What is Data integrity?

Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that the data remains intact, unchanged, and reliable, regardless of its storage, transfer, or manipulation processes. Data integrity is essential for maintaining the quality and trustworthiness of information.

There are different aspects of data integrity:

1. Accuracy: Data should be accurate and free from errors or inconsistencies.

2. Completeness: Data should be complete, with no missing or incomplete information.

3. Consistency: Data should be consistent and cohesive, with no contradictions or conflicts within or across datasets.

4. Validity: Data should conform to predefined rules and constraints, ensuring that it is valid and meaningful.

5. Reliability: Data should be reliable, meaning it can be consistently depended upon and trusted for decision-making and analysis.

6. Security: Data should be protected against unauthorized access, modification, or deletion, ensuring its integrity is maintained.

7. Auditability: Data integrity includes the ability to track and monitor changes to data, maintaining a reliable audit trail.

8. Durability: Data should be durable and persistent, with mechanisms in place to protect against data loss or corruption.

9. Non-repudiation: Data integrity ensures that the origin and authenticity of data can be verified, preventing users from denying their actions.

10. Timeliness: Data should be timely and up-to-date, reflecting the most current and accurate information.

Data integrity is crucial for maintaining the trust and credibility of data in various applications, including databases, data warehouses, financial systems, healthcare systems, and other critical domains where accurate and reliable information is paramount.