Data integrity is the accuracy, consistency, and reliability of data throughout its life cycle. It ensures that data remains correct and unaltered during storage, retrieval, transfer, and processing operations. Preserving data integrity involves following best practices such as setting validation rules, implementing encryption, creating backups, and restricting access to authorized users. Data integrity is distinct from data quality, which focuses on ensuring information is relevant to the intended use case, and data security, which protects data from unauthorized access and threats. Maintaining data integrity is critical for building organizational trust, making informed decisions, and meeting regulatory requirements. It benefits organizations by boosting operational efficiency, increasing customer trust, enabling better analytics and insights, improving decision making, reducing risk, and supporting regulatory compliance. However, data integrity can be compromised through incorrect data entry, technical issues, cyberattacks, unauthorized access, inadequate validation, weak security controls, or poor data migration practices. To improve data integrity, organizations should implement validation rules, enforce database constraints, regularly audit data for accuracy and consistency, secure access controls, and use automated integrity testing and consistent data governance practices.