guide
Transport emissions data quality: primary data, fallbacks, gaps, and review notes
Data quality is often the difference between a useful customer response and a number that cannot be explained later.
Audience
Teams preparing customer evidence packs from shipment exports and operational records.
Problem
When missing data and fallbacks are not visible, customers cannot tell which results are strong and which need improvement.
Product fit
LogisticsAI separates calculation output from quality context, evidence notes, and improvement actions in the evidence pack.
What to grade
A data quality workflow should look at completeness, source reliability, fallback use, review status, and whether evidence exists for the most important inputs.
- Check distance, weight, mode, fuel, vehicle type, dates, origin, and destination coverage.
- Flag rows that rely on defaults or incomplete shipment records.
- Keep source notes available for customer and internal review.
Quality report contents
A useful quality report should not only score data. It should tell teams what to fix next and what to avoid overclaiming.
- Summarize missing or fallback fields.
- List evidence gaps and recommended fixes.
- Separate internal review notes from customer-facing summary text.
Operational improvement
The same quality findings can be reused to improve TMS exports, carrier data requests, and procurement reporting workflows.
Explore the evidence workflow
Frequently asked questions
Does low quality data make a pack unusable?
Not necessarily. It can still be useful if limitations and assumptions are explicit. The pack should avoid presenting weak estimates as verified facts.
What is the fastest way to improve quality?
Improve shipment export completeness for distance, cargo weight, mode, fuel or vehicle context, and source evidence notes.
Related resources
Move from content to workflow
Use the public samples and tools to evaluate the workflow, then create a workspace or request async follow-up when real shipment data is ready.