What does data integrity refer to?

Data integrity is fundamentally concerned with maintaining the accuracy and consistency of data over its entire lifecycle. It ensures that the data is reliable, accurate, and can be trusted for decision-making. In a database context, this means that the data is correct, up-to-date, and reflects the true value that it is supposed to represent.

Implementing measures such as validation checks, constraints, and standard protocols helps to preserve data integrity, preventing both accidental and intentional data corruption. This aspect is critically important for any organization as compromised data integrity can lead to erroneous conclusions and potentially harmful decisions.

In contrast, the other options focus on different aspects of data management. For instance, the speed of data retrieval is about how quickly data can be accessed, which relates to performance rather than accuracy. The structure of database tables deals with how data is organized rather than its correctness, while the amount of data pertains to storage capacity and does not directly address whether that data is accurate or consistent. Thus, option B aligns precisely with the definition of data integrity.