Which option correctly lists common data quality dimensions with concise definitions?

Which option correctly lists common data quality dimensions with concise definitions?

• A. Accuracy — data matches real world; Completeness — no missing values; Consistency — uniform across sources; Timeliness — up-to-date data; Validity — conforms to rules; Uniqueness — no duplicates.
• B. Accuracy — data matches real world; Completeness — has some missing values; Consistency — inconsistent across sources; Timeliness — not current; Validity — conforms to rules; Uniqueness — duplicates exist.
• C. Accuracy — data matches reality; Completeness — all fields filled; Consistency — same across systems; Timeliness — data is current; Validity — conforms to business rules; Uniqueness — no duplicates.
• D. Accuracy — data mismatches reality; Completeness — no missing values; Consistency — uniform across sources; Timeliness — up-to-date data; Validity — violates rules; Uniqueness — duplicates exist.

Answer: (A)

Data quality is assessed across several dimensions that tell you how reliable the data is for decision making. Accuracy means the data truly reflects the real world. Completeness means there are no missing values for the data elements you need. Consistency means the same data behaves the same way across different sources or systems, with no contradictions or mismatched formats. Timeliness means the data is current enough for its intended use. Validity means the data conforms to the defined rules, formats, and business constraints. Uniqueness means there are no duplicate records for the same entity. This option lists each dimension with a concise, correct definition that aligns with common data quality practice: accuracy matches reality, completeness has no missing values, consistency is uniform across sources, timeliness is up-to-date, validity conforms to rules, and uniqueness has no duplicates. Other sets either redefine completeness as allowing missing values, treat consistency as merely uniform without addressing conflicts, describe timeliness as not current, cast validity as violating rules, or describe duplicates as an inherent problem for uniqueness. These misalignments make them less accurate representations of the standard data quality dimensions.

Data quality is assessed across several dimensions that tell you how reliable the data is for decision making. Accuracy means the data truly reflects the real world. Completeness means there are no missing values for the data elements you need. Consistency means the same data behaves the same way across different sources or systems, with no contradictions or mismatched formats. Timeliness means the data is current enough for its intended use. Validity means the data conforms to the defined rules, formats, and business constraints. Uniqueness means there are no duplicate records for the same entity.

This option lists each dimension with a concise, correct definition that aligns with common data quality practice: accuracy matches reality, completeness has no missing values, consistency is uniform across sources, timeliness is up-to-date, validity conforms to rules, and uniqueness has no duplicates.

Other sets either redefine completeness as allowing missing values, treat consistency as merely uniform without addressing conflicts, describe timeliness as not current, cast validity as violating rules, or describe duplicates as an inherent problem for uniqueness. These misalignments make them less accurate representations of the standard data quality dimensions.