Data Quality

This covers the operational side of data quality programs in financial services: how to set up golden source designations, write validation rules for pricing and client data, build exception management workflows, and track data lineage for regulations like BCBS 239. It walks through the six quality dimensions (accuracy, completeness, timeliness, consistency, validity, uniqueness) with financial data examples, which is helpful when you're actually implementing scorecards or investigating why reconciliation keeps breaking. The regulatory angle is strong, covering what examiners look for and what reporting standards require. Use it when you're building data pipelines that feed compliance, billing, or portfolio systems where bad data creates expensive downstream problems.

skills.sh installs: 745
GitHub stars: 224
last updated: Aug 21, 2026

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Install

$ npx -y skills add joellewis/finance_skills --skill data-quality --agent claude-code

Installs into .claude/skills of the current project.

Files

Data Quality

Core Concepts

1. Data Quality Dimensions for Financial Data

Six dimensions define data quality. Each has domain-specific meaning in financial services.

Accuracy — Data values correctly represent the real-world entity or event they describe. A security price is accurate if it reflects the actual market closing price or evaluated value from the designated source. A client address is accurate if it matches the client's current legal address of record. Accuracy failures propagate: an inaccurate price produces inaccurate valuations, performance, billing, and regulatory reports. Accuracy is measured by comparing data against an independent authoritative source — cross-vendor price comparison, custodian-to-PMS reconciliation, client confirmation of personal data. In practice, accuracy is the hardest dimension to measure because it requires an independent reference point for comparison.

SKILL.md on GitHub