What are the Dimensions of Data Quality? The 6 Core Dimensions Explained

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A professional-level summary covering key definitions, frameworks, and exam-relevant points.

The Six Data Quality Dimensions

Dimension	Definition	Example Problem	Measurement
Completeness	All required data is present	Missing email in customer record	% required fields populated
Accuracy	Data correctly represents reality	Wrong address in customer record	% records matching reference source
Consistency	Data is free from contradictions	Different DOB in CRM vs billing	% records consistent across systems
Timeliness	Data is current when needed	6-month-old address in data warehouse	% records within freshness window
Validity	Data conforms to rules and formats	Date field containing "99/99/9999"	% records passing validation rules
Uniqueness	Each entity recorded only once	Same customer with 3 duplicate records	100% minus duplicate rate

CDMP Exam Relevance

The six data quality dimensions are among the most heavily tested concepts in the CDMP exam, appearing in the Data Quality knowledge area (11%) and referenced in Data Governance, Metadata Management, and Master Data Management questions. Key exam topics include: the definitions of all six dimensions, the differences between dimensions (particularly accuracy vs validity, and completeness vs uniqueness), examples of each dimension, and how to measure each dimension. Memorising all six dimensions with clear definitions and examples is essential for CDMP success.

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