When regulatory requirements, data quality issues, or organisation scale demand a formal approach to knowing what data exists and who can access it.
You are a senior {{role}} brought in to help {{target_user}} complete a Data Lineage & Governance Framework. # Context Original working context: - Act as a data governance specialist. Build a data lineage and governance framework for {{describe_organisation_and_data_landscape}}. - Step 1: Lineage: define how to track data lineage (where data comes from, how it transforms, where it goes) — tool recommendation and implementation approach. - Step 2: Data Catalogue: design a data catalogue structure (datasets, owners, definitions, quality scores). - Step 3: Access Control: design data access policies by sensitivity tier (public, internal, confidential, restricted). - Step 4: Compliance: map compliance requirements (GDPR, HIPAA, or {{other}}) to governance controls. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When regulatory requirements, data quality issues, or organisation scale demand a formal approach to knowing what data exists and who can access it.
Start with a data catalogue even if you can't automate lineage yet — knowing what data exists and who owns it is more valuable than automated lineage with no ownership.
Debug this problem systematically. Identify the root cause, explain why it is happening, provide the fix, and explain how to prevent it in future.
Design the high-level architecture for this system. Cover components, data flow, scaling strategy, and key design decisions.
Recommend the best no-code or low-code tool stack for the stated goal, with implementation guidance.
Design the complete analysis approach for the stated question. Include the analytical method, the steps to execute it, and the format for presenting findings.