Why data quality matters more than it looks
The gap between knowing this and actually doing it is where most teams lose ground. Data quality is easy to treat as a detail, and that is exactly why it is worth a few minutes of attention.
Most data problems are ownership problems that turned into technical ones. Budget a little time for it every quarter and it never becomes a project of its own.
The reason this keeps coming up
Bad data spreads faster than anyone corrects it. Getting it slightly wrong is survivable. Ignoring it entirely is not. Most teams find the first pass takes an afternoon and the maintenance takes minutes a month.
Automated checks catch drift that eyeballs miss. It is worth being explicit about, because assumptions differ quietly. Write the reasoning down alongside the decision, because the reasoning is what changes first.
Common failure modes
Someone must own each dataset or nobody does. It is worth being explicit about, because assumptions differ quietly. Write the reasoning down alongside the decision, because the reasoning is what changes first.
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
Pick the one that would hurt most if it failed, and start there.