Data Monitoring / Pipeline Freshness & Data Quality
Data pipelines: Data quality exception ownership
Quality failures accumulate with no clear responder. Record dataset owner, rule intent, failed record scope and escalation deadline.
Environment and inputs: Data pipelines and analytical datasets; Apache Airflow is an orchestration example. Quality acceptance rules must match the dataset contract.
Failure scenario
Quality failures accumulate with no clear responder. Confirm the cause before choosing a fix.
Evidence to collect
Record dataset owner, rule intent, failed record scope and escalation deadline. Record the time, affected scope and a healthy comparison interval.
Fix the confirmed cause
Route each exception to an accountable owner with a visible resolution or approved waiver. Write the expected result first. Change one relevant setting at a time.
Check the result and hand over
Repeat the check for data quality exception ownership using the same scope and a representative interval. Check that the symptom is resolved, expected records are present and downstream results are correct. If coverage, correctness or latency worsens, restore the recorded configuration through the agreed rollback process.
Scope and precautions
Prefer read-only access and limit queries to the relevant period or dataset. This example has not been tested on your systems. Test changes in an approved environment. Keep credentials and sensitive data out of shared evidence.