1.2.0

Data quality validation patterns for daily checks and anomaly follow-up.

Data Quality Operations

Use when dataset freshness/completeness checks must be run consistently.

Inputs to Gather

• Primary target (service, team, or dataset)
• Current impact and urgency
• Assigned owner and deadline

Core Commands

• dq profile --dataset <name>
• dq validate --rule-set <id>
• dq anomaly --open --metric <name>
• workflow checklist --from templates/checklist.md
• workflow report --from templates/report.md

Operating Notes

• Prefer explicit owner assignment before action.
• Keep timeline notes concise and timestamped.
• Save output artifacts for audit and handoff.
• This version adds a structured report template for post-task summaries.

Version marker: data-quality-operations 1.2.0