EA Anamnesis

Abstract

Enterprise Architecture (EA) modeling languages can express the business-to-IT-stack for an organization, showing how changes in the IT landscape impact business aspects and vice versa. Yet EA languages provide only the final architectural design, not the rationale behind this design. In earlier work, the authors presented the EA Anamnesis approach for EA rationalization. The authors discussed how EA Anamnesis forms a complement to current EA modeling languages, showing for example design alternatives, EA artifact selection criteria and the decision making strategy that was used. In this paper, the authors extend EA Anamnesis with a capability for organizational learning. In particular, the authors present an integration of two viewpoints presented in earlier work: (1) an ex-ante decision making viewpoint for rationalizing EA during decision making, which for example captures a decision and its anticipated consequences, and (2) an ex-post decision making viewpoint, which for example captures the unanticipated decision consequences, and possible adjustments in criteria. The authors use a fictitious, yet realistic, case study to illustrate our approach.

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