Data Quality Dimensions
CompTIA Data+ DA0-001 (V1) PBQ
This exercise includes matching data quality dimensions, like accuracy, completeness, and timeliness, to their corresponding definitions or real-world examples.
Timeliness
Relevance
Completeness
Integrity
Validity
Consistency
Accuracy
Precision
Traceability
Uniqueness
Data that is available when it is needed and is up-to-date
The extent to which records are distinct with no duplicates
The ability to track the origins, updates, or sources of the data
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 has all required values and is not missing crucial information
Data that maintains proper relationships or linkages between records or datasets
The level of detail or granularity in the data
The degree to which data reflects the real-world object or event it represents
The adherence of data to rules, formats, or constraints like a specific data type or pattern