Checklist

Data Quality Review Template

Interactive checklist to assess dataset completeness, consistency, provenance, access controls, and remediation readiness before analysis. Saves findings so teams can track issues and follow up.

Interactive Tool

Data Quality Review

Use this checklist to assess dataset readiness for analysis and reproducible research. For each check, select a status and add concise evidence or remediation notes. Save the review to build organizational memory, prioritize fixes, and schedule follow-up.

YYYY-MM-DD
Person completing the review
Canonical name or identifier
Branch, tag, timestamp, or version id
e.g., database name, file path, API
Are expected fields present and documented?
List missing or unexpected fields, column dictionary references, or sample rows.
Do missing-value patterns or outliers threaten analysis?
Summarize missingness rates, example outliers, thresholds, and any exploratory charts or queries used.
Are values consistent across sources, joins, or expected canonical references?
Describe mismatches, affected keys, and reconciliation steps or scripts.
Is data provenance documented and reproducible (ingestion, transformations, versions)?
Point to ETL scripts, notebook versions, ingestion logs, or reproducible build steps.
Are permissions, encryption, masking, and PII handling appropriate for intended analysis?
List detected sensitive fields, current controls, and recommended privacy actions.
Is there a documented process to flag issues, assign owners, and track remediation?
Link to incident tickets, owners, SLAs, or automated alerting rules.
For each issue include owner, impact, and suggested remediation.
Estimate overall risk if dataset used as-is.
Actionable tasks, scripts to run, owners, and quick checks to reduce risk.
YYYY-MM-DD — when remediation should be rechecked.
URLs, dataset snapshots, query IDs, or notebook references.
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