DataIntegrity

Data integrity refers to the accuracy, consistency, and trustworthiness of data throughout its lifecycle. It concerns whether data remains complete and unaltered except by authorized processes, and whether it reliably reflects the real-world state it is meant to model.

Key dimensions include accuracy, consistency, completeness, validity, and timeliness, as well as referential integrity in relational data. Data integrity covers both physical integrity of stored bits and logical integrity of data according to business rules.

Data integrity is threatened by hardware failures, software bugs, human error, and malicious tampering, as well as errors introduced during transmission or integration. Poor access controls and weak governance can erode integrity after data creation.

Controls include validation at entry, database constraints (primary keys, foreign keys, checks), cryptographic hashes and checksums, versioning, auditing, and backups with integrity verification. Strong authentication, authorization, and least-privilege access support ongoing integrity in multi-user systems.

Maintaining data integrity supports data quality, governance, and compliance across sectors such as finance, healthcare, and government. It underpins reliable decision-making and risk management by ensuring data remains accurate and coherent throughout its life.