Daimler Trucks successfully separated IT systems using graphic data technology... while ensuring operational efficiency and security

Daimler Truck( introduced graph database technology as part of the subsidiary split, which has been transformed into an internal achievement that enhances overall IT infrastructure visibility and efficiency. During the 2021 separation from Mercedes-Benz, Daimler needed to split over 1,500 IT systems and applications with decades of cross-dependencies. This was a large-scale project affecting 100,000 employees worldwide, 55,000 dealerships, and 6,000 business partners.

In the past, the company only recorded assets through simple Configuration Management Database)CMDB(, but it lacked information reflecting actual interaction structures or inter-system connections. To address these complex IT dependencies, Daimler turned to graph technology. Ultimately, the company adopted Neo4j’s graph database and combined telemetry data from enterprise network traffic analysis tool ExtraHop to visualize real-time operational relationships as nodes and edges.

This approach goes beyond simple documentation, building a “live real-time map.” It not only shows the connection structure between applications but also incorporates high-level representations of email relays, tax services, and touchpoints with legacy systems. This enabled early detection of unexpected dependencies during the split process, allowing seamless transitions.

Over three and a half years, Daimler migrated 130,000 mobile devices and 15,000 servers to its own data centers, reducing the total number of applications by about 40%, while also modernizing outdated financial, logistics, and HR systems. The company stated that by integrating large language models)LLM( into the graph database, even non-experts can easily understand questions like “Why did a system that was running normally yesterday encounter errors today” or “Where are the dependencies of this application.”

This graph-based approach not only supports short-term architecture splitting but also creates long-term value in security visibility and operational stability. For example, the company used this technology to proactively detect issues such as intermittent errors caused by missed certificate updates and unnecessary power consumption of legacy devices. It also successfully identified malicious proxy devices through graph analysis.

Ultimately, the technological choices made for the split became the foundation for sustainable digital transformation. Daimler Truck’s graph-based system visualizes the expected versus actual behavior of each application in real time and analyzes discrepancies, enabling early responses to unexpected failures and network threats.