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The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. The digitization of software-intensive products and services is enabled basically by four megatrends: Cloud computing, big data mobile systems, and social technologies. This disruptive change interacts with all information processes and systems that are important business enablers for the current digital transformation. The internet of things, social collaboration systems for adaptive case management, mobility systems and services for big data in cloud services environments are emerging to support intelligent user-centered and social community systems. Modern enterprises see themselves confronted with an ever growing design space to engineer business models of the future as well as their IT support, respectively. The decision analytics in this field becomes increasingly complex and decision support, particularly for the development and evolution of sustainable enterprise architectures (EA), is duly needed. With the advent of intelligent user-centered and social community systems, the challenging decision processes can be supported in more flexible and intuitive ways. Tapping into these systems and techniques, the engineers and managers of the enterprise architecture become part of a viable enterprise, i.e. a resilient and continuously evolving system that develops innovative business models.
The evolution of Services Oriented Architectures (SOA) presents many challenges due to their complex, dynamic and heterogeneous nature. We describe how SOA design principles can facilitate SOA evolvability and examine several approaches to support SOA evolution. SOA evolution approaches can be classified based on the level of granularity they address, namely, service code level, service interaction level and model level. We also discuss emerging trends, such as microservices and knowledge-based support, which can enhance the evolution of future SOA systems.
Many organizations identified the opportunities of big data analytics to support the business with problem-specific insights through the exploitation of generated data. Socio-technical solutions are developed in big data projects to reach competitive advantage. Although these projects are aligned to specific business needs, common architectural challenges are not addressed in a comprehensive manner. Enterprise architecture management is a holistic approach to tackle the complex business and IT architecture. The transformation of an organization's EA is influenced by big data projects and their data-driven approach on all layers. To enable strategy oriented development of the EA it is essential to synchronize these projects supported by EA management. In
this paper, we conduct a systematic review of big data literature to analyze which requirements for the EA management discipline are proposed. Thereby, a broad overview about existing research is presented to facilitate a more detailed exploration and to foster the evolution o the EA management discipline.