Logistics
When your data can’t be trusted
With 3,500+ cars on the road, Greenwheels needed insight but had clashing KPI definitions and slow legacy tooling.
Client
Greenwheels
Industry
Logistics
Service
Data Quality Improvement, Data Platform Migration
Stack
Snowflake, dbt, Tableau
Market
Netherlands
The challenge
Plenty of data. Not enough confidence.
Greenwheels had no shortage of demand for data-driven insights, but trust in the data was low. Different teams reported the same KPIs using different definitions and arriving at different numbers. There was no single source of truth, and data from core systems was often inconsistent, incomplete, or incorrect.
At the same time, the company’s legacy tooling was struggling to keep up. Reporting was slow, difficult to maintain, and a constant source of frustration for both business users and the data team.
Leadership saw an opportunity to change this. Their ambition was clear: build trust in the data, modernize the platform, and turn data into a competitive advantage.
“It was a pleasure working with POINT05. They are flexible and versatile data & analytics experts leading our migration from legacy tools to a modern analytics stack, setting up data governance and hiring data & analytics professionals for our internal team.”
Robin de Weert, CPO, Greenwheels
The approach
A transformation across processes, people, and technology
We approached this as an organizational transformation, not just a technical upgrade. That meant working across three dimensions simultaneously:
Implemented data governance with clear KPI definitions and ownership
Built a modern analytics platform with automated data quality checks
Supported the hiring process to expand and upskill the internal data team where needed
On top of this we also set up a self-service BI environment and ran training sessions for internal teams.
One platform, trusted data and a team that owns it.
A data governance framework with aligned KPI definitions across all teams
A modern, cloud based analytics platform replacing legacy tooling. Radically cutting costs, reducing platform outages and calculation time
Automated data quality checks embedded across the entire data pipeline. From source system to management report
Support hiring process for an analytics engineer. Reviewing applications, designing a technical assessment and conducting interviews.
Is everyone in your company looking at different numbers?
Let’s change that.