Service guides / AI automation
Data quality checks · AI automationTimesheet preparation: data quality checks
Agree what a valid source record looks like. Start with sample clock records and shift rules and plan a timesheet exception list for payroll review.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- sample clock records and shift rules.
- Proposed result
- a timesheet exception list for payroll review.
- A decision to resolve
- missing clock-outs and overnight shifts.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check sample clock records and shift rules for completeness, conflicting versions and consistent references.
- Add a separate validation rule for missing clock-outs and overnight shifts.
- Keep unresolved records separate from a timesheet exception list for payroll review; 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 missing clock-outs and overnight shifts 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.