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The internet of things, enterprise social networks, adaptive case management, mobility systems, analytics for big data, and cloud environments are emerging to support smart connected i.e. digital products and services and the digital transformation. Biological metaphors for living and adaptable ecosystems are currently providing the logical foundation for resilient run-time environments with serviceoriented digitization architectures and for self-optimizing intelligent business services and related distributed information systems. We are investigating mechanisms for flexible adaptation and evolution of information systems with digital architecture in the context of the ongoing digital transformation. The goal is to support flexible and agile transformations for both business and related information systems through adaptation and dynamical evolution of their digital architectures. The present research paper investigates mechanisms of decision analytics for digitization architectures, putting a spotlight to internet of things micro-granular architectures, by extending original enterprise architecture reference models with digitization architectures and their multi-perspective architectural decision management.
Organizations identified the opportunities of big data analytics to support the business with problem-specific insights through the exploitation of generated data. Sociotechnical 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 complex business and IT architectures. The transformation of an organization’s EA is influenced by big data transformation processes and their data-driven approach on all layers. In this paper, we review big data literature to analyze which requirements for the EA management discipline are proposed. Based on a systematic literature identification, conceptual categories of requirements for EA management are elicited utilizing an inductive category formation. These conceptual categories of requirements constitute a category system that facilitates a new perspective on EA management and fosters the innovation-driven evolution of the EA management.
discipline.
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.