Service guides / AI automation
Data quality checks · AI automationForm data validation: data quality checks
Agree what a valid source record looks like. Start with sample submissions and required field rules and plan a clean submission queue with error reasons.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- sample submissions and required field rules.
- Proposed result
- a clean submission queue with error reasons.
- A decision to resolve
- valid records incorrectly rejected by strict rules.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check sample submissions and required field rules for completeness, conflicting versions and consistent references.
- Add a separate validation rule for valid records incorrectly rejected by strict rules.
- Keep unresolved records separate from a clean submission queue with error reasons; record the reason rather than silently filling a gap.
Questions to answer together
- Which fields, references or measurements must be present?
- How are duplicates, contradictions and missing values reported?
- Which corrections require confirmation from the source owner?
What to have at the end
A validation checklist with examples of valid, rejected and unresolved records.
Record the decision on valid records incorrectly rejected by strict rules alongside the source material. If it is still unresolved, identify who can answer it and what work depends on the answer.
Prepare your enquiry
Use this as a starting point. Add your current process, available examples and constraints before sharing.
Send your completed brief to hello@iansamonte.com or visit the main page to discuss your project.