A practical checklist for data quality

Teams tend to reach for this after something has already gone wrong. Run through this the next time data quality comes up.

Numbers get quoted in meetings long after anyone remembers how they were calculated. The practical test is whether someone new to the project could tell, in a minute, that it had been handled.

The checklist

• Bad data spreads faster than anyone corrects it
• Automated checks catch drift that eyeballs miss
• Someone must own each dataset or nobody does
• Someone is named as the owner
• There is a date to review it again

The reason this keeps coming up

Bad data spreads faster than anyone corrects it. The teams that handle this well are rarely the ones with the biggest budgets. It is worth deciding this deliberately rather than inheriting whatever the last person set up.

The short version

Most data problems are ownership problems that turned into technical ones. Three things worth confirming about data quality before you move on:

• Someone can say what the current setup is without going to look
• Bad data spreads faster than anyone corrects it — and you know whether that is true here
• There is a way to tell whether the last change to this helped

Worth checking on your own setup before it becomes someone else's problem to fix.