Systems Thinking

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Processes of Sense-Making and Systems Thinking in Government EA Planning

This purpose of this article is to investigate the systemic properties of Enterprise Architecture Planning (EAP) in the Australian government sector. Based on a case study of the Land and Property Management Authority of New South Wales, the article examines and outlines the crucial necessity for including systems thinking, systems learning, and organizational sense-making in Enterprise Architecture (EA) theory and planning. The main argument is based on qualitative research into the limitations of capturing and modeling organizations using EA methodologies and modeling approaches. The EA discipline, including its tools and methodologies, relies on the metaphor of engineering the enterprise and building stable taxonomies of knowledge and process. The practical reality that e-government programs are facing is technical, sociological, and messy. However, EA tends to operate within an engineering metaphor that assumes stability, predictability, and control. Here, the author highlights the necessity of an alternative, less positivist approach to EA planning in order to understand and articulate the tacit knowledge dimensions and messy, wicked problems of organizational life. Soft systems thinking, socio-technical theory, and sense-making are introduced as theoretical and practical frames to overcome these limitations and produce a better, more viable and realistic model of planning in government enterprises. These concepts are finally amalgamated into a general, integrative model of EA planning.

Re-thinking Enterprise Architecture Using Systems and Complexity Approaches

This article looks at the issues currently confronting enterprise architects and the challenges posed when extending EA to be the architecture of the enterprise rather than just its information technology. It describes the contribution that Systems Practice and other disciplines can make to Enterprise Architecture (EA), and considers how the Cynefin sense-making framework can be used to help indicate which are the most appropriate types of approach.