What Are the Types of Data Validation and Why They Matter

Data validation is the process of ensuring data is accurate, complete, and meets quality standards before use. Key validation types include format verification, range validation, presence checks, uniqueness checks, and cross-field validation. Poor data quality costs organizations roughly $12.9 million annually. Real-world applications span healthcare (patient record accuracy), finance (fraud prevention), and e-commerce (checkout accuracy). Best practices include automating validation, conducting regular audits, maintaining documentation, and establishing feedback loops. Decube is highlighted as a platform that supports these processes through automated metadata crawling, ML-powered tests, and smart alerts.