It is imperative to master some central aspects of Big Data in Digital Ecosystems if such systems are to become reality. One of the challenges in practice will be, for example, to find suitable infrastructures:
In Digital 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 companies.
Small and medium-sized companies, in particular, 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 analytics. To examine sensitive data, temporary infrastructure lease approaches are also conceivable. Both approaches can be thought of as Big Data analytics “on-demand”.
Another problematic issue continues to be the progressive establishment of different, partly incompatible technologies for Big Data Analytics:
Currently, a heterogeneous landscape of Big Data providers is developing, which manifests itself in different ways in the Digital Ecosystems of different companies. For cross-company analyses, however, it is necessary to establish de-facto standards in an ecosystem, compatible interfaces between provider systems, and a suitable, highly performant intermediate layer for data exchange.
Establishment of innovative business models
It is a generally accepted fact that innovative business models are a central issue in Digital Ecosystems. In order to realize these, new, partly not yet existent service providers would need to become established. One example would be the provision of the above-mentioned “on-demand” analyses. However, potential stakeholders in such an innovative business model in the Digital Ecosystem are often faced with the question of risk management, respectively the viability of such a business model. If research develops simulation environments for such Digital Ecosystems in the sense of rapid-prototyping environments, the question of added value can be answered better. Once processes and Big Data technologies are more mature and can guarantee data protection for analytics and its data, the inhibiting barriers will come down.
To achieve efficient data exchange among companies as well as 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 very helpful. Many standardization processes are nowadays taking place in specific domains, e.g., in mechanical engineering, in the automotive industry, or in the financial sector. However, it is characteristic of the Digital Ecosystems of tomorrow that stakeholders from a wide variety 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 Digital Ecosystem can no longer 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 energy sector and in the area of 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 smart mobility systems. Here, the goal is to reduce the transportation and waiting times of goods and to be able to react flexibly to the re-planning of production processes.