Signs it is time to revisit data quality

The gap between knowing this and actually doing it is where most teams lose ground. A few signals that data quality is due some attention.

Data outlives the applications built on top of it, which is why the model deserves more thought than the screens. The teams that stay on top of it are the ones who put it on a calendar rather than a wish list.

The signals

• Nobody can say when it was last reviewed
• The answer depends on who you ask
• Bad data spreads faster than anyone corrects it
• Automated checks catch drift that eyeballs miss

How to approach it

Someone must own each dataset or nobody does. This is the sort of thing that compounds, quietly, in both directions. Anything you cannot measure here, you are deciding by taste, which is fine as long as everyone knows it.

In practice

Numbers get quoted in meetings long after anyone remembers how they were calculated. Three things worth confirming about data quality before you move on:

• Someone can say what the current setup is without going to look
• Automated checks catch drift that eyeballs miss — and you know whether that is true here
• There is a way to tell whether the last change to this helped

Pick the one that would hurt most if it failed, and start there.