The quick brief.
- Prioritize checks around important business rules and failure risks.
- Combine repeatable validation with a clear response to exceptions.
- Maintain the checks as definitions, systems, and responsibilities change.
Choose the problem the check needs to catch
A quality program is more useful when it starts with concrete failures: a duplicate order, a missing identifier, an incomplete extract, or a total that changes after a join. Describe the impact of the problem and the evidence that would show it has occurred.
Prioritize the rules that protect an important workflow or decision. A long list of automated checks can be difficult to maintain if nobody can explain why a failure matters. A smaller set with clear intent gives the team a more useful starting point.
Turn an agreed rule into a testable assertion
dbt’s data-testing documentation describes tests as assertions about data and includes familiar checks such as uniqueness, non-null values, accepted values, and relationships. These examples illustrate how a business expectation can become a repeatable validation.
Keep the purpose of each check visible. State the records or workflow it covers, what a failure means, and the action it should trigger. Choose tolerances and exceptions with the relevant owner rather than silently treating every unusual value as an error.
- Document the business rule and the reason for checking it.
- Identify the dataset, interface, or process under review.
- Define the expected result and any approved exceptions.
- Name the person responsible for reviewing failures.
Technical references: dbt — Data tests and assertions (opens in a new tab)
Extend validation across system boundaries
Some defects only appear when several components work together. For an application interface, review both the exchange and the business outcome: valid and invalid inputs, repeated requests, changed records, and failures in a dependency.
An interface description can provide a common basis for testing. OpenAPI offers a standard way to describe HTTP APIs, but a project still needs to decide which business scenarios and access conditions its tests must exercise. A successful response alone may not demonstrate that the intended handoff is complete.
Technical references: OpenAPI Initiative — The OpenAPI Specification (opens in a new tab)
Give exceptions a response path
An automated failure without an owner can become background noise. Agree on how a result reaches the responsible team, how the impact is assessed, and where the resolution is recorded. Distinguish a genuinely broken rule from a check that needs to change because the business definition has changed.
Review the checks as part of delivery and handover. Keep supporting examples, dependencies, and operating notes accessible. Automation should help the team repeat an understood process, while leaving a clear path for decisions that still require judgment.
Automate one meaningful validation routine
Select a recurring manual check that has a clear owner and a known response when it fails. Document the rule, confirm representative examples, and automate the repeatable steps. Assess the result against the problem it was intended to catch before expanding the scope.
DUB Consulting helps build testing, validation, and repeatable delivery practices into data and integration work. The engagement connects technical checks with ownership and follow-through so that quality becomes part of how the system is maintained.