What does "data integrity" refer to?

Data integrity refers to the maintenance of the accuracy and consistency of data over its entire lifecycle. This involves ensuring that data remains unchanged and uncorrupted when it is collected, stored, and accessed. It is crucial for systems that rely on data for decision-making and operations, as compromised integrity can lead to incorrect outcomes, business disruptions, and security vulnerabilities.

To maintain data integrity, organizations implement various measures, such as checksums, hashing, and access controls, that prevent unauthorized modifications and detect errors or changes to the data. These practices are foundational in fields where data accuracy is paramount, such as finance, healthcare, and scientific research.

While other choices address important aspects of data management, they do not define data integrity. For instance, storing data in multiple locations enhances availability and resilience but does not directly pertain to its accuracy and consistency. Performing regular backups is critical for recovery in case of data loss or corruption but does not directly address the integrity aspect. Eliminating all potential data access would hinder legitimate use and management of data and is counterproductive to the concept of integrity in a collaborative environment. Therefore, maintaining the accuracy and consistency of data captures the essence of data integrity effectively.