Business Architectures for the Design of Enterprise Service Systems

Part of the Service Science: Research and Innovations in the Service Economy book series (SSRI)


Business Architecture provides foundational and actionable concepts for enterprise service systems and their transformation. In practical terms, Business Architecture is an approach to formalizing the way an organization operates based on the convergence among strategy management, business process management and information technology. Partial perspectives on this convergence have received a great deal of attention from different disciplines in the last two decades. Companies and industries in regimes of fast technological change and innovation have made Business Architecture gain new emphasis, and thus, the discipline has been recently revisited intensively by companies, government, analysts, standards organizations, and researchers.

Business Architecture comprises three core components or dimensions, namely, conceptual model, methodology and tooling. Thereby, the variety of Business Architecture perspectives is wide and applications depend on purpose of adoption, scope of usage, and overall maturity of specific concepts. As Business Architecture involves different concepts and it has a strong multidisciplinary nature, it is common to find “different Business Architectures” in the literature. However, it is the different contexts for its application what makes Business Architecture appear as distinct.

With the goal of providing some practical assessment, this chapter reviews ten approaches to Business Architecture from the literature and evaluates them according to proposed measures of strength and weakness. Emphasizing the service system nature of an enterprise, the evaluation makes emphasis on the service concept as a main constituent of Business Architecture.


Business Process Business Service Enterprise Architecture Business Process Management Business Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.IBM Research - AlmadenSan JoseUSA

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