Data Quality Monitoring
Alerts on distribution shift as well as on nulls, since silent skew is the expensive failure.
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Running it, start to finish
- Add distribution checks alongside the completeness ones.
- Check volume composition, not just the total.
- Verify each past incident would have been caught.
What you get back
The output this produces, every time.
- Monitors distributions, catching data that arrives valid and wrong.
- Checks volume by segment, since a healthy total can conceal one source failing entirely.
- Tests the design against past incidents, which is the only honest measure of coverage.
Getting better results
Where this usually goes wrong, and how to avoid it.
- Watch for unit changes. A field that starts arriving in different units passes every completeness check and corrupts everything downstream.
- Decide blocking per check. Blocking on non-critical checks produces a pipeline people disable, which removes all the checks at once.
- Set thresholds from historic variation. Intuition produces alerts on normal fluctuation, and people learn to ignore the channel within weeks.
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Written for The AI University. Every prompt in this library is original work — authored, tested and revised here, not collected from elsewhere. 365 of them, free with an account.