Which are the six data quality dimensions?

• A. Accuracy, completeness, consistency, timeliness, validity, and uniqueness.
• B. Availability, portability, resilience, scalability, redundancy, auditability.
• C. Relevancy, granularity, consistency, trust, lineage, audit.
• D. Validity, accuracy, integrity, accessibility, timeliness, privacy.

Answer: (A)

Data quality is measured across dimensions that describe how usable and trustworthy the data is for its intended purpose. The six commonly cited dimensions are accuracy, completeness, consistency, timeliness, validity, and uniqueness.

Accuracy means the data reflects real-world values; for example, a customer’s age or address should match reality. Completeness means all required fields are present and not missing. Consistency means data is uniform across systems and over time, so the same entity has the same attributes everywhere. Timeliness means the data is current enough for its use, not outdated. Validity means data adheres to defined formats, types, and business rules, such as dates that are real calendar dates and codes that follow established patterns. Uniqueness means there are no duplicate records representing the same real-world entity. These six together capture essential aspects of data quality; other options mix in broader IT properties or less directly tied quality aspects.