Data Validation
Check whether your data meets defined requirements.
Validate datasets against explicit constraints before consumption.
Data Validation checks a single dataset against defined business and schema rules. Unlike reconciliation, it does not require a second reference dataset.
It tests for missing values, range bounds, type integrity, uniqueness, and consistency across fields to prevent invalid records from corrupting downstream systems.
Rule evaluation example
Rules applied: customer_id is required; amount must be ≥ 0.00.
| Record | Customer | Amount | Expected Result |
|---|---|---|---|
| V-100 | C-10 | 25.00 | Pass |
| V-101 | Empty | 10.00 | Required customer missing |
| V-102 | C-12 | -5.00 | Amount below zero |
Three records, one passing record, two failing records, two rule failures. Record-failure counts and rule-failure counts can differ if multiple rules fail on a single record.
Need help implementing validation checks?
We work with data teams to translate complex business rules into reproducible validation suites that run at ingestion or before reporting handovers.
Discuss Data Validation
Tell us about your dataset, rules, and validation objectives.