Difficulty: Easy

Average Score: 100%

What is the definition of completeness in the context of data integrity?

Rationale

The degree to which there is no missing data.

Completeness in the context of data integrity refers to the extent to which all required data is present and accounted for, indicating that there are no gaps or missing elements in the dataset.

A (The degree to which a set of measures can be used equivalently across systems) YOUR ANSWER

This option describes interoperability or consistency of data across different systems rather than completeness. While important for data integrity, it does not address the presence or absence of data itself, which is the core aspect of completeness.

B (The degree a measure conforms to a standard or a true value) YOUR ANSWER

This choice relates to accuracy or validity, which measures how well data reflects the real-world or adheres to standards. Completeness, however, specifically deals with whether all necessary data is included, not how well it aligns with true values or standards.

C (The degree to which there is no missing data) CORRECT

This is the correct definition of completeness, emphasizing that completeness signifies a dataset's totality without omissions. It ensures that all relevant data points are captured and available for analysis, which is critical for maintaining overall data integrity.

D (The degree to which measures conform to defined business rules) YOUR ANSWER

While this option pertains to adherence to business rules, it relates more to data quality and governance rather than completeness. Completeness focuses on data availability rather than how well it aligns with predefined rules or criteria.

Conclusion

Completeness is a vital aspect of data integrity, fundamentally defined by the absence of missing data within a dataset. While other choices address different aspects of data quality, such as accuracy and conformity to standards, completeness specifically ensures that all required information is present. This principle is essential for achieving reliable and actionable insights from data.

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