Service guides / AI automation
Data quality checks · AI automationSpreadsheet consolidation: data quality checks
Agree what a valid source record looks like. Start with sample workbooks and column definitions and plan a merged dataset with a validation report.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- sample workbooks and column definitions.
- Proposed result
- a merged dataset with a validation report.
- A decision to resolve
- conflicting identifiers and inconsistent units.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check sample workbooks and column definitions for completeness, conflicting versions and consistent references.
- Add a separate validation rule for conflicting identifiers and inconsistent units.
- Keep unresolved records separate from a merged dataset with a validation report; 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 conflicting identifiers and inconsistent units 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.