Digitalization and enterprise architecture management: a perspective on benefits and challenges

Abstract

Many companies digitally transform their business models, processes, and services. They have also been using Enterprise Architecture Management approaches for a long time to synchronize corporate strategy and information technology. Such digitalization projects bring different challenges for Enterprise Architecture Management. Without understanding and addressing them, Enterprise Architecture Management projects will fail or not deliver the expected value. Since existing research has not yet addressed these challenges, they were investigated based on a qualitative expert study with leading industry experts from Europe. Furthermore, potential benefits of digitalization projects for Enterprise Architecture Management were researched. Our results provide a theoretical framework consisting of five identified challenges, triggers and a number of benefits. Furthermore, we discuss in what ways digitalization and EAM is a promising topic for future research.

Activating supply chain business models' value potentials through Systems Engineering

“…63,99 In contradiction to its intention, EAM has emerged as an IT stand-alone approach separated from business domains, with a lack of integration between IT organizations and senior management. [100][101][102] In addition, the complexity of IT is rising caused by smart digitalization processes, 103 followed by high coordination efforts to avoid these barriers. 63,104 As a result, a holistic approach is missing to supporting small and medium enterprises (SMEs).…”

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

“…The plan of action for coordinating company goals with technological capabilities is called enterprise architecture (Möhring et al (2023), Gaurav et al (2023). EA provides enterprises with various advantages in the field of IoT and ML, including:…”

“…The plan of action for coordinating company goals with technological capabilities is called enterprise architecture (Möhring et al (2023), Gaurav et al (2023)). EA provides enterprises with various advantages in the field of IoT and ML, including: Integration and Interoperability :The seamless integration of numerous IoT devices, sensors, and ML algorithms across diverse business units is made possible by enterprise architecture.…”

“…The gathering, storing, integrating, and analytics of data are the main topics of the data architecture component. Addressing data quality issues, interoperability, and real‐time processing provides ML algorithms with a solid foundation (Möhring et al (2023)). Technology architecture: Technology architecture includes networking, hardware, and software to support IoT and ML applications (Jnr and Petersen (2023)). Building a strong technical environment considers connectivity, scalability, and device management. Application architecture: The creation and implementation of IoT and ML applications are the focus of this component.…”

“…The gathering, storing, integrating, and analytics of data are the main topics of the data architecture component. Addressing data quality issues, interoperability, and real‐time processing provides ML algorithms with a solid foundation (Möhring et al (2023)).…”