Ensuring consistent data usage throughout the data lifecycle in engineering research activities is crucial for industrial companies and scientific institutions. Despite thematic similarities, industrial and scientific environments differ significantly in their structure and organization of data management. These domain-specific differences directly impact data management practices and consistency. A deeper understanding is essential for realizing synergies between industry and science and for sustainably promoting data-driven innovation. The aim of this study is therefore to systematically analyze differences and challenges in data management and consistency between industrial and scientific engineering practices and derive potential for mutual improvement. The analysis is conducted qualitatively based on two case studies. For the industrial context, a data landscape was created through expert interviews at a company providing product-service systems. In the scientific sector, a collaborative research center with several subprojects was examined through interviews, workshops, and document analyses, and both domains were compared. From this comparison, two methodological approaches emerge, a top-down approach derived from the industry use case, to enable diverse analytical insights, thereby creating a large but often heterogeneous database whose potential needs to be exploited. The scientific use case, on the other hand, take a bottom-up approach, collecting and processing data in a targeted and project-oriented manner, with a stronger focus on quality and traceability. Yet, this project-based orientation makes long-term interoperability and sustainable reuse in the research community more difficult. Significant potential for industrial companies arises if data is increasingly understood as a strategic asset and specifically prepared for long-term reuse, similar to the principles applied in science. This approach increases the relevance of quality dimensions and, at the same time, creates the basis for establishing data value. Scientific institutions can benefit from industrial experience in standardization and scalability, enabling both sides to significantly optimize their data management practices through mutual alignment.
Wawer et al. (Thu,) studied this question.