Data validation and verification are crucial processes for ensuring data quality and accuracy. Data validation involves checking the accuracy, quality, and integrity of data against predefined rules before it is stored or used, aiming to maintain high data quality and meet compliance requirements. Validation checks can include data type, format, range, presence, code, consistency, and uniqueness checks, often performed at the point of data entry to prevent errors from spreading. Data verification, on the other hand, is the process of confirming that data accurately reflects real-world facts by comparing it against authoritative sources, and is typically performed after validation when the reliability of the data source is uncertain. Verification methods include automated verification, proofreading, double-entry systems, and source data verification, which are more complex and may involve uncertainty and manual review. Both processes are complementary, serving to ensure data is both properly structured and genuinely accurate, thus preventing costly mistakes and supporting effective decision-making.