Five mistakes teams make with data modelling

We end up explaining this on discovery calls often enough that it deserved writing down. These are the ones we run into repeatedly when we audit data modelling.

Numbers get quoted in meetings long after anyone remembers how they were calculated. Assume whoever inherits this will have half your context and none of your patience.

Common failure modes

• Treating it as a launch task rather than an ongoing one
• Assuming someone else already owns it
• The model outlives the application built on top of it
• Name things the way the business names them
• Never checking whether the fix actually worked

Model what is true, not what is convenient this quarter. Getting it slightly wrong is survivable. Ignoring it entirely is not. If two people in the business would answer this differently, that gap is the actual problem.

Where to go from here

The short version

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

• Someone can say what the current setup is without going to look
• The model outlives the application built on top of 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.