Data integrity is a critical part of information security. The term is generally used to refer to the consistency and accuracy of the data stored in a data warehouse, data mart, database, or any other construct over its entire lifecycle. It can be used to refer to a process, function, or state – and is often used as a synonym for “data quality”.
Data with integrity means that it is complete or whole structured. Its values are standardized according to a specific data type or model and all its characteristics are correct, including business relations, rules, dates, lineage, and definitions. It also means the data has been unaltered during its creation, processing, and while in transit. There are many factors that can impact a data’s integrity, from breaches to hardware failures that cause sensitive information to be lost or compromised.
Data integrity is usually imposed in databases when it’s designed and authenticated by continually using validation and error checking routines. However, data integrity measures are not always intricate procedures. For instance, not allowing alphabetic data to be inserted into numeric columns or cells is a simple example of how data integrity can be maintained.