Service guides / AI automation
Data quality checks · AI automationDelivery status updates: data quality checks
Agree what a valid source record looks like. Start with sample shipment events and order references and plan an order status timeline.
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The project in view
- Start with
- sample shipment events and order references.
- Proposed result
- an order status timeline.
- A decision to resolve
- duplicate events arriving out of sequence.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check sample shipment events and order references for completeness, conflicting versions and consistent references.
- Add a separate validation rule for duplicate events arriving out of sequence.
- Keep unresolved records separate from an order status timeline; 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 duplicate events arriving out of sequence 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.