Five mistakes teams make with data quality

This is one of those topics that looks small until it costs you something. These are the ones we run into repeatedly when we audit data quality.

Numbers get quoted in meetings long after anyone remembers how they were calculated. It is the sort of thing that looks like polish right up until it costs you an enquiry.

Warning signs

• Treating it as a launch task rather than an ongoing one
• Assuming someone else already owns it
• Bad data spreads faster than anyone corrects it
• Automated checks catch drift that eyeballs miss
• Never checking whether the fix actually worked

Someone must own each dataset or nobody does. The teams that handle this well are rarely the ones with the biggest budgets. It is the sort of thing that looks like polish right up until it costs you an enquiry.

Turning this into a decision

The short version

Data outlives the applications built on top of it, which is why the model deserves more thought than the screens. 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

If any of that sounds like a description of your current setup, it is fixable.