Data quality: what to get right first

It is rarely the thing that gets a project approved, and often the thing that decides how it goes. If you only fix one thing about data quality this quarter, make it the first item below.

Data outlives the applications built on top of it, which is why the model deserves more thought than the screens. The practical test is whether someone new to the project could tell, in a minute, that it had been handled.

Start here

Bad data spreads faster than anyone corrects it. In practice this is a scheduling problem more than a technical one. It rarely shows up as a line item, which is exactly why it slips.

Then this

Automated checks catch drift that eyeballs miss. Where this goes wrong is almost never a lack of knowledge. Most teams find the first pass takes an afternoon and the maintenance takes minutes a month.

Eventually

Someone must own each dataset or nobody does. There is a version of this that is over-engineered, and it is worth avoiding. The teams that stay on top of it are the ones who put it on a calendar rather than a wish list.

How to tell if yours is fine

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

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