12 Facts About Data integrity

Data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle and is a critical aspect to the design, implementation, and usage of any system that stores, processes, or retrieves data.

The overall intent of any data integrity technique is the same: ensure data is recorded exactly as intended.

In short, data integrity aims to prevent unintentional changes to information.

Data integrity is not to be confused with data security, the discipline of protecting data from unauthorized parties.

Physical Data integrity often makes extensive use of error detecting algorithms known as error-correcting codes.

Human-induced data integrity errors are often detected through the use of simpler checks and algorithms, such as the Damm algorithm or Luhn algorithm.

Common methods of ensuring logical Data integrity include things such as check constraints, foreign key constraints, program assertions, and other run-time sanity checks.

Data integrity contains guidelines for data retention, specifying or guaranteeing the length of time data can be retained in a particular database.

Data integrity includes rules defining the relations a piece of data can have to other pieces of data, such as a Customer record being allowed to link to purchased Products, but not to unrelated data such as Corporate Assets.

Data integrity often includes checks and correction for invalid data, based on a fixed schema or a predefined set of rules.

Data integrity is normally enforced in a database system by a series of integrity constraints or rules.

Three types of integrity constraints are an inherent part of the relational data model: entity integrity, referential integrity and domain integrity.