Service guides / AI automation
Data quality checks · AI automationSupport ticket triage: data quality checks
Agree what a valid source record looks like. Start with redacted tickets and escalation categories and plan a prioritised support queue.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- redacted tickets and escalation categories.
- Proposed result
- a prioritised support queue.
- A decision to resolve
- urgent issues with vague wording.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check redacted tickets and escalation categories for completeness, conflicting versions and consistent references.
- Add a separate validation rule for urgent issues with vague wording.
- Keep unresolved records separate from a prioritised support queue; 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 urgent issues with vague wording 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.