What Data Integrity Meaning, Applications & Example
Ensuring the accuracy, completeness, and consistency of data used to train and deploy AI systems.
What is Data Integrity?
Data Integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that data remains unaltered, complete, and accurate, whether it’s being stored, processed, or transmitted. High data integrity is crucial for making informed decisions, maintaining trust in data-driven systems, and ensuring regulatory compliance.
Types of Data Integrity
- Physical Integrity: Protects data from physical damage, corruption, or loss due to hardware failure or environmental factors.
- Logical Integrity: Ensures data is accurate, consistent, and follows predefined formats, constraints, and rules.
- Referential Integrity: Maintains consistency across related datasets by ensuring foreign key relationships remain intact.
- Domain Integrity: Guarantees that data values fall within defined ranges or sets of permissible values.
Applications of Data Integrity
- Financial Systems: Ensures accuracy and consistency of transaction data for audits, reporting, and compliance .
- Healthcare: Maintains the reliability of patient records, ensuring the data used in medical decisions is accurate and up-to-date.
- E-commerce: Protects customer information and transaction records from corruption or manipulation.
Example of Data Integrity
A banking system that checks for duplicate or erroneous entries when processing customer transactions ensures that the financial data remains consistent and accurate, preventing errors like overcharging or incorrect account balances.