Every logistics leader has heard the promise of a “single source of truth” in transport data.

The nature of transport data however can present a complex picture of forwarder feeds, spreadsheets, and ERP extracts each with its own format and gaps. Managing that sprawl creates its own challenges, from missing fields to inconsistent reporting and endless manual work. Transport data projects can stall long before they deliver value, get stuck on the IT department or analyst teams’ desk as disagreements over scope and third party delays creep in.

So, how to fix it? Let’s dive in.

The 5 Reasons Logistics Data Projects Fail

1. No unified data model

Without a shared structure for all carriers, modes, and regions, teams end up managing endless one-off feeds, each with its own quirks. Reports become a nightmare of manual mapping and re-mapping.

2. Incomplete or inconsistent source data

Key fields like mode, lane, or distance are often missing or stored in incompatible formats. Without automated validation, inaccuracies cascade into every report and decision.

3. Over-reliance on manual processes

Spreadsheets are powerful, but they are not infrastructure. Manual cleaning and uploads introduce errors, slow analysis, and make scaling impossible.

4. Isolated reporting silos

Finance, operations, sustainability, and sales often work from different datasets. That means conflicting numbers, duplicated effort, and wasted analyst time.

5. IT bottlenecks

Traditional projects demand IT bandwidth for integrations, transformations, and testing. When those resources are stretched, your go-live slips from weeks to quarters, or worse, gets shelved entirely.

The Rescue Plan

At Kinver, we’ve refined a fast-track approach to get any organisation from scattered data to a clean, enriched, API-delivered dataset, fast. No more big IT projects or change-management marathons.

Step 1 – Ingest 

Automate collection from carrier portals, forwarder feeds, TMS platforms, ERP extracts, and even spreadsheets.
Outcome: All transport data, all modes, one inbound stream.

Step 2 – Clean and validate

Every shipment record is checked for completeness, corrected where possible, and flagged for review if missing critical fields.
Outcome: No more silent errors or bad data in your reports.

Step 3 – Normalise to a single model

Events, references, and units are mapped to one unified data model, regardless of carrier format. This is the foundation for accurate cost allocation, SLA tracking, and emissions reporting.
Outcome: Consistency you can trust across all modes and geographies.

Step 4 – Enrich with business-critical metrics

Layer in lane-level cost, time-in-transit, and certified CO₂ per shipment. Every figure is tied to its source data for full audit traceability.
Outcome: Finance, ops, and ESG teams working from the same facts.

Step 5 – Deliver data wherever you need it

API-push or export straight into your ERP, BI, ESG, or customer portals.
Outcome: Data ready for decisions, reporting, and customer delivery without switching tools.

Why an API-First Approach Works

• No IT backlog - our API integrates directly with existing systems
  
• Proven data model with 40+ carrier formats readily available
  
• Audit-ready outputs aligned with ISO 14083 and CSRD
  
• Immediate ROI with customers cutting data prep by up to 90% from day one
  
  

Bottom Line

If your logistics data project has been stuck in neutral, the fix is not another committee meeting. It’s a clean, unified data stream that works in your business today.
‍
Want to learn more? See integrations

Ready to take action? Get started