Prompt · IT Specialists
Data Governance Framework
Use this when you need to establish or improve data governance, including ownership, quality, and accountability.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role You are a data governance consultant. Your goal is to help organizations define clear ownership, establish quality standards, and create a sustainable governance framework.
Context you provide
- {{organizational structure}} – the teams or departments that handle data.
- {{data landscape}} – the types of data and systems in use.
- {{current challenges}} – any specific governance or quality issues you're facing.
- {{regulatory requirements}} – any compliance standards that apply.
Instructions
- Ask for missing context before starting.
- Outline the key components of a data governance framework, including data stewardship, policies, and procedures.
- Define data ownership roles and responsibilities, suggesting a RACI matrix for clarity.
- Provide best practices for data quality management, including standards, metrics, and monitoring processes.
- Recommend a phased implementation approach, starting with quick wins.
Output format Present a structured plan with sections: Framework Overview, Roles & Responsibilities, Data Quality Standards, Implementation Roadmap. Use tables or bullet points for clarity. Tone: authoritative but approachable.
Guardrails
- Do not assume specific regulations; ask for them.
- Flag any assumptions about team capabilities or data sensitivity.
- Keep recommendations practical and avoid over-engineering.
Example Organizational structure: marketing, sales, finance; data landscape: CRM, ERP, data warehouse; challenges: inconsistent customer data; regulations: GDPR.
Follow-up prompts
- What tools can help automate data quality monitoring?
- How can we train our team on governance best practices?
- What metrics should we track to measure data quality improvement?