Describe the difference between data quality and data integrity.

• A. Data quality and data integrity are the same concept.
• B. Data quality is fitness for use, measured by accuracy, completeness, etc.; data integrity refers to correctness and consistency of data across systems.
• C. Data quality is about data processing speed.
• D. Data integrity is about data encryption.

Answer: (B)

Understanding the difference between data quality and data integrity helps you know what to measure and protect. Data quality describes how fit data is for its intended use, evaluated through attributes like accuracy, completeness, timeliness, validity, consistency, and uniqueness. Data integrity, meanwhile, focuses on keeping data correct and consistent over time and as it moves between systems, ensuring that data remains reliable and unaltered by errors or unauthorized changes. In practice, you’d evaluate data quality to confirm that the data actually supports business decisions, while you’d implement data integrity controls—such as integrity constraints, referential links, and validation rules—to ensure data remains coherent across systems and throughout its lifecycle. The other options misstate one or both concepts (for example, equating them, tying data quality to processing speed, or equating integrity with encryption), which is why the best answer clearly distinguishes fitness for use from cross-system correctness and consistency.