What does the Data Quality Dimension of Validity ensure?

What does the Data Quality Dimension of Validity ensure?

• A. Values are consistent with domain values
• B. Values are checked for patterns
• C. Entries in datasets are unique
• D. Data sets are timely and updated frequently

Answer: (A)

The Data Quality Dimension of Validity focuses on ensuring that the values contained within a dataset are appropriate and conform to established rules, often associated with the specific context or domain of the data. When we say that values are consistent with domain values, we mean that they should align with the types of values that are expected or allowed within a particular field or category. For instance, if a data field is designated for storing age, an entry must be a valid integer that falls within a reasonable range (e.g., 0-120). This ensures the data reflects real-world scenarios and adheres to business rules and regulatory requirements. Validity checks help maintain data integrity and prevent errors that could arise from incorrect or improperly formatted entries. In contrast, the other choices pertain to different aspects of data quality. Checking for patterns relates to consistency or the detection of anomalies, uniqueness pertains to data integrity and ensuring no duplicate entries exist, and timeliness addresses the currency and regular updates of the data. These dimensions are also important but focus on different areas of data quality rather than validity.

The Data Quality Dimension of Validity focuses on ensuring that the values contained within a dataset are appropriate and conform to established rules, often associated with the specific context or domain of the data. When we say that values are consistent with domain values, we mean that they should align with the types of values that are expected or allowed within a particular field or category.

For instance, if a data field is designated for storing age, an entry must be a valid integer that falls within a reasonable range (e.g., 0-120). This ensures the data reflects real-world scenarios and adheres to business rules and regulatory requirements. Validity checks help maintain data integrity and prevent errors that could arise from incorrect or improperly formatted entries.

In contrast, the other choices pertain to different aspects of data quality. Checking for patterns relates to consistency or the detection of anomalies, uniqueness pertains to data integrity and ensuring no duplicate entries exist, and timeliness addresses the currency and regular updates of the data. These dimensions are also important but focus on different areas of data quality rather than validity.