# Data Quality Controls

> Data Quality Controls

- Skill: `bkjohn2018/data-quality-controls-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add bkjohn2018/data-quality-controls-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/bkjohn2018/data-quality-controls-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: bkjohn2018 (https://skillmd.com/u/bkjohn2018)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/bkjohn2018/data-quality-controls-2

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# Data Quality Controls

## Purpose
Provide guidance on defining data quality controls for analytics governance.

## Scope
This skill covers data quality requirements for analytics packages, including:
- source validation
- completeness checks
- accuracy controls
- issue management

Before drafting governance documentation, check and apply:
.github/skills/finance-documentation-lifecycle

Use it as the source of truth for ISO 9001-inspired terminology, process/procedure/SOP distinctions, documentation lifecycle, and evidence/record expectations.

## Control rules
- Every policy requirement must map to a control point.
- Every control point must map to an SOP step.
- Every SOP step must generate evidence.
- Define thresholds and escalation paths for quality failures.
- Use data profiling and reconciliation checks.

## Structure
1. Quality objective
2. Control description
3. Data source
4. Validation method
5. Thresholds
6. Escalation path
7. Evidence produced

## Output expectations
- Define controls in a table format.
- Include both automated and manual checks where needed.
- Document ownership for each control.
- Keep controls aligned with governance requirements.