Business rules turn policy into repeatable validation
Standard validation checks answer technical questions. Is the field present? Is the date valid? Is the number inside an allowed range?
Business rule validation asks a different question: does this record make sense according to the organisation's rules? That is where healthcare, finance, operations, compliance, and reporting teams often need checks that are specific to their process.
In Advanced ETL Processor, these rules run inside the ETL workflow before data is loaded. Failed rows are logged, rejected, or routed for review while the original source file stays unchanged.
The NHS patient age rule is a business rule check
A practical healthcare example is simple: an NHS patient's age must match the date of birth. The date of birth may be a valid date. The age may be a valid number. The row is still wrong if the two values disagree.
- Valid record: date of birth is 10 July 1980 and age is 45 on the reporting date.
- Invalid record: date of birth is 10 July 1980 and age is 32 on the same reporting date.
- Review record: date of birth is missing, but age is present. The workflow routes it for correction instead of guessing.
Guessing patient data is not validation. It is a spreadsheet wearing a lab coat, and nobody needs that near a production workflow.
Common business rule validation examples
- Patient age must match date of birth on the reporting date.
- An order marked completed must have a completion date.
- A discount must be allowed for the customer type and contract.
- A tax code must match the country, product type, and customer status.
- A payment date must not be earlier than the invoice date unless the business explicitly allows prepayment.
Write the rule in plain language first
The safest business rules start as plain sentences. If the rule cannot be explained clearly, do not automate it yet. The workflow does exactly what you configure, including confidently enforcing a badly written rule at 2:13 a.m.
Start with the business statement, then translate it into validation logic. For the NHS age example: calculate age from date of birth on the reporting date, compare it with the supplied age, and use Advanced ETL Processor to reject or route the row when the values do not match.
When not to automate business rule validation
Do not automate a rule if the business cannot agree on it. Also avoid automation when the file is a one-off sample and a manual review is quicker than building the workflow.
Automate business rule validation when the feed repeats, the rule protects reporting or compliance, and rejected rows need a clear audit trail. That is where validation stops being admin and starts being useful engineering.
Useful references
Related checks include consistency validation, range validation, and creating complex data validation rules. For setup details, see the Validator object documentation and the NHS England guidance on organisation data downloads.