Data Quality Dimensions
CompTIA Data+ DA0-002 (V2) PBQ
This exercise includes matching data quality dimensions, like accuracy, completeness, and timeliness, to their corresponding definitions or real-world examples.
Completeness
Timeliness
Validity
Uniqueness
Traceability
Integrity
Accuracy
Relevance
Consistency
Precision
The adherence of data to rules, formats, or constraints like a specific data type or pattern
The level of detail or granularity in the data
Data that is available when it is needed and is up-to-date
Data that has all required values and is not missing crucial information
Data that is applicable and useful for a specific purpose or decision-making
Data that is uniform across databases or datasets without contradictions
Data that maintains proper relationships or linkages between records or datasets
The degree to which data reflects the real-world object or event it represents
The ability to track the origins, updates, or sources of the data
The extent to which records are distinct with no duplicates