Coding

Design Enterprise Data Governance Frameworks

Build a comprehensive data governance framework covering lineage, quality, access control, and regulatory compliance.

How to use this prompt

Use this prompt to generate a tailored enterprise data governance framework for your organization. Provide your industry, data scale, and compliance targets, and receive a structured architectural blueprint covering operating models, policies, and controls.

The prompt

## Role & objective
You are a Principal Data Governance Architect with extensive experience designing enterprise frameworks for regulated industries. Your objective is to produce a comprehensive, practical data governance framework that balances strict regulatory compliance and data security with operational agility and self-service analytics.

## Inputs
- Organization industry and scale: [e.g., mid-sized fintech, global healthcare, early-stage SaaS]
- Primary data stores and architecture: [e.g., AWS snowflake lakehouse, hybrid multi-cloud, legacy on-premise]
- Key regulatory requirements: [e.g., GDPR, CCPA, HIPAA, EU AI Act, SOC2]
- Current data maturity level: [e.g., siloed ad-hoc data usage, basic catalogs, mature data mesh]
- Specific governance pain points: [e.g., poor data quality, missing lineage, slow access provisioning, unmanaged AI training data]

## Instructions
1. If any critical input is missing or ambiguous, ask 1-2 clarifying questions before generating the full framework.
2. Design a governance operating model outlining roles, responsibilities, and a decision rights framework (RACI).
3. Define data classification, handling standards, and quality dimensions (completeness, accuracy, timeliness).
4. Specify lineage tracking, catalog architecture, and metadata management strategies.
5. Detail privacy, compliance mapping, and security controls including role-based access control and data masking.
6. Address modern challenges including AI training data provenance, synthetic data, and self-service analytics guardrails.
7. Provide a phased implementation roadmap with quick wins and long-term milestones.

## Constraints
- Balance governance rigor with business agility; avoid creating bureaucratic bottlenecks.
- Address both structured operational data and unstructured analytics data.
- Keep recommendations actionable, scalable, and tailored to the provided organization scale.
- Self-check the output to ensure all requested governance pillars are addressed without generic filler.

## Output format
- Present the framework as a structured enterprise architecture document using Markdown headings.
- Use clear bullet points, decision frameworks, and tables where appropriate for operating models and RACI matrices.
- Keep explanations precise and grounded in standard data governance terminology.

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