Validatsiya

Validatsiya, often rendered as validation, is the process of establishing that a concept, product, service, or dataset meets defined requirements and fulfills its intended use. The term is frequently transliterated as validatsiya in Slavic languages and derives from the Latin word validus. In many sectors, validation is formalized with documented plans, procedures, and records.

In software and product development, validation asks whether the final product satisfies user needs and intended use, i.e., “are we building the right thing?” It is distinct from verification, which asks whether the product was built correctly according to specifications. Validation activities can include user acceptance testing, field trials, and regulatory or customer approvals.

Data validation focuses on data quality and integrity. It involves checks for correct data types, allowed ranges, formats, consistency across fields, and absence of corruption. Common methods include type checks, range checks, regular-expression validation, cross-field validation, deduplication, and schema enforcement. In data science and machine learning, model validation assesses generalization to new data, using holdout sets, k-fold cross-validation, or external validation, with metrics such as accuracy, RMSE, or ROC AUC.

Measurement and analytical validation ensure instruments, assays, or methods produce accurate, precise, and reproducible results within predefined limits. Process validation demonstrates that a process can consistently deliver products meeting quality attributes, often under regulatory frameworks such as GMP or GLP, supported by validation plans, protocols, and reports.

Regulatory and governance aspects require documented validation for medical devices, pharmaceuticals, and certain software and data processing applications. Ongoing monitoring and periodic revalidation may be necessary to maintain demonstrated validity in changing conditions.

See also: verification, testing, quality assurance, data quality, cross-validation.