The Validation Rule Is Working. The Business Process Is Not.

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 continues, the Data Owner becomes involved.

But when the same pattern keeps returning for days or weeks, repeatedly correcting the result is no longer enough.

The question changes from:

“Why is this value wrong?”

to:

“Why does this process keep producing the wrong value?”

That distinction matters.

A validation rule is not only a mechanism for detecting bad data.

In a mature Data Governance environment, it can also become a sensor for how well a business process is supporting data quality.

But there is an important boundary here.

Data Governance does not manage every business process.

If a department decides that twenty hours of manual work every month is acceptable and the resulting data is accurate, DG should not become the manager of that department and tell it how to organize its work.

That is a business decision.

From a Data Governance perspective, the primary question remains:

Is the data accurate and maintained according to the agreed standard?

Still, recurring corrections can reveal an opportunity.

If a Data Steward has to repair the same type of error every day, Data Governance can help move the control closer to the source.

Instead of detecting the problem after the data has already entered the system, perhaps the error can be prevented before it happens.

Not by automatically correcting the data.

For important business data, someone should remain accountable for what is entered.

Automation can detect and prevent.

It should not remove accountability.

The Data Owner or Data Steward understands what the value means at the point of entry.

Data Governance brings another perspective: where that value travels afterwards.

The same field may later feed operational decisions, management reporting, KPIs or several downstream systems.

That downstream context can help the business design a better validation at the source.

There is also another dimension that becomes possible as Data Governance matures.

In the beginning, Data Quality usually has to operate at a very detailed level.

A specific table.

A specific field.

A specific rule.

A specific Data Steward.

When an organization has thousands of unresolved issues, aggregating everything into a high-level “Sales Data Quality” score can hide more than it explains.

You first need to know exactly where the problem is.

But over time, the picture can change.

The number of errors falls.

Ownership becomes established.

People understand why Data Governance matters.

Data discussions become a normal part of the business.

At that point, individual technical controls can start to tell a broader story.

Instead of only reporting:

“Table X generated 120 errors.”

you can begin to understand:

“Product creation generated 120 errors and required ten hours of correction this month.”

For a mature organization, this can be useful information.

Time is one of the most valuable resources in modern business.

If a recurring data issue consumes hours of manual work, the validation framework can help quantify that impact.

But even then, Data Governance provides the evidence.

The business decides whether changing the process is worth it.

And maturity changes the controls themselves.

A validation rule that continuously detects problems may need to run every day.

A rule that has produced no errors for months may still be valuable as a safety net, but perhaps it only needs to run monthly or quarterly, depending on technical cost and business criticality.

A rule does not become useless simply because the data is finally clean.

Sometimes the fact that it finds nothing is exactly what you want.

Good Data Governance therefore does more than count errors.

It learns from them.

First, it identifies exactly where the data is wrong.

Then it helps establish accountability.

And when the same problem keeps returning, it asks whether the control can move closer to the point where the error is created.

The validation rule may be working perfectly.

The real value comes when the organization starts listening to what it is telling them.