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A professional-level summary covering key definitions, frameworks, and exam-relevant points.
Data Quality Management: The Six Dimensions Framework
Data quality is defined by DAMA International as the degree to which data meets the requirements of its intended use. The DMBOK identifies six core quality dimensions that together provide a comprehensive framework for assessing and managing data fitness for purpose. This is one of the highest-weighted knowledge areas on the CDMP exam.
The Six Dimensions
| Dimension | Definition | Measurement Approach | Common Violation |
|---|---|---|---|
| Accuracy | Data correctly represents the real-world entity | Comparison to authoritative source | Wrong address, incorrect amount |
| Completeness | All required data values are present | % of non-null values in mandatory fields | Missing phone number, no postcode |
| Consistency | Data is the same across systems and time | Cross-system reconciliation | Different customer name in two systems |
| Timeliness | Data is current and available when needed | Age of data vs. required freshness | Stale address, outdated price |
| Validity | Data conforms to defined formats and business rules | Rule-based validation checks | Invalid date, out-of-range value |
| Uniqueness | Each real-world entity appears only once | Duplicate detection algorithms | Same customer in database twice |
Data Quality Lifecycle
The DMBOK describes data quality management as a lifecycle: Define (establish quality requirements and metrics), Measure (profile and assess current quality), Analyse (identify root causes of quality issues), Improve (remediate issues and implement controls), and Monitor (continuously track quality metrics). This lifecycle is frequently tested in the CDMP exam.
Data Profiling
Data profiling is the analytical process of examining data to understand its structure, content, and quality. It is the primary technique for measuring data quality across all six dimensions. Profiling produces statistics such as null rates, value distributions, uniqueness ratios, and format conformance rates.
CDMP Exam Focus
Candidates should be able to: define each of the six dimensions precisely, identify which dimension is violated in a given scenario, describe the data quality lifecycle, explain the role of data profiling, and distinguish between data quality and data governance responsibilities.