Service guides / AI automation
Data quality checks · AI automationMeeting action tracking: data quality checks
Agree what a valid source record looks like. Start with approved sample notes and a team directory and plan a draft action list with owners.
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The project in view
- Start with
- approved sample notes and a team directory.
- Proposed result
- a draft action list with owners.
- A decision to resolve
- unclear ownership and tentative commitments.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check approved sample notes and a team directory for completeness, conflicting versions and consistent references.
- Add a separate validation rule for unclear ownership and tentative commitments.
- Keep unresolved records separate from a draft action list with owners; 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 unclear ownership and tentative commitments 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.