Data Quality Controls

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.