost of inaccuracy compounds. Errors are not contained, they are scaled. Governance must therefore evolve from periodic review to continuous oversight. The question is no longer whether data is accurate at a point in time, but whether the organisation has the capability to maintain accuracy as data volumes and uses expand.
In practical terms, boards should expect clear signal lines. Where is data accuracy strongest, where is it weakest, and what is being done about it. What is the trend? Are issues being resolved at source or repeatedly managed downstream? What decisions are being made on the basis of known imperfect data, and what is the risk appetite around that?
Occam’s razor cuts through the noise. Effective data governance is not about doing more. It is about doing the essential things well. Know your critical data. Assign real ownership. Maintain accuracy at source. Monitor relentlessly. If those fundamentals hold, the organisation can trust its data and act with confidence. If they do not, no amount of framework or reporting will compensate.
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Michael Irene, CIPM, CIPP(E) certification, is a data and information governance practitioner based in London, United Kingdom. He is also a Fellow of Higher Education Academy, UK, and can be reached via moshoke@yahoo.com; twitter: @moshoke
Data governance is about trust, not frameworks