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Data quality checks · AI agents

Procurement assistance: data quality checks

Agree what a valid source record looks like. Start with approved supplier records and selection criteria and plan a draft comparison with unanswered questions.

Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.

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The project in view

Start with
approved supplier records and selection criteria.
Proposed result
a draft comparison with unanswered questions.
A decision to resolve
treating an incomplete quotation as comparable.

This is a planning example. It does not describe a completed client engagement.

Data quality checks for this project

  • Check approved supplier records and selection criteria for completeness, conflicting versions and consistent references.
  • Add a separate validation rule for treating an incomplete quotation as comparable.
  • Keep unresolved records separate from a draft comparison with unanswered questions; 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 treating an incomplete quotation as comparable 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.

Email Ian

Send your completed brief to hello@iansamonte.com or visit the main page to discuss your project.