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