data-governance-framework

name	data-governance-framework
enabled	true
description	Use when performing data governance framework — establishes a comprehensive data governance framework covering data ownership, quality standards, classification, lineage tracking, access policies, and stewardship roles. Produces actionable policies and procedures to ensure data is managed as a strategic organizational asset.
required_connections	[{"prefix":"data-catalog","label":"Data Catalog Platform"}]
config_fields	[{"key":"organization_name","label":"Organization Name","required":true,"placeholder":"e.g., Acme Corp"},{"key":"data_domains","label":"Primary Data Domains","required":true,"placeholder":"e.g., Customer, Product, Financial, Operational"},{"key":"regulatory_requirements","label":"Regulatory Requirements","required":false,"placeholder":"e.g., GDPR, CCPA, HIPAA, SOX"}]
features	["DATA","GOVERNANCE","COMPLIANCE"]

Data Governance Framework

Phase 1: Governance Structure

1. Define governance organization
   • Data governance council (executive sponsors)
   • Data domain owners (business leaders per domain)
   • Data stewards (operational responsibility per domain)
   • Data engineers (technical implementation)
   • Data protection officer (privacy and compliance)
2. Define decision rights and escalation paths
3. Establish governance meeting cadence
4. Create governance charter and mandate

RACI Matrix

Activity	Council	Domain Owner	Data Steward	Data Engineer	DPO
Policy creation	A	R	C	I	C
Data classification	I	A	R	C	C
Quality standards	I	A	R	C	I
Access approval	I	A	R	I	C
Compliance monitoring	A	I	R	C	R
Issue resolution	A	R	C	C	C

Phase 2: Data Classification & Inventory

1. Define data classification levels
   • Public - no restrictions
   • Internal - organization-wide access
   • Confidential - role-based access
   • Restricted - strict access controls (PII, PHI, financial)
2. Inventory and classify data assets
3. Document data lineage (source to consumption)
4. Map data to regulatory requirements

Data Asset Inventory

Domain	Dataset	Classification	Owner	Steward	Regulatory	Quality Score
		Public/Internal/Confidential/Restricted			GDPR/CCPA/HIPAA/None	/100

Phase 3: Data Quality Standards

1. Define data quality dimensions
   • Completeness - required fields populated
   • Accuracy - values reflect reality
   • Consistency - same data matches across systems
   • Timeliness - data available when needed
   • Uniqueness - no unintended duplicates
   • Validity - values conform to expected formats
2. Set quality thresholds per domain
3. Implement automated quality monitoring
4. Define data quality incident process

Quality Thresholds

Domain	Completeness	Accuracy	Consistency	Timeliness	Overall Target
Customer	> %	> %	> %	< hrs	> %
Product	> %	> %	> %	< hrs	> %
Financial	> %	> %	> %	< hrs	> %

Phase 4: Access & Security Policies

1. Define data access policies
   • Role-based access control (RBAC) for data assets
   • Data access request and approval workflow
   • Sensitive data masking and anonymization rules
   • Data sharing agreements for external parties
   • Data retention and deletion schedules
   • Audit logging for data access
2. Implement technical controls for data protection
3. Define breach notification procedures

Phase 5: Data Lifecycle Management

1. Define lifecycle stages
   • Creation / ingestion standards
   • Storage and retention policies
   • Usage and sharing guidelines
   • Archival procedures
   • Deletion and destruction procedures
2. Implement automated lifecycle enforcement
3. Document data retention schedule per regulation

Phase 6: Monitoring & Continuous Improvement

1. Establish governance metrics
   • Data quality scores by domain (monthly)
   • Policy compliance rate
   • Access review completion rate
   • Data incident count and resolution time
   • Governance adoption rate across teams
2. Conduct quarterly governance reviews
3. Update policies based on regulatory changes

Counter-Rationalizations

Shortcut	Counter	Why
"We can skip some steps for this case"	Adapt the workflow steps, don't skip them	Skipped steps are where incidents and oversights originate
"The user seems to already know what to do"	Complete all workflow phases with the user	The workflow catches blind spots that experience alone misses
"This is a minor case, full process is overkill"	Scale the process down, don't turn it off	Minor cases become major when unstructured; the process scales, not disappears
"I'll fill in the details later"	Complete each section before moving on	Deferred details are forgotten; real-time capture is more accurate
"The template output isn't necessary"	Always produce the structured output format	Structured output enables comparison, audit trails, and handoff to other teams

Output Format

• Governance Charter: Roles, responsibilities, and decision rights
• Data Classification Guide: Levels with handling requirements
• Quality Standards: Thresholds and monitoring procedures
• Access Policy: RBAC model and approval workflows
• Lifecycle Policy: Retention schedules and procedures

Action Items

• Establish data governance council with executive sponsorship
• Appoint domain owners and data stewards
• Classify and inventory all critical data assets
• Define and implement data quality standards
• Publish data access and retention policies
• Set up governance metrics dashboard
• Schedule quarterly governance review meetings