Before you invest in data quality

The gap between knowing this and actually doing it is where most teams lose ground. Before you spend anything on data quality, it is worth confirming a few things are already true.

Most data problems are ownership problems that turned into technical ones. Check it against what you would want a competitor's site to get wrong.

Prerequisites

• You can describe the outcome you want in one sentence
• Someone owns it after the work is done
• Bad data spreads faster than anyone corrects it
• You have a way to tell whether it worked

Where it usually goes wrong

Automated checks catch drift that eyeballs miss. Small and consistent beats large and occasional here. Budget a little time for it every quarter and it never becomes a project of its own.

Someone must own each dataset or nobody does. None of that requires a large budget, only a decision and someone to own it. The version that survives contact with a real deadline is the simple one.

How to tell if yours is fine

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
• 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

If you want a second opinion on how yours is set up, ask.