Question 27

Consistency is the degree to which a set of measures can be applied equivalently across systems.

In the context of data integrity, consistency refers to the ability to ensure that data remains the same across different systems or instances, enabling reliable comparisons and analyses.

A) The degree a measure conforms to a standard or true value

This option describes accuracy rather than consistency. While accuracy is important for ensuring that data is correct and reliable, it does not address the uniform application of measures across various systems, which is the essence of consistency.

B) The degree to which all necessary measures are known

This option pertains to completeness, indicating that all relevant data or measures are accounted for. It does not reflect the concept of consistency, which focuses on the uniform application of measures rather than merely knowing all necessary measures.

C) The degree to which a set of measures can be applied equivalently across systems

This is the correct definition of consistency in the context of data integrity. It emphasizes the importance of having standardized measures that can be uniformly applied across different data systems, ensuring that data remains consistent and comparable.

D) The degree to which measures conform to defined business rules

While this option relates to adherence to business protocols, it does not capture the essential aspect of consistency. Conforming to business rules does not inherently guarantee that measures will be uniformly applicable across different systems, which is the core of consistency.

Conclusion

The correct definition of consistency is crucial for maintaining data integrity, as it ensures that measures can be uniformly applied across different systems. Options A, B, and D fail to capture this concept, focusing instead on aspects like accuracy, completeness, and rule adherence, which do not address the uniformity required for true consistency. Therefore, Option C is definitively the right choice.