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A professional-level summary covering key definitions, frameworks, and exam-relevant points.
DMBOK Definition
The DAMA DMBOK v2 defines Data Quality as "the planning, implementation, and control of activities that apply quality management techniques to data, in order to assure it is fit for consumption and meets the needs of data consumers." Data Quality Management carries an 11% weighting in the CDMP exam.
The Six DAMA Data Quality Dimensions
| Dimension | Definition | Example Violation |
|---|---|---|
| Accuracy | Data correctly represents the real-world entity | Customer address is outdated |
| Completeness | All required data is present | Email field is blank |
| Consistency | Same data is represented the same way across systems | Date format differs between systems |
| Timeliness | Data is available when needed and sufficiently current | Inventory data is 24 hours old in a real-time system |
| Validity | Data conforms to required format, range, or business rules | Date of birth is 30 February |
| Uniqueness | Each entity is represented only once | Duplicate customer records |
Data Quality Management Activities
The DMBOK identifies the following core activities: data profiling (assessing current data quality), data quality rule definition (establishing measurable standards), data quality monitoring (ongoing measurement against standards), data quality issue management (identifying, prioritising, and resolving issues), and data quality improvement (root cause analysis and process change to prevent recurrence).
Data Quality vs Data Cleansing
A critical CDMP distinction: data cleansing is a reactive, one-time activity (fixing known errors). Data quality management is a proactive, ongoing programme (preventing errors from occurring and monitoring quality continuously). The CDMP exam consistently favours answers that describe data quality management as a programme, not a project.