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The term data integrity also leads to confusion because it may refer either to a state or a process.Data integrity as a state defines a data set that is both valid and accurate.
Other data integrity best practices include input validation to preclude the entering of invalid data, error detection/data validation to identify errors in data transmission, and security measures such as data loss prevention, access control, data encryption, and more.
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Protecting the validity and accuracy of data also increases stability and performance while improving reusability and maintainability.
Data increasingly drives enterprise decision-making, but it must undergo a variety of changes and processes to go from raw form to formats more practical for identifying relationships and facilitating informed decisions.