Which dimension of data quality indicates whether data adheres to its intended format?

Which dimension of data quality indicates whether data adheres to its intended format?

• A. Accuracy
• B. Validity
• C. Completeness
• D. Consistency

Answer: (B)

The dimension of data quality that indicates whether data adheres to its intended format is validity. Validity refers to the extent to which data is applicable and appropriate for its intended purpose. This means that data must not only be correct but also must conform to established rules and constraints that define what constitutes acceptable data in a given context. For example, if a dataset includes a date field, the validity dimension ensures that all entries in that field conform to the expected date format, such as "YYYY-MM-DD" or "MM/DD/YYYY." If the data entered does not match these formats, it would be considered invalid. This concept is crucial because using data that does not meet these formatting requirements can lead to errors in data processing and analysis. While other dimensions of data quality like accuracy, completeness, and consistency are also important, they serve different purposes. Accuracy refers to how close data is to the true value; completeness deals with whether all required data is present; and consistency ensures that data is the same across different datasets or systems. Validity specifically focuses on the conformance to format or rules, making it the appropriate answer to this question.

The dimension of data quality that indicates whether data adheres to its intended format is validity. Validity refers to the extent to which data is applicable and appropriate for its intended purpose. This means that data must not only be correct but also must conform to established rules and constraints that define what constitutes acceptable data in a given context.

For example, if a dataset includes a date field, the validity dimension ensures that all entries in that field conform to the expected date format, such as "YYYY-MM-DD" or "MM/DD/YYYY." If the data entered does not match these formats, it would be considered invalid. This concept is crucial because using data that does not meet these formatting requirements can lead to errors in data processing and analysis.

While other dimensions of data quality like accuracy, completeness, and consistency are also important, they serve different purposes. Accuracy refers to how close data is to the true value; completeness deals with whether all required data is present; and consistency ensures that data is the same across different datasets or systems. Validity specifically focuses on the conformance to format or rules, making it the appropriate answer to this question.