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Data-science-driven development projects are increasingly gaining the attention of small and medium sized enterprises. Since SME are often lacking the necessary competencies in data science, cooperation with other companies or universities is required. The efficient handling of data is one of the main challenges in joint cross-enterprise development projects. Actual cost driver is the development of data by labeling and classifying the data by domain experts, which is very time-consuming and labor-intensive with large amounts of data. Furthermore, clearance processes also have a high potential to cause delays before data can be shared with project partners. Moreover, before the actual work can begin, it is often necessary to clean up and repair incomplete or noisy data. The concept of Data Administration Shell presented in this paper addresses the challenge of structured information sharing and information management in joint cross-enterprise engineering. The Data Administration Shell links data sets to information regarding data origin and already performed analyses including their results and program scripts. Adding relations and documentation facilitates the reuse of data sets for subsequent projects. For this purpose, the Data Administration Shell adapts the concepts serving the information sharing in the research field of manufacturing and Digital Twin. The evaluation of the Data Administration Shell was based on time-series measurement data from a production process optimization scenario. Here, the Data Administration Shell manages the data sets of time series data and facilitates the joint cross-enterprise engineering of data-driven solutions.

The system development process is becoming increasingly complex as the systems themselves become more and more complex. At the same time, the various disciplines such as mechanical engineering, electrical engineering and software engineering are becoming increasingly intermixed, so that companies in one discipline are experiencing leaps and bounds in the complexity of their systems and their development. For this reason, this publication presents a concept of a virtual assistant that leads through a development phase. It shows that the software supporting the development needs an information model to store the data of the developed system and to connect it with the existing knowledge. This knowledge can either be available internally or on the web. The development process should therefore support cooperation so that the assistance software and engineers interact with each other.