.jpg?crop=yes&k=c&w=768&h=1024&itok=RQuXrvwo)
26.09.2025
How GEODIS Is Redefining their Data Practices to Build the Future of Logistics
Smarter Data to Smarter Logistics
In logistics, data is not just about tracking shipment or just a number; it is the compass guiding every strategic decision. Historically, Data has always been managed in isolated systems, all operating separately and accessible to the team or department directly responsible for the system. At GEODIS, we’re changing that narrative.
We have embarked on a transformation to reimagine how it is managed, shared, and used across our global operations. This change isn’t only about adding another tool or reporting system. It’s about laying the foundation for a future where customers experience faster responses, intelligent insights, and personalized service—all powered by better practices.
This transformation is not just technical, it is cultural. It is about empowering people to treat data as a product that evolves, scales and delivers value continuously.
From Silos to Scalable Insight
As a global organization, GEODIS operates in 166 countries spanning Europe, the Americas, and the APAC regions, with diverse business needs, platforms, and teams. Over time, the volume and complexity of our data have increased, making it challenging for users, especially those without technical expertise, to access, understand, and utilise this asset effectively.
We knew it was time for a shift - from treating data as a byproduct to managing it as a product. That’s where Data Products come in, a purpose-built solution designed to make data accessible, consumable, and actionable. It will support user needs, extend to all functional areas, and help build a unified environment within our decentralized organization.
What Are Data Products?
These are curated, custom-built datasets or services designed to solve specific problems or support decision-making. They range from simple table to more complex formats, such as customer segmentation models, real-time dashboards, recommendation engines, and APIs that deliver enriched insights.
Managing data with the same discipline as traditional products brings a more strategic and structured approach. It marks a shift in mindset, from viewing it as a secondary output to recognizing its value as a core asset that serves both internal and external consumers.
While the 'data-as-a-product' philosophy defines how it is governed, the true value lies in making data assets usable, high-quality, and relevant for decision-making.
The logic behind designing one is similar to creating a water bottle:
The Framework Behind the Shift
To enable this transformation at scale, we adopted the Data Mesh principle—a modern, federated approach to data management. It allows local and global workforce to operate independently while staying aligned through shared principles.
The four pillars of Data Mesh are:
- Domain-oriented ownership: Critical information is grouped into functional domains and each is managed independently by designated custodians.
- Data as a product: This involves applying product management principles to the way information is handled—focusing on clear ownership, comprehensive metadata, and structured cataloguing to enhance usability and trust.
- Self-service platform: A platform where users can autonomously manipulate, transform and access data.
- Federated computational governance: Each domain organizes their own governance, while a central coordination unit oversees that global guidelines and interoperability standards are followed.
Figure – Four Data Products complementary pillars at GEODIS
This framework is becoming the standard for large, distributed organizations. Adoption of Data Mesh increased sharply in 2023, rising by 65 percentage points, from just 13% to 78%, among the organizations surveyed compared to 2022 (Capgemini, 2024).
By equipping people with the right tools, training, and documentation, we aim to make data available and intuitive for everyone, regardless of their technical background.
From Vision to Rollout
This isn’t a one-off tech project—it’s a company-wide evolution. In collaboration with Capgemini, we’ve designed a two-phase roadmap to guide this shift:
1. Build the foundation: Create guidelines, choose the right tooling, and validate through pilot testing.
2. Deploy at scale: Drive communication, provide hands-on training, and expand adoption across all LoBs.
Our roadmap includes the development of 20+ Data Products spanning six distinct data domains by early 2026. This effort involves a wide range of stakeholders, from operations to data engineering. To support adoption, we launched an acculturation campaign that has already reached over 1,500 employees.
The deployment at scale required a tool that adapted to our specificities and could be easily adopted by new users. After auditing existing tools, we selected Data Mesh Manager by Entropy Data / INNOQ as our central Data Product catalogue. This all-in-one platform helps standardize documentation, improve discoverability, and ensure reusability. It will enable faster adoption by future users and reduce development workload.
We started small, working closely with pilot groups to co-develop simple use cases. As understanding grew, so did our ambition—extending the program to new teams at both group and Business Unit levels. The result is a scalable, federated model grounded in real practice, not just theory.
Building Operational Excellence Through Better Data
While the transformation is still underway, its benefits will soon be felt across every level of the company. Here’s what it will enable:
1. Accelerated Data-Driven Decisions
Accessible, well-structured data will reduce silos, speed up analysis, and improve decision quality—cutting time to market by up to 90% in some cases (Capgemini, 2024).
2. Optimized Operations
Better quality will support automation, accurate forecasting, and greater process efficiency—freeing people from manual effort to focus on higher-impact work.
3. Instill Product Thinking Logic into Practices
Treating it like a product ensures it's designed with users in mind. Clear ownership, documentation, and utility will drive consistency and trust.
4. Enhance Collaboration and Reusability
Each data asset is owned and managed with clear accountability, enabling the creation of roadmaps aligned with organizational priorities. Once created, it can be easily shared, serving as a reliable source for analytics and other use cases.
This model simplifies sharing, encourages collaboration, and enables up to 30% reuse between domains as reported by Opendatasoft (2024). It represents a crucial step toward building an open and connected network for seamless interaction with partners and customers.
5. Stronger AI Foundations
By organizing information into structured, reusable assets, we’re laying the groundwork for scalable AI. Models can be trained on consistent, trusted inputs, while AI agents gain access to the proper context to support accurate decisions and timely actions.
As Data Products gain traction, these benefits will become embedded in everyday operations, making GEODIS faster, smarter, and more connected.
What’s Next: An Ecosystem for Intelligent Supply Chains
Looking ahead, our focus is on:
- Expanding deployment to all LoBs and Regions with full support and resources needed to succeed.
- Evolving to meet real-world needs by refining solutions based on use cases and supporting our AI and innovation ambitions.
- Cultural transformation through training, communication, and product initiatives to build a data-centric mindset
This is not just an initiative; it’s an enabler of business-centric innovation. With every product we build, we move closer to an ecosystem that’s proactive, responsive, and ready for what’s next.
At GEODIS, we believe high-quality, structured data leads to smarter logistics. And smarter logistics lead to better outcomes for our people, our partners, and most importantly, our customers.