Data Quality Management
High-quality data is like clean drinking water—essential, trusted, and foundational to everything else.
Definition
Data Quality Management focuses on ensuring that data is accurate, complete, timely, consistent, and relevant for its intended use. It includes measurement, monitoring, and remediation processes that build trust in enterprise data.
- Ensures data is fit for purpose and free of defects
- Improves trust in analytics, operations, and reporting
- Involves monitoring, remediation, and prevention
Core Components
- Data Quality Dimensions (e.g., accuracy, completeness)
- Data Profiling and Rule Definition
- Data Cleansing and Enrichment
- Issue Detection and Root Cause Analysis
- Quality Monitoring and Scorecards
Example in Practice
A financial services firm applies data quality rules to client records, flagging missing tax IDs, duplicate accounts, and outdated contact details. A dashboard tracks remediation progress and reports monthly quality trends to leadership.
Key Techniques & Tools
- Informatica Data Quality, Ataccama, Talend
- Collibra DQ, Dataedo, SAS
- Data profiling and cleansing utilities
- Automated validation and exception workflows
Upcoming Events & Content
February 12, 2026: "Beyond Clean: Embedding Quality into the Data Lifecycle"
Who Should Attend: Data stewards, governance leads, analytics professionals, operations managers
Vendor Partners in this Space
DAMA International (DAMA-I) is a not-for-profit, vendor independent association of technical and business professionals dedicated to advancing the concepts and practices for data resource management and enterprise information. DAMA-I is the parent organization of the DAMA-MN, and has chapters and members throughout the world.