The Mechanics of Enterprise Architecture Principles

Inspired by the city planning metaphor, enterprise architecture (EA) has gained considerable attention from academia and industry for systematically planning an IT landscape. Since EA is a relatively young discipline, a great deal of its work focuses on architecture representations (descriptive EA) that conceptualize the different architecture layers, their components, and relationships. Beside architecture representations, EA should comprise principles that guide architecture design and evolution toward predefined value and outcomes (prescriptive EA). However, research on EA principles is still very limited. Notwithstanding the increasing consensus regarding EA principles’ role and definition, the limited publications neither discuss what can be considered suitable principles, nor explain how they can be turned into effective means to achieve expected EA outcomes. This study seeks to strengthen EA’s extant theoretical core by investigating EA principles through a mixed methods research design comprising a literature review, an expert study, and three case studies. The first contribution of this study is that it sheds light on the ambiguous interpretation of EA principles in extant research by ontologically distinguishing between principles and nonprinciples, as well as deriving a set of suitable EA (meta-)principles. The second contribution connects the nascent academic discourse on EA principles to studies on EA value and outcomes. This study conceptualizes the “mechanics” of EA principles as a value-creation process, where EA principles shape the architecture design and guide its evolution and thereby realize EA outcomes. Consequently, this study brings EA’s underserved, prescriptive aspect to the fore and helps enrich its theoretical foundations

An Exploration of Enterprise Architecture Research

Management of the enterprise architecture has become increasingly recognized as a crucial part of both business and IT management. Still, a common understanding and methodological consistency seems far from being developed. Acknowledging the significant role of research in moving the development process along, this article employs different bibliometric methods, complemented by an extensive qualitative interpretation of the research field, to provide a unique overview of the enterprise architecture literature. After answering our research questions about the collaboration via co-authorships, the intellectual structure of the research field and its most influential works, and the principal themes of research, we propose an agenda for future research based on the findings from the above analyses and their comparison to empirical insights from the literature. In particular, our study finds a considerable degree of co-authorship clustering and a positive impact of the extent of co-authorship on the diffusion of works on enterprise architecture. In addition, this article identifies three major research streams and shows that research to date has revolved around specific themes, while some of high practical relevance receive minor attention. Hence, the contribution of our study is manifold and offers support for researchers and practitioners alike

A Technique for Annotating EA Information Models with Goals

Abstract. Many of today’s enterprises experience the need to establish and conduct management processes to ensure closely alignment between business and IT. Enterprise architecture (EA) management provides a model-based approach to understand and evolve the complex dependencies between the enterprise constituents, as e.g. business processes and business applications. In recent years the understanding of EA management in literature and in practice has converged, but up to this point no commonly accepted standard information model for EA management nor a standard set of goals verifying the overall objective of business/ITalignment have been devised. Grounded in indications that such models and goals are highly enterprise-specific, this paper presents a method for flexible combining EA-relevant goals and EA information models to optimally support EA management in a using company