Data integrity, defined
Data integrity refers to how valid, complete and consistent data is and how safe and secure the information is regarding regulatory compliance.
A data set’s integrity is maintained by a collection of processes and standards, which ensure the data remains complete and accurate no matter how long it’s stored or how many times it’s been accessed. To prevent data loss or leaks, companies should emphasise data integrity. By implementing processes to check for errors within data sets and ensuring internal users are handling data correctly, organisations can reduce the risk of outside forces gaining access to sensitive data.
When looking at data integrity, legal professionals are often concerned with the trustworthiness and accuracy of the data throughout its lifecycle. This concerns when the data is entered into the system, how it’s stored, where it’s transferred and more.
You’re most likely to encounter the concern of data integrity in litigation when looking at issues regarding compliance or privacy protocols. Accurate and well-managed data is essential for meeting compliance regulations. Additionally, data integrity is vital for litigation and internal company investigations, as data is often required to be recoverable, searchable and traceable.
If a company ensures its data is of high integrity, investigations can run more smoothly. Data is more easily recoverable, remains accurate throughout processing and produces robust evidence a case can rely on.
With different data definitions to be aware of, it’s easy to confuse data integrity with other meanings, such as data quality. Data quality is only a part of data integrity, with data integrity encompassing all aspects of what defines data quality and more.
