What does the Data Quality Dimension of Reasonability assess?

What does the Data Quality Dimension of Reasonability assess?

• A. Whether data patterns meet expectations
• B. Whether data is timely and up-to-date
• C. Whether all values are present in the dataset
• D. Whether data values are unique

Answer: (A)

The Data Quality Dimension of Reasonability primarily focuses on assessing whether data patterns meet expectations. This involves examining the logical consistencies within the data to ensure that the values fall within a range that is considered plausible or expected based on the context of the dataset. Reasonability checks help identify outliers, anomalies, or errors that can occur during data entry or collection processes. For instance, if the data indicates that a person's age is 150 years, a reasonability check would flag this value as improbable, signaling a potential error in data collection or input. In contrast, the other dimensions mentioned relate to different aspects of data quality. Timeliness focuses on whether data is current and relevant, which is vital for ensuring that decisions made based on this data are based on the most up-to-date information. Completeness relates to the presence of all required values in the dataset; missing data can significantly limit the dataset's usability and reliability. Uniqueness assesses whether data values are distinct and free from duplicates, ensuring accurate representation and analysis. Overall, the dimension of reasonability is crucial for ensuring that data not only appears valid on the surface but also aligns with what one would logically expect, enhancing the overall quality and trustworthiness of the data.

The Data Quality Dimension of Reasonability primarily focuses on assessing whether data patterns meet expectations. This involves examining the logical consistencies within the data to ensure that the values fall within a range that is considered plausible or expected based on the context of the dataset. Reasonability checks help identify outliers, anomalies, or errors that can occur during data entry or collection processes. For instance, if the data indicates that a person's age is 150 years, a reasonability check would flag this value as improbable, signaling a potential error in data collection or input.

In contrast, the other dimensions mentioned relate to different aspects of data quality. Timeliness focuses on whether data is current and relevant, which is vital for ensuring that decisions made based on this data are based on the most up-to-date information. Completeness relates to the presence of all required values in the dataset; missing data can significantly limit the dataset's usability and reliability. Uniqueness assesses whether data values are distinct and free from duplicates, ensuring accurate representation and analysis.

Overall, the dimension of reasonability is crucial for ensuring that data not only appears valid on the surface but also aligns with what one would logically expect, enhancing the overall quality and trustworthiness of the data.