Data Quality Assessment
Evaluates dataset readiness across core quality dimensions and generates actionable scorecards, defect rankings, and remediation plans.
Data Quality Assessment provides a structured framework aligned with DMBOK standards to evaluate datasets across completeness, validity, consistency, timeliness, and uniqueness. It enables data engineers, analysts, and governance leads to systematically validate data products against business tolerances, quantify defect rates, assign remediation ownership, and generate clear Go/No-Go recommendations prior to production deployment or analytical consumption.
主な機能
010 GitHub stars
02Reproducible check definition tied to explicit business validation rules
03DMBOK-aligned evaluation across completeness, validity, consistency, timeliness, and uniqueness
04Standardized Data Quality Scorecard generation with Go/No-Go verdicts
05Defect rate quantification mapped directly to business impact and tolerances
06Material defect triage with severity classification, ownership, and target resolution ETAs
ユースケース
01Conducting dataset readiness reviews prior to analytics or operational system releases
02Generating governance scorecards and prioritizing data remediation backlogs
03Establishing pre-flight validation checkpoints for machine learning feature pipelines