Understanding Data Fluency in Self-Maintaining Data Ecosystems Introduction Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project. Background TRATON GROUP is one of the world’s leading commercial vehicle manufacturers, with brands including Scania, MAN, International and Volkswagen Truck & Bus. Its portfolio covers light commercial vehicles, trucks and buses, complemented by financing, charging and digital logistics services. Through its global operations, production sites and extensive sales and service networks, TRATON has access to diverse vehicle platforms, real-world operational data and fleet deployment environments. This provides a strong foundation for developing and validating innovative solutions for sustainable and efficient transportation. We are a cross-functional enablement team within TRATON R&D, responsible for data platforms, AI strategies, and human-centered technology development. Our mission is to empower R&D engineers to use trustworthy data and smart tools to make faster, better decisions. You’ll work closely with experts in AI, product development, and data engineering– in a context where your research can directly shape the way we build the future of transportation. Target Scope What will Battery & Charging R&D look like as domain teams build strong data capabilities and data systems increasingly communicate with one another? Many current visions of AI-enabled work emphasise speed, efficiency and reduced human involvement. Less is known about how domain teams’ functions and skills may change as systems take on more responsibility, and how to develop systems that remain resilient as they become more connected and automated. Objective This thesis will explore a plausible five-to-ten-year horizon in Battery & Charging R&D. It will examine what resilience means for data systems and infrastructures that exchange information, monitor processes and adapt over time: how they can remain reliable as conditions change, how their dependencies affect one another, and how people can understand when systems need attention or intervention. The study will also consider how the roles, responsibilities and skills of domain experts may change as they work with and oversee these systems. A current initiative such as Nest may provide a point of reference for understanding this transition. The focus, however, is on the future of B&C domain work and on developing resilience, not only on how engineers use AI tools. The research will consider how AI-enabled and data-enabled futures relate, and how different expectations about them shape plans for investment and capability development. Potential Contribution The thesis aims to help B&C develop a more grounded view of how to build resilient data systems and prepare resilient engineering teams as data systems become more connected and autonomous by incorporating strategic foresight p
