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A professional-level summary covering key definitions, frameworks, and exam-relevant points.
The Six Data Quality Dimensions (DMBOK)
The DAMA DMBOK v2 defines six core dimensions of data quality that provide a framework for measuring and improving data quality:
| Dimension | Definition | Example Metric |
|---|---|---|
| Accuracy | Data correctly represents the real-world entity or event | % of customer addresses that match postal records |
| Completeness | All required data is present | % of customer records with a valid email address |
| Consistency | Data is the same across all systems and contexts | % of customer records with matching name across CRM and billing |
| Timeliness | Data is available when needed and reflects current reality | Average age of customer address data |
| Validity | Data conforms to defined formats, ranges, and business rules | % of dates in valid format; % of postcodes matching valid pattern |
| Uniqueness | No unintended duplicates exist | % of customer records that are unique (no duplicates) |
Data Quality Improvement Approaches
The DMBOK distinguishes between reactive approaches (cleansing data after quality issues are detected) and proactive approaches (preventing quality issues from occurring through process improvement, validation rules, and governance). Sustainable data quality requires both, but the emphasis should shift over time from reactive to proactive as governance matures.
CDMP Exam Relevance
Data quality improvement is one of the most heavily tested areas in the CDMP exam. Key topics: the six quality dimensions, the distinction between reactive and proactive approaches, root cause analysis, data profiling, and the role of Data Stewards in maintaining quality. The CDMP exam frequently tests whether candidates understand that data quality is a business responsibility, not just an IT responsibility.