Validations

Validation is the process of establishing evidence that a product, service, or system fulfils its intended use and complies with defined requirements. It is applied across disciplines such as software engineering, data management, statistics, manufacturing, and healthcare. Validation focuses on usefulness and real-world performance, often contrasting with verification, which asks whether the product was built according to its specifications.

In data management, data validation checks that inputs are correct, complete, and consistent with rules or schemas. Techniques include type checks, range and constraint validation, format validation, and cross-field consistency checks. Validation aims to prevent errors from entering systems and to support trustworthy analytics.

In software development, validation seeks assurance that the software meets the needs of users and stakeholders. Activities include requirements reviews, testing, user acceptance testing, beta deployments, and field studies. Validation is often performed iteratively and may be required by regulators in safety-critical domains.

In statistics and machine learning, model validation assesses how well a model generalizes to new data. Methods include train-test splits, cross-validation, and external validation datasets. Overfitting and data leakage are key risks to address during validation.

In regulated industries, process validation and equipment validation document that manufacturing processes consistently produce products meeting quality criteria. This commonly encompasses installation qualification, operational qualification, and performance qualification, as well as ongoing monitoring and periodic revalidation.

A validation program typically produces a validation plan, protocols, and a final validation report. A risk-based approach, clear acceptance criteria, and traceability to requirements help ensure objective evidence. Validation is an ongoing activity, with monitoring and revalidation as conditions change.