The organisation creates sophisticated data governance structures without changing the behaviours that determine data quality. Poor information practices then become institutionalised inside apparently compliant processes, giving decision-makers confidence in information that may be incomplete, inconsistent or unsuitable for the decisions being made.
- Every piece of asset information begins with human behaviour, because someone ultimately creates, modifies, validates or interprets the data.
- Accountability needs to exist at the point of data creation so people understand the consequences of the information they enter or maintain.
- Mandatory fields don’t automatically produce useful information when people don’t understand why the information is required or how it will be used.
- Data ownership needs to translate into genuine accountability, not simply assigning names to a governance matrix.
- KPIs influence data behaviour because people naturally respond to what the organisation measures, rewards and scrutinises.
- Poor data frequently originates in poor process design when the easiest way to complete a task does not produce the information the organisation actually needs.
- Information requirements should connect directly to decisions so people can understand why data quality matters beyond administrative compliance.
Real World
The global PLM implementation brought this home to me. We were attempting to standardise information across five regions, 55 countries and 18 languages under a zero-customisation philosophy. You can create naming conventions, master-data rules, workflows and governance structures, but ultimately somebody has to enter information correctly, somebody has to maintain it, somebody has to challenge it when it looks wrong. Somebody has to accept accountability for the decisions subsequently made from it.
That is why I increasingly see data governance as behavioural governance. The technical architecture matters, but governance becomes real through thousands of small human actions. Do I use the agreed definition or invent my own? Do I correct a record I know is wrong? Do I keep my own private dataset because I don’t trust the enterprise source? Do I challenge a number in a meeting or quietly work around it? Those behaviours determine the quality of organisational information long after the data-governance framework has been approved. The question isn’t simply whether an organisation has data standards. It is whether people behave as though those standards matter.
Change Management Perspective
I approach data governance as much a behavioural issue as a technical one. Every data field ultimately has a human interaction behind it: someone creates it, validates it, changes it, interprets it or makes a decision from it. If people don’t understand why the information matters, adding another mandatory field or governance procedure rarely solves the problem.
I would connect information requirements directly to the decisions they enable and make accountability visible where information is created and used. When people understand who depends on their information, why it matters, and what happens when it is wrong, data governance becomes part of normal organisational behaviour rather than an administrative requirement.
Key Takeaway
Every data-quality problem eventually leads back to a behaviour, process or decision.
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