Service guides / AI automation
Data quality checks · AI automationBooking reminders: data quality checks
Agree what a valid source record looks like. Start with a sample booking calendar and consent rules and plan a reminder queue tied to booking status.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- a sample booking calendar and consent rules.
- Proposed result
- a reminder queue tied to booking status.
- A decision to resolve
- cancellations after a reminder has been scheduled.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check a sample booking calendar and consent rules for completeness, conflicting versions and consistent references.
- Add a separate validation rule for cancellations after a reminder has been scheduled.
- Keep unresolved records separate from a reminder queue tied to booking status; 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 cancellations after a reminder has been scheduled 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.