Data integrity is the degree in which data sets can remain unaltered after changes or updates have been performed in the database. It is a concept that measures the accuracy of stored data.
Explanation :
The integrity of stored data is essential in the digital environment as most of the records and information produced by companies and organizations is now stored digitally, the risks of losing data even partially must be avoided as much as possible. Systems have many different methods to ensure data integrity, the most popular among them being error checking, validation procedures and backups. On the other hand, all data should be interconnected. This way, if data is partially or entirely lost somewhere, it can be traced back to another point, therefore avoiding a complete loss. Data integrity can be compromised by external intrusions, damaged hardware, malware and viruses, human error, update failures or errors experienced during a transfer or a replication. On the other hand, access to raw data or certain levels of data should be restricted by using authorization structures that allow or deny certain individuals to make changes or to erase data without previous clearance. Databases should be equipped to react properly to any of these scenarios, to protect data integrity.
In a nutshell:
- Data integrity is the overall accuracy, completeness, and consistency of data. Data integrity also refers to the safety of data in regard to regulatory compliance , such as GDPR compliance and security.
- It is maintained by a collection of processes, rules, and standards implemented during the design phase.
- When the integrity of data is secure, the information stored in a database will remain complete, accurate, and reliable no matter how long it’s stored or how often it’s accessed.
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