Develop Data Quality Assessment Plan

1. Replace placeholders like \[dataset or database name] with your specific dataset or system.
2. Specify the business context or domain if relevant to make the output more actionable.
3. Review the generated plan and adjust KPIs or thresholds according to organizational standards.
4. Use iterative prompts to refine sections (e.g., focus separately on metrics, tools, or roles).
5. Avoid overly broad datasets; specify dataset size or type for more practical recommendations.
6. Double-check recommendations for feasibility before implementing in production.

Establishing a formal data governance framework
Evaluating the quality of new datasets before integration
Preparing for regulatory compliance audits
Improving accuracy in business intelligence reports
Reducing errors in customer or transactional data
Benchmarking data quality across departments
Creating repeatable quality monitoring procedures
Training staff on data quality standards

Customize KPIs to reflect business priorities and risk tolerance.
Include examples of common data issues specific to your domain.
Use clear, measurable thresholds to make the plan actionable.
Review and update the plan periodically as datasets evolve.
Consider including automated data quality monitoring tools to increase efficiency.