It is imperative to master some central aspects of Big Data in Smart Ecosystems if such systems are to become reality. One of the challenges in practice will be, for example, to find suitable infrastructures: In Smart Ecosystems, companies of various sizes are collaborating in an ecosystem. For the analysis of the data, a Big Data infrastructure is provided in these companies with appropriate computing power. This entails several challenges for such companies. Particularly small and medium-sized companies might not be willing or able to purchase a dedicated infrastructure. Here, new solution strategies must be found: On the one hand, the trend towards storing data in the Cloud offers first approaches for Big Data analyses. For the study of sensitive data, temporary infrastructure lease approaches are also conceivable. Both approaches can be thought of as Big Data analyses “on demand”. Another problematic issue continues to be the progressive establishment of different, partly incompatible technologies for use in Big Data analyses: Currently a heterogeneous landscape of Big Data providers is developing, which manifests itself differently in the Smart Ecosystems of different companies. For cross-company analyses, however, de-facto standards in an ecosystem, compatible interfaces between provider systems, and a suitable, highly performant intermediate layer for data exchange must be established.
Establishment of Innovative Business Models: It is a generally accepted fact that innovative business models are a central issue in Smart Ecosystems. In order to realize these, new, partly not yet existent service providers should be founded. One example would be the provision of the above-mentioned so-called “on-demand” analyses. However, for potential stakeholders in such an innovative business model in a Smart Ecosystem, questions frequently arise regarding the issue of risk management, respectively the feasibility of such a business model. If research develops simulation environments for such Smart Ecosystems in the sense of rapid-prototyping environments, the question about added value can be answered better. Once processes and technologies are more mature and can guarantee data protection for the analysis and its data, the inhibiting barriers will come down.
Standardization: To achieve the goal of an efficient exchange of data among companies and between the analysis results obtained with different technologies, standardization of the data, of their modeling processes, and of the specification of data qualities would be a very helpful. Many standardization processes nowadays take place in specific domains, e.g., in mechanical engineering, in the automotive industry, or in the financial sector. It is characteristic of the Smart Ecosystems of tomorrow, however, that stakeholders from a diversity of domains are involved in an ecosystem, with the number of stakeholders in the ecosystem varying widely. This can make it very hard to achieve standardization quickly and thus constitutes one of the greatest challenges.
The numerous conceivable application scenarios in a Smart Ecosystem cannot be regarded independent of each other, but should rather be considered an excerpt of a continually evolving system. In this system, new services and organizations are added and replace others over time. This intertwining can already be observed today in the area of energy and electromobility, where electric vehicles consume energy on the one hand, but can also be used for decentralized energy storage on the other hand. Another example is the interconnection between production technology and intelligent mobility systems. Here, the goal is to reduce the transport and waiting times of goods and to be able to react flexibly to the re-planning of production processes.