The term “validation” refers to the systematic checking of whether data, inputs, API calls, configurations, or results comply with defined rules, specifications, and business objectives. Objectives include ensuring data quality, detecting errors early, meeting compliance requirements, and stabilizing downstream processes.
In practice, two meanings are common: (1) data/input validation during operations (focus of this entry) and (2) system/process validation in regulated industries (documented evidence that software performs reliably for its intended use).
Data type and required-field checks: Ensure mandatory fields are populated and values match expected types.
Range and threshold checks: Min/max validation, allowed intervals, allow/deny lists.
Format and syntax checks: Regular expressions and checksums (e.g., email, phone, IBAN, VAT ID).
Consistency and cross-field checks: Logical dependencies such as start date ≤ end date, ZIP↔city, currency↔country.
Business rules validation: Rule engines (e.g., DMN), decision trees, and policies for domain-specific constraints.
Schema and interface validation: Validate against JSON Schema, XML/XSD, or OpenAPI; manage schema versions.
Duplicate and uniqueness checks: Detect duplicates (fuzzy matching) and enforce unique keys.
Master data and address validation: Cross-check with reference directories (e.g., postal databases) and company IDs.
File and document validation: MIME type, file size, malware scanning, signature and timestamp verification.
Localization and time validation: Time zones, calendar rules, country-specific formats.
Real-time vs. batch validation: Inline form feedback, server-side checks, ETL gatekeeping.
Error handling and user feedback: Clear error messages, correction hints, validation tooltips.
Logging, monitoring, and audit trail: Traceability of checks for compliance and quality assurance.
Rule management and versioning: No/low-code editors, test cases/sandboxes, approval workflows (four-eyes principle).
Quarantine and exception workflows: Route invalid records for review and controlled approval.
A CRM enforces required fields and email format when creating a lead, providing instant inline feedback.
An ERP blocks a purchase order if the delivery date is earlier than the order date or the currency conflicts with the tenant’s settings.
An e-commerce checkout validates IBAN via checksum and rejects invalid entries before payment processing.
An ETL pipeline validates incoming records against a JSON schema; invalid records are quarantined and reported.
An API gateway validates requests against an OpenAPI spec and returns structured error messages on violations.
A DMS verifies qualified electronic signatures and timestamps for uploaded documents.
A pharma company documents IQ/OQ/PQ to demonstrate that a LIMS reliably serves its intended use (system validation).
