What is meant by data integrity?

Data integrity refers to the accuracy and consistency of data over its lifecycle. This is crucial in maintaining trustworthiness and reliability in any dataset. When data is said to have high integrity, it implies that the data is correct and has remained unchanged in a way that would distort its meaning or accuracy. This is essential in environments where decisions are made based on the analyses of this data, as errors or inconsistencies can lead to incorrect conclusions.

In the context of data management, maintaining data integrity means implementing measures to prevent unintended changes or corruption of data, whether from system malfunctions, user error, or unauthorized access. This concept encompasses various aspects, including data validation rules and error detection mechanisms, which help ensure that only valid and accurate data is stored in databases.

The other options do not accurately capture the essence of data integrity. The extent to which data can be stored relates more to data capacity than integrity. The duplication of data in multiple places can actually lead to integrity issues if the duplicates become inconsistent. Finally, the speed at which data is retrieved pertains to performance rather than the quality of the data itself.