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Data analytics/artificial intelligence and business process automation require an ever-increasing wealth of data. To remain competitive, organizations cannot just use internal and publicly available data sources, but need information also from external individuals and organizations. As, e.g., supply chains evolve into highly flexible supply and demand networks, much of the required data exchange cannot be prepared any longer by lengthy human negotiations but must be semi-automatically negotiated, executed and monitored for contractual and legal compliance. Furthermore, on top of exchanging data within a business network, an increasing number of innovative business services requires data sharing, i.e., the joint use of data from different sources within the network. An example is collaborative predictive maintenance which is based on the analysis of and learning from production process data from several manufacturing companies using the same production machinery. Every machine operator provides data in order to benefit from better maintenance services. The more operators contribute, i.e., share their data, the greater the benefit for each one of them. Technologies used today include, among other things, mappings between heterogeneous data source schemas, automated data transformation, fast parallel query processing and machine learning for information fusion, and blockchains for traceability. Several recent BISE special issues have addressed such topics individually. In practice, data exchange and sharing often happen via open or semi-open platforms, around which data and service ecosystems have evolved. Such platforms can be the intended side effect of main services such as search (e.g., Google), social networking (e.g., Facebook), entertainment (e.g., Netflix), or trade (e.g., eBay, Amazon, Otto), or much more specialized regional platforms such as the smart farming ecosystem around the farm equipment provider Claas in Germany. So far, the majority of ecosystems has been driven by keystone players. Due to network effects (the value of a network grows quadratically with the number of participants), these platforms often quickly reach monopolistic or at least oligopolistic situations. If knowledge-intensive user organizations operate on such platforms, the enormous amount of deep knowledge in their data can create threats for the competitive advantage of whole engineering-intensive industries, and makes them susceptible to disruptive new competitors from completely different industries. However, not taking advantage of digitalization and the resulting chances for data mining and process automation is obviously not an option either. Prof. Prof. (OM) Dr. M. Jarke (&) Information Systems, RWTH Aachen University, Ahornstr. 55, 52074 Aachen, Germany e-mail: jarke@dbis.rwth-aachen.de

Data Sovereignty and Data Space Ecosystems