Service guides / AI automation
Data quality checks · AI automationCRM data cleanup: data quality checks
Agree what a valid source record looks like. Start with a redacted contact export and field rules and plan a proposed deduplication list for review.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- a redacted contact export and field rules.
- Proposed result
- a proposed deduplication list for review.
- A decision to resolve
- different people sharing an email address.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check a redacted contact export and field rules for completeness, conflicting versions and consistent references.
- Add a separate validation rule for different people sharing an email address.
- Keep unresolved records separate from a proposed deduplication list for 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 different people sharing an email address 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.