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The development and evaluation of In-Vehicle Information Systems (IVISs) is strongly based on insights from qualitative studies conducted in artificial contexts (e.g., driving simulators or lab experiments). However, the growing complexity of the systems and the uncertainty about the context in which they are used, create a need to augment qualitative data with quantitative data, collected during real-world driving. In contrast to many digital companies that are already successfully using data-driven methods, Original Equipment Manufacturers (OEMs) are not yet succeeding in releasing the potentials such methods offer. We aim to understand what prevents automotive OEMs from applying data-driven methods, what needs practitioners formulate, and how collecting and analyzing usage data from vehicles can enhance UX activities. We adopted a Multiphase Mixed Methods approach comprising two interview studies with more than 15 UX practitioners and two action research studies conducted with two different OEMs. From the four studies, we synthesize the needs of UX designers, extract limitations within the domain that hinder the application of data-driven methods, elaborate on unleveraged potentials, and formulate recommendations to improve the usage of vehicle data. We conclude that, in addition to modernizing the legal, technical, and organizational infrastructure, UX and Data Science must be brought closer together by reducing silo mentality and increasing interdisciplinary collaboration. New tools and methods need to be developed and UX experts must be empowered to make data-based evidence an integral part of the UX design process.

In automation plants, technical processes must be conducted in a way that products, substances, or services are produced reliably, with sufficient quality and with minimal strain on resources. A key driver in conducting these processes is the automation plant’s control software, which controls the technical plant components and thereby affects the physical, chemical, and mechanical processes that take place in automation plants. To this end, the control software of an automation plant must adhere to strict process requirements arising from the technical processes, and from the physical plant design. Currently, the validation of the control software often starts late in the engineering process in many cases – once the automation plant is almost completely constructed. However, as widely acknowledged, the later the control software of the automation plant is validated, the higher the effort for correcting revealed defects is, which can lead to serious budget overruns and project delays. In this article we propose an approach that allows the early validation of automation control software against the technical plant processes and assumptions about the physical plant design by means of simulation. We demonstrate the application of our approach on the example of an actual plant project from the automation industry and present it’s technical implementation