In the context of data governance, what does Integrity refer to?

In the context of data governance, what does Integrity refer to?

• A. Consistency across data objects via reference keys
• B. Uniqueness of data entries across datasets
• C. Timeliness of data updates and changes
• D. Reasonability of data patterns

Answer: (A)

In the context of data governance, Integrity specifically refers to the accuracy and consistency of data within datasets. This notion encompasses various aspects of data management, but in this case, it highlights the idea that data must maintain a high degree of reliability and that relationships between data elements should be validated by reference keys. Reference keys are crucial because they establish connections between different data objects, ensuring that they correspond correctly across various datasets. This guarantees that data remains trustworthy and that any operations carried out involving these datasets will yield consistent and valid results. For instance, if a database contains customer information linked by unique identifiers, these reference keys must remain intact across the database to preserve the integrity of the data, allowing for reliable data retrieval and analysis. Timeliness, uniqueness, and reasonability are important aspects of data management but do not exclusively define data integrity. While they contribute to the overall quality of data, they focus on different facets, such as timely updates, ensuring no duplicate entries exist, or checking whether data patterns make sense respectively. However, integrity's core focus is on maintaining consistency through these relationships across datasets, thus making the first option the most aligned with the definition of integrity in data governance.

In the context of data governance, Integrity specifically refers to the accuracy and consistency of data within datasets. This notion encompasses various aspects of data management, but in this case, it highlights the idea that data must maintain a high degree of reliability and that relationships between data elements should be validated by reference keys.

Reference keys are crucial because they establish connections between different data objects, ensuring that they correspond correctly across various datasets. This guarantees that data remains trustworthy and that any operations carried out involving these datasets will yield consistent and valid results. For instance, if a database contains customer information linked by unique identifiers, these reference keys must remain intact across the database to preserve the integrity of the data, allowing for reliable data retrieval and analysis.

Timeliness, uniqueness, and reasonability are important aspects of data management but do not exclusively define data integrity. While they contribute to the overall quality of data, they focus on different facets, such as timely updates, ensuring no duplicate entries exist, or checking whether data patterns make sense respectively. However, integrity's core focus is on maintaining consistency through these relationships across datasets, thus making the first option the most aligned with the definition of integrity in data governance.