data-quality-finance
Improve financial data quality — definitions, lineage, reconciliations, and ownership — so reporting and models are trustworthy. Use when numbers conflict across systems, metrics are disputed, or automation is blocked by bad data.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- data-quality-finance
- description
- Improve financial data quality — definitions, lineage, reconciliations, and ownership — so reporting and models are trustworthy. Use when numbers conflict across systems, metrics are disputed, or automation is blocked by bad data.
03 SKILL.md 正文
Data Quality (Finance)
Overview
Finance decisions inherit data quality. Conflicting metrics, broken reconciliations, and unclear ownership create noise and politics.
When to Use
- Conflicting reports from different systems
- Metric definition disputes
- Pre-automation data cleanup
- Audit findings on data integrity
Core Practices
- Define canonical sources for key metrics
- Document data lineage for material reports
- Reconcile critical subledgers to GL on a cadence
- Assign data owners for master data domains
- Fix root causes, not only report patches
Principles
- One source of truth per metric where possible
- Manual journal adjustments that “fix” reports are a smell
- Quality is a process, not a one-time cleanse
- Incentives to game metrics will surface in the data
Verification
- [ ] Critical metrics have canonical definitions and sources
- [ ] Material reconciliations are routine
- [ ] Data issues have owners and remediation paths
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