What aspect of data management is concerned with maintaining the accuracy and consistency of data?

Data integrity management is fundamentally focused on ensuring that data remains accurate, consistent, and trustworthy throughout its lifecycle. This aspect of data management involves implementing standards and practices that protect data from being altered in any unauthorized or unintended manner. This could include practices like validation checks, error detection and correction mechanisms, and access controls that limit who can make changes to the data.

The processes under data integrity management also contribute to maintaining data quality by ensuring that any data entered into a system conforms to predefined standards and remains consistent across different databases and applications. By emphasizing the importance of data accuracy and consistency, organizations can make more informed decisions based on reliable data, minimize errors, and maintain operational efficiency.

In contrast, data archival focuses on moving data that is no longer actively used to a separate storage for long-term retention, but does not inherently ensure the accuracy or consistency of that data. Data sharing relates to how data is distributed and used among users or systems, which does not address the integrity of the data itself. Data storage pertains to the physical or cloud-based locations where data is housed, also lacking a direct connection to accuracy and consistency. Therefore, the most relevant concept when discussing the maintenance of data accuracy and consistency is indeed data integrity management.