Data Governance Autopilot

Problem

Data governance often stalls because metadata is scattered, lineage is missing, and quality rules are maintained manually. Documentation drifts after schema changes, sensitive fields rely on heuristic labels, and audit evidence is assembled late. This skill turns governance into an executable pipeline with YAML, collectors, checkers, and audit artifacts instead of documentation alone.

How It Works

• Metadata and lineage: multi-source collectors scan schemas, comments, and storage statistics; --incremental collects only changed tables. Automatic field/table lineage inference covers integration paths.
• Quality checks: six-dimensional rules define constraints such as severity: critical; the engine runs checks and produces scores, helping maintain rules by priority.
• Sensitive data: an AI classification engine grades sensitive fields. L3 partial masking preserves analytical value, while L4/L5 can use Tokenization instead of Masking.
• Audit output: generates compliance reports and maps DAMA domains, showing coverage for data architecture, security, metadata, and quality.

Boundaries

The skill focuses on architecture, modeling, security, quality, and metadata automation. Storage, integration, and master data are only partially covered. Classification accuracy depends on --threshold; mislabels may need manual feedback. Masking choices should balance downstream analytics.