Enterprise Architecture

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An Application of Semantic Techniques to the Analysis of Enterprise Architecture Models

Enterprise architecture (EA) model analysis can be defined as the application of property assessment criteria to EA models. Ontologies can be used to represent conceptual models, allowing the application of computational inference to derive logical conclusions from the facts present in the models. As the actual common EA modelling languages are conceptual, advantage can be taken of representing such conceptual models using ontologies. Several techniques for this purpose are widely available as part of the semantic web standards and frameworks. This paper explores the use of the aforementioned techniques in the analysis of enterprise architecture models. Namely, two techniques are used to this end: computational inference and the use of SPARQL. The aim is to demonstrate the possibilities brought by the use of these techniques in EA model analysis.

The Enterprise and its Architecture: Ontology and Challenges

Enterprise Architecture (EA) is a set of concepts and practices based on holistic systems thinking, principles of shared language, and the long-standing disciplines of engineering and architecture. EA represents a change in how we think about and manage information technologies (ITs) and the organizations they serve. Many existing organizational activities are EA-type activities, but done in isolation, by different groups, using different tools, models, and vernaculars. EA is about bridging the chasms among these activities, from strategy to operations, and better aligning, integrating, optimizing, and synergizing the whole organization. This article: (1) posits that EA is about the architecture of the entire enterprise including its ITs

Institutionalization of Contested Practices: A Case of Enterprise Architecture Implementation in a US State Government

Information Systems (IS) practices are often ‘institutionally contested’ when introduced into organizations. They run counter to the status quo and disrupt organizational stability. Furthermore, they contravene the normative, regulatory, and cultural-cognitive legitimacy in existing institutionalized processes. This research explores contested practices, examining the struggles and techniques IS organizations use to legitimize and institutionalize them. Using an institutional change and translation perspective, we investigate a case of Enterprise Architecture (EA) implementations in a US state government, highlighting the struggles in translating new practices to connect to potential users and in connecting new practices to existing norms, regulations, and cultural values. We elucidate two key techniques to overcome these struggles: inductive communication to make new practices relatable to users, and the deployment of experts to local contexts to facilitate knowledge transfer. The research shows how institutional change unfolds and informs practitioners of how to legitimize EA practices.

Revisiting the Impact of Information Systems Architecture Complexity: A Complex Adaptive Systems Perspective

Organizations constantly adapt their Information Systems (IS) architecture to reflect changes in their environment. In general, such adaptations steadily increase the complexity of their IS architecture, thereby negatively impacting IS efficiency and IS flexibility. Based on a Complex Adaptive Systems (CAS) perspective, we present a more differentiated analysis of the impact of IS architecture complexity. We hypothesize the relation between IS architecture complexity on the one hand, and IS efficiency and IS flexibility on the other hand to be mediated by evolutionary and revolutionary IS change. Subsequently, we test our hypotheses through a partial least squares (PLS) approach to structural equation modelling (SEM) based on survey data from 185 respondents. We find that the direct negative impact of IS architecture complexity on IS efficiency and IS flexibility is no longer statistically relevant when also considering the mediating effects of revolutionary and evolutionary IS change.

Organizational Learning Supported by Reference Architecture Models: Industry 4.0 Laboratory Study

The wave of the fourth industrial revolution (Industry 4.0) is bringing a new vision of the manufacturing industry. In manufacturing, one of the buzzwords of the moment is “Smart production”. Smart production involves manufacturing equipment with many sensors that can generate and transmit large amounts of data. These data and information from manufacturing operations are however not shared in the organization. Therefore the organization is not using them to learn and improve their operations. To address this problem, the authors implemented in an Industry 4.0 laboratory an instance of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory by collecting and sharing up-to-date information concerning manufacturing equipment. This article discusses and generalizes the experience and outlines future research directions.

Research Perspective in Enterprise Architecture

The challenges of aligning IT with business triggered the attention towards Enterprise Architecture (EA). Despite the increase interest of academic scholars in EA, there is scarcity of studies that provide an up to date comprehensive research perspective view. The purpose of this study is to examine the research methodologies and theories utilized in EA studies from 2010 to 2016. The study employed Systematic Literature Review (SLR) as method to explore and analyze the literature of EA. The study revealed the research approaches and data collection methods utilized in EA. It shows that case study approach and interviews are the highly used compared to other research approaches and data collection instruments. Furthermore, it pointed out the low employment of theories in EA studies. The study is contributing to the body of knowledge by providing a foundation for novice researchers in the area of EA through detailed discussions of research methodologies and theories which are expected to support them in designing future studies.