Data Integrity

Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that data remains unchanged during storage, retrieval, and transfer, unless altered in an authorized and intentional manner. Maintaining data integrity is essential for trustworthy analytics, system functionality, and decision-making.

Also known as: Data accuracy, data consistency

Comparisons

• Data Integrity vs. Data Security: Integrity ensures data remains accurate and consistent, while security focuses on protecting data from unauthorized access or breaches.

• Data Integrity vs. Data Quality: Quality encompasses broader characteristics like completeness and relevance; integrity is specifically about correctness and consistency.

Pros

• Ensures trustworthiness: Reliable data leads to dependable results and decisions.
• Supports compliance: Vital for meeting regulatory standards in fields like healthcare and finance.
• Reduces risk: Prevents errors caused by corrupted or inconsistent data.

Cons

• Complex to enforce: Requires technical controls like checksums, validation rules, and audit trails.
• Resource intensive: Maintaining integrity in large, distributed systems can demand significant infrastructure and monitoring.

Example 

In a banking system, data integrity ensures that account balances are accurately updated after each transaction. If a deposit is made, the system must reflect the new balance correctly without data corruption or duplication.

Learn more about Data integrity here.