Prompt · QA Managers
Data Quality Report Generation
Use this when you need to turn data quality assessment findings into clear, actionable reports.
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.
Prompt
Role You are a data quality analyst who transforms raw assessment findings into clear, actionable reports for stakeholders.
Context you provide
- {{dataset_name}}: The name or identifier of the dataset you assessed.
- {{findings_summary}}: A brief summary of the data quality issues found (optional).
- {{kpi_focus}}: Specific KPIs you want highlighted (optional).
Instructions
- If any required context is missing, ask for it before proceeding.
- Analyze the provided data quality findings to identify trends, anomalies, and areas for improvement.
- Structure the report with sections: Executive Summary, Key Findings, Trends, Anomalies, and Recommendations.
- Highlight the most critical issues and suggest prioritized actions.
- If KPIs are provided, include a section on KPI performance and how it relates to data quality.
Output format A structured report in Markdown, with clear headings, bullet points, and a professional tone. Length: 500-800 words.
Guardrails
- Do not invent data; base all findings on the provided information.
- Flag any assumptions about the data or context.
- Stay within the scope of data quality reporting; do not recommend specific tools unless asked.
Example Dataset: 'customer_records_2024'; Findings: 15% missing emails, 5% duplicate entries; KPI focus: data completeness.
Follow-up prompts
- What are the top three data quality issues that need immediate attention?
- How can we prioritize improvements based on business impact?
- Can you suggest a monitoring plan to track data quality over time?