Lessons from Volvo and BMW: How Car Companies Really Integrate Their Systems

Integrating systems in the automotive industry is much more complex than just data mapping between standard business systems like ERP or CRM. A good real-world example is the collaboration between Volvo and BMW in Vehicle-to-Everything (V2X) and telematics services, where cloud platforms, ECUs, telematics units, and ADAS systems all need to work together. These projects require attention not only to data structures but also to protocols, security, and real-time operation.

https://www.energy-storage.news/bmw-volvo-step-up-interest-in-bi-directional-charging-and-vehicle-to-x-use-cases/

Key Differences in Automotive System Integration

Looking at Volvo and BMW, we can see several things that make car system integration different from standard data integration:

Variety of systems -> ECUs, telematics, multimedia, ADAS, each using different protocols (CAN, LIN, Ethernet).

Real-time data -> sensors and control systems need instant responses; batch updates alone are not sufficient.

Security and standards –> ISO 26262, AUTOSAR, certified protocols, and encryption are required.

Complex data –> binary packets, telemetry, and multiple parameters with interdependencies.

Version compatibility –> ECUs and software may have different versions, so integration should ensure compatibility.

Approaches for the Discovery Phase

For systems analysts planning integration in automotive projects, the following methods are especially useful during the discovery phase:

1. Integration Context Mapping –> visual maps showing how systems and modules interact, including data flows.

2. Interface Analysis –> detailed study of interfaces, protocols, message formats, and security requirements.

3. Data & Protocol Mapping –> matching parameters and formats between systems, including unit conversions and range checks.

4. Sequence Modeling –> diagrams showing the order of messages and system reactions in real time.

5. Gap Analysis & Compatibility Assessment -> identifying version mismatches, protocol differences, and standards gaps.

6. Proof of Concept (PoC) –> pilot testing integration points to verify data flow and timing.

Conclusion

The discovery phase in automotive system integration goes beyond simple data mapping. System Analysts should understand technical system features, protocols, security, and version compatibility.
Using structured analysis methods and visual models helps plan integrations correctly and reduces risks when connecting complex automotive systems.