Data Integrity

What is data integrity?

Data integrity refers to the quality, accuracy, and consistency of data throughout its lifecycle. It is a separate but related concept to data security, which contributes to data integrity by protecting data from both internal and external threats. Common risks to data integrity include incorrect or incomplete data due to human error, cybersecurity issues, data transfer errors, and software or hardware issues.

Example: To ensure data integrity, we have a robust procedure for checking and correcting any human errors within a customer data set. 

Why is data integrity important?

Maintaining data integrity is crucial for making informed, data-driven decisions and protecting data subjects’ information. Threats to data integrity can cost organizations time, money and resources, and can harm the organization’s reputation and authority. 

Physical data integrity vs. logical data integrity

Data integrity is typically broken down into two main categories: physical and logical integrity.  Physical integrity refers to protecting the integrity of data when it is stored and retrieved, while logical integrity refers to the accuracy of the data itself. Logical integrity can be broken down into four different subcategories: entity integrity, referential integrity, domain integrity, and user-defined integrity.