The cost of bad data
A single duplicated record or a silently changed schema can skew a metric enough to drive a wrong decision. The damage is rarely loud—it's a slow erosion of trust in the numbers.
What we check
- Freshness: did the data arrive on time?
- Completeness: are required fields populated?
- Validity: do values fall in expected ranges?
- Uniqueness: no accidental duplicates.
- Consistency: totals reconcile across tables.
Make it automatic
Quality tests run on every pipeline run, with alerts to a named owner. Catching issues at ingestion is far cheaper than explaining a wrong board‑meeting chart after the fact.
