Key Takeaways
- Daimler Truck Holding AG used graph database technology to separate its IT systems from Mercedes-Benz Group AG, mapping over 1,500 IT systems and applications.
- The graph database provided a living model of how systems interacted, allowing for the identification of hidden dependencies and the development of a separation plan.
- The transition took 3.5 years to complete, resulting in the migration of 130,000 mobile devices, 15,000 servers, and a 40% reduction in the total number of applications.
- The graph database has become a strategic asset, providing visibility into the IT landscape and enabling the detection of security risks and abnormal behavior.
- The use of a large language model on top of the graph has allowed users to ask questions and get answers without needing to know technical details.
Introduction to the Challenge
When Daimler Truck Holding AG began the process of separating from Mercedes-Benz Group AG in 2021, it faced a daunting problem. Decades of tightly interwoven information technology systems, shared infrastructure, and undocumented dependencies needed to be untangled. More than 1,500 IT systems and applications needed to be separated, redesigned, or replaced. The changes would affect 100,000 employees worldwide, 55,000 dealers, and 6,000 business partners. Simply cutting the cord between the two companies was not an option, as an unanticipated dependence could bring down critical applications on both sides, with no clear way to diagnose or recover from failures.
The Limitations of Traditional Configuration Management
Like many large enterprises, Daimler AG relied on a configuration management database (CMDB) to track applications and infrastructure. However, the CMDB lacked context, documenting applications as collections of servers, but often missing real-world dependencies such as DNS entries, proxies, databases, email relays, tax services, and connections to legacy systems. These dependencies were often missing or outdated, making it difficult to understand how applications behaved in practice. Manual analysis was not a realistic option, with Daimler Truck estimating that it would cost over $1 million and months of effort to hire contractors to map dependencies by hand.
The Solution: Graph Database Technology
Daimler Truck turned to graph database technology to map its application landscape as a living model of how systems interacted. The company chose Neo4j Inc.’s graph database technology to model its IT environment as an ontology, or a structured representation of entities and their relationships. Network telemetry from ExtraHop Networks Inc. provided a foundation, capturing live traffic flows across the enterprise and identifying client-server communications, ports, protocols, and behavioral signals. Daimler Truck built an extract/transform/load pipeline using Go to sample and transform millions of flows into graph relationships.
The Benefits of the Graph Database
The graph database provided a continuously updating model of how applications operated on the network, allowing Daimler Truck to identify which applications belonged to the truck business, which belonged to Mercedes-Benz, and where they overlapped. The graph reduced the unknowns, which are the riskiest factor in large-scale IT transitions. The transition took 3.5 years to complete, resulting in the migration of 130,000 mobile devices, 15,000 servers, and a 40% reduction in the total number of applications. The graph database has become a strategic asset, providing visibility into the IT landscape and enabling the detection of security risks and abnormal behavior.
Long-Term Value and Future Developments
The graph database has proven to be a valuable tool beyond the initial separation of the two companies. It has provided a foundation for observability, security insight, and application intelligence across the newly independent company. The use of a large language model on top of the graph has allowed users to ask questions and get answers without needing to know technical details. The graph has also exposed underused and unknown assets, legacy servers without a clear owner could be monitored and safely taken down, rather than left running indefinitely out of fear of breaking something. Security risks have become easier to spot, as abnormal behavior stands out clearly against expected relationships. The graph database has become a lasting visibility into how applications work, how they change, and how failures propagate, benefits that extend well beyond the original goal of separating two companies.
Conclusion and Future Opportunities
In conclusion, Daimler Truck Holding AG’s use of graph database technology to separate its IT systems from Mercedes-Benz Group AG has been a successful and strategic move. The graph database has provided a living model of how systems interacted, allowing for the identification of hidden dependencies and the development of a separation plan. The long-term value of the graph database has been evident, providing visibility into the IT landscape and enabling the detection of security risks and abnormal behavior. As the company continues to evolve and grow, the graph database will remain a vital tool, providing a foundation for observability, security insight, and application intelligence.