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Data quality checks · AI automationTender document sorting: data quality checks
Agree what a valid source record looks like. Start with a tender folder and document naming rules and plan a document register grouped by trade and revision.
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The project in view
- Start with
- a tender folder and document naming rules.
- Proposed result
- a document register grouped by trade and revision.
- A decision to resolve
- superseded drawings mixed with current issues.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check a tender folder and document naming rules for completeness, conflicting versions and consistent references.
- Add a separate validation rule for superseded drawings mixed with current issues.
- Keep unresolved records separate from a document register grouped by trade and revision; 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Record the decision on superseded drawings mixed with current issues alongside the source material.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.