Data validation is the process of checking that data meets defined rules before it is used, such as the right format, type, range, required fields, and relationships between values. It answers a specific question: is this data well-formed and conformant? Validation runs at many points, from the moment data is entered, to batch jobs, to schema and business-rule checks, often with tools like Great Expectations, Soda, or Monte Carlo.
Validation is distinct from verification. Validation asks whether data follows the rules; verification asks whether it is actually correct against a trusted source. A value can pass validation, a date in the right format, and still be verified as wrong. For analytics and AI, validation is an early gate that stops malformed data from propagating downstream.