Service guides / SaaS development
Data quality checks · SaaS developmentQuote tracking: data quality checks
Agree what a valid source record looks like. Start with quote samples and approval roles and plan a draft-to-accepted quote journey.
Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.
The project in view
- Start with
- quote samples and approval roles.
- Proposed result
- a draft-to-accepted quote journey.
- A decision to resolve
- editing a quote after acceptance.
This is a planning example. It does not describe a completed client engagement.
Data quality checks for this project
- Check quote samples and approval roles for completeness, conflicting versions and consistent references.
- Add a separate validation rule for editing a quote after acceptance.
- Keep unresolved records separate from a draft-to-accepted quote journey; 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 editing a quote after acceptance 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.