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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.

Abstract-Our work aims to rationalize Enterprise Architectures (EA) by providing the reasoning behind the designs, in terms of selection criteria, design alternatives and more. Its major contribution is a formal metamodel that captures the reasoning and the inter-relationships of design decisions.This paper extends our approach in order to provide an explicit bridging between the Problem space that is defined by the different requirements and the Solution space that is described by specific design decisions. In doing so, EA Anamnesis also supports traceability from specific design decisions to the given requirements.

Abstract. We aim for rationalizing Enterprise Architecture, supplementing models that express EA designs with models that express the decision making behind the designs. In our previous work we introduced the EA Anamnesis approach for architectural rationalization, and illustrated it with a fictitious case study. In this paper we evaluate our approach in terms of its ability to capture design rationales in the context of a real life case study. Together with stakeholders from the business and IT domains of a Luxembourgish Research and Technology Organization, we captured the design rationales behind the introduction of a new budget forecast business process. Our case study shows that EA Anamnesis can reflect the design rationales of the stakeholders, also linking business and IT concerns. Furthermore our study shows that, for this particular case, the stakeholders often used heuristics (commonsensical "short cuts") to make their decision, or even made decisions without considering alternative choices. Finally, we discuss what the lessons learned from this case imply for further research.

Abstract. Enterprise Architecture (EA) languages describe the design of an enterprise holistically, typically linking products and services to supporting business processes and, in turn, business processes to their supporting IT systems. In earlier work, we introduced EA Anamnesis, which provides an approach and corresponding meta-model for rationalizing architectural designs. EA Anamnesis captures the motivations of design decisions in enterprise architecture, alternative designs, design criteria, observed impacts of a design decision, and more. We argued that EA Anamnesis nicely complements current architectural languages by providing the capability to learn from past decision making.In this paper, we provide a first empirical grounding for the practical usefulness of EA Anamnesis. Using a survey amongst 35 enterprise architecture practitioners, we test the perceived usefulness of EA Anamnesis concepts, and compare this to their current uptake in practice. Results indicate that while many EA Anamnesis concepts are perceived as useful, the current uptake in practice is limited to a few concepts -prominently 'rationale' and 'layer'. Our results go on and show that architects currently rationalize architectural decisions in an ad hoc manner, forgoing structured templates such as provided by EA Anamnesis. Finally, we interpret the survey results discussing for example possible reasons for the gap between perceived usefulness and uptake of architectural rationalization.

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ArchiMate [4] is a Domain Specific Language (DSL) to model an enterprise from a holistic perspective, showing not only the IT infrastructure of an organization, but also how this IT infrastructure supports business processes and contributes to the realization of products and (commercial) services.Yet, ArchiMate lacks the capability to capture the design decisions behind the models. Capturing such decisions is important to improve the design teaching and communication after the design process. This is what we refer to as EA Anamnesis. People who can benefit from EA Anamnesis are e.g. persons that are foreign to a given architecture, such as external Enterprise architects.In this paper, we introduce an approach to capture design decisions. For the moment we target our approach primarily on capturing design decisions in the context of Archi-Mate models. Specifically, we (1) introduce a metamodel for capturing architectural design decisions. This metamodel is grounded in DSLs for capturing rationales in software engineering. (2) Finally, we provide a fictitious use case scenario for the insurance industry to illustrate the use of our approach.