Validation checks whether a value follows an agreed format, range or relationship. Define those rules from the business context, preserve legitimate exceptions and remember that a value can pass validation while still being inaccurate.
State the rule in business terms
Begin with the requirement rather than the software expression. “A completion date cannot precede the agreed start date” is easier to review than an unexplained formula. Record which workflow the rule applies to and the owner who can clarify an exception.
Separate formatting rules from business rules. A date stored in a recognised format may still refer to the wrong day. A numeric amount may be within an accepted range but entered in the wrong currency. Validation can narrow the investigation, but it cannot establish every aspect of real-world correctness.
Choose the appropriate response
Some violations should prevent entry, while others should produce a warning or a review item. Consider the consequence of accepting an error and the consequence of rejecting a legitimate case. A rule that is too rigid can cause people to enter fabricated placeholders or move important work outside the system.
Create an explicit exception path. A reviewer should know what evidence is needed, who may approve the exception and how the decision is recorded. That does not mean every inconvenient record should bypass the rule; it means the process can handle cases the initial definition did not anticipate.
Test ordinary and boundary cases
Build a small set of examples before applying the rule widely. Include typical values, missing values, boundary values and known legitimate exceptions. If a calculation depends on several fields, test combinations rather than examining each field independently.
Ask another person to explain what the rule does using these examples. Differences in interpretation often reveal an incomplete definition before they become a production issue. Keep the test cases with the rule so later changes can be checked against the original intention.
Inspect the exception list
An exception count is an invitation to investigate. Group violations by source, collection step or time period to see whether a process change is responsible. A sudden increase after a system update may point toward a changed format rather than a real business problem.
Avoid measuring success only by the percentage of passing records. A rule that stops running, excludes difficult cases or accepts meaningless defaults can produce an excellent pass rate. Review a sample of passing records as well as the flagged list when the consequence warrants it.
Maintain rules as the workflow changes
Assign an owner, effective date and change history. New products, customer groups or operating regions may make an earlier assumption incomplete. A range that was sensible during a pilot can become inappropriate after a genuine scope change.
When revising a rule, explain whether earlier reports need to be reconsidered. Distinguish a changed business definition from corrected bad data. Readers should be able to understand why an exception count changed without guessing whether the business improved or the checking method moved.
Illustrative worked example
A fictional scenario to explain the method, not a customer result or a market benchmark.
An illustrative delivery dataset requires an estimated completion date on or after the order date. A new import contains dates written in a different day-month order, causing many records to appear to violate the rule. Automatically swapping every date would be risky because some combinations are valid in both formats.
The team isolates the affected import, checks its source convention and corrects the transformation for that source. It then tests unambiguous dates, ambiguous dates and a genuine rescheduled order. The exception process preserves the unresolved cases instead of assigning convenient values.
The final record explains the import change, the transformation applied and the cases still awaiting confirmation. The scenario is fictional. It demonstrates why a failed validation check should lead to investigation rather than an automatic claim that every affected business record is wrong.
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A practical next-step checklist.
- Express the rule and its business scope plainly.
- Define reject, warn and review behaviour deliberately.
- Test boundary cases and legitimate exceptions.
- Keep an owner and change history for the rule.
Common questions.
Does passing a validation rule prove a value is correct?
No. It establishes that the value meets the rule. Accuracy may require comparison with a reliable source or confirmation of the real event.
Should every out-of-range value be automatically replaced?
No. Investigate the meaning, source and consequence first. Some unusual values are legitimate, while others indicate a transformation or collection problem.
Further reading & source context.
These primary references provide background. They are not evidence for the fictional example above and do not endorse Accuralytix.
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