Data Quality Is Not Decision Quality

Why better measurement often creates false confidence

Most organizations have invested heavily in data quality.

Definitions are standardized. Pipelines are monitored. Single sources of truth are declared. From a technical perspective, the data estate looks healthy.

Yet decision quality often remains inconsistent.

First principle: a report is not a decision

A report represents the world.
A decision commits the organization to act in the world.

They obey different rules.

A report can be correct and still irrelevant.
A decision can be reasonable and still wrong.
A report can be late and acceptable.
A decision can be late and disastrous.

Measuring reports does not measure decisions.

The truth fallacy

Executives often talk about “the truth” in data.

But decisions do not operate on truth.
They operate on thresholds.

Risk feels high enough.
Opportunity feels large enough.
Confidence feels sufficient.

These thresholds are shaped by incentives, accountability, and pressure. Data informs them, but does not control them.

This is why perfect data does not guarantee consistent decisions.

The hidden system: interpretation

Between data and decision sits interpretation.

Interpretation includes:

• what the dashboard emphasizes
• which metric is shown first
• what is highlighted or ignored
• who is in the meeting
• what caveats are dismissed
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Interpretation is the real decision interface.

You can have perfect data and still make poor decisions if interpretation distorts intent.

How dashboards create false confidence

Dashboards often:

• reward explanation over ownership
• encourage hindsight reasoning
• hide where decisions actually execute
• shift attention to what is measurable, not what is material
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Leadership feels informed. Exposure remains.

Influence is the missing KPI

If you want decision quality, measure influence.

For any insight, ask:

• Did it change a decision?
• Which one?
• Who owned that decision?
• Was it executed as intended?
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Most organizations measure consumption. Very few measure causality.

The defensibility test

Decision quality at C-suite level is not accuracy.
It is defensibility.

Could the decision be defended given the information available at the time?

That is how boards and regulators judge decisions. Not by dashboards.

The executive question

If your dashboards disappeared tomorrow, would your most important decisions degrade?

If yes, you have dependency.
If no, you may have irrelevance.

Both answers are instructive.

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