data-quality-management
Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- data-quality-management
- description
- Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.
03 SKILL.md 正文
Data Quality Management
Overview
Data quality management treats quality as an ongoing product property: define what “good” means, measure it, fix root causes, and prevent recurrence.
When to Use
- Chronic dashboard or model distrust
- Regulatory or financial data accuracy requirements
- Scaling data products beyond hero analysts
- Defining SLAs for critical tables
Core Practices
- Define quality dimensions: accuracy, completeness, consistency, timeliness, uniqueness, validity
- Set rules and thresholds per critical field/table
- Measure continuously; publish quality scores where useful
- Triage incidents: severity, owner, SLA
- Fix upstream root causes, not only downstream patches
- Prevent via contracts, validation at entry, and producer accountability
Principles
- Quality is defined relative to purpose — bank-grade ≠ blog-grade
- Detection without remediation is noise
- Producers must own quality of what they emit
- Perfect quality on unimportant data is waste
Verification
- [ ] Critical data has explicit quality rules
- [ ] Incidents have owners and closure tracking
- [ ] Upstream fixes are preferred over perpetual cleanup
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