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Sometimes the data is completely correct. The dashboard works. The KPI definitions are clear. The lineage is known. The controls are in place. And the business still makes the wrong decision. That does not automatically mean Data Governance failed. This distinction is important because organizations often expect Data Governance to do more than it actually should. The purpose of Data Governance is not to guarantee that every business decision will be successful. Its purpose is

Sometimes a validation rule works perfectly. It detects the right problem. There are no false positives. The logic is correct. And yet the same errors appear again tomorrow. And the day after that. At that point, the validation rule is not failing. It may actually be telling you something much more important. The process behind the data is producing the problem. The first reaction should still be simple. The Data Steward reviews and corrects the data. If the same issue contin

Many organizations appoint Data Owners. The name appears in a governance document. The responsibility is assigned. The role exists on paper. But then the first real conflict happens. A data standard has been agreed. The Data Owner asks the team to correct the data. A manager says there is no time for it. And suddenly the person who supposedly “owns” the data has no authority to do anything. That is where formal ownership stops being enough. For me, the most important role of