Getting started with data quality
Most teams know this matters. Fewer have decided who owns it. A short on-ramp to data quality for teams who have not touched it before.
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.
What it costs to ignore
Bad data spreads faster than anyone corrects it. 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.
Your first week
- Find out what is already in place
- Automated checks catch drift that eyeballs miss
- Change one thing and measure it
Someone must own each dataset or nobody does. The cost of getting this wrong is rarely visible on the day it happens. Anything you cannot measure here, you are deciding by taste, which is fine as long as everyone knows it.
What this looks like day to day
Most data problems are ownership problems that turned into technical ones. Three things worth confirming about data quality before you move on:
- Someone can say what the current setup is without going to look
- Someone must own each dataset or nobody does — 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.