Data quality management is crucial for ensuring data accuracy, completeness, consistency, and reliability. Assessing data quality helps identify data issues, improve data integrity, and enable data-driven decision-making.
What needs to be assessed
Data quality rules and standards
Data quality assessment processes and tools
Data cleansing and remediation procedures
Data quality monitoring and reporting mechanisms
Are data quality rules and standards defined and implemented across the organization?
Is there a systematic process for data quality assessment and monitoring?
Are data cleansing and remediation procedures in place to address data quality issues?
Key Performance Indicators (KPIs) for maturity
Data accuracy, completeness, and consistency metrics
Reduction in data quality issues or errors
Timeliness and effectiveness of data cleansing and remediation activities