Enterprise architecture (EA) is a description of an enterprise from an integrated business and IT perspective. Enterprise architecture management (EAM) is a management practice embracing all the management processes related to EA aiming to improve business and IT alignment. EAM is typically described as a sequential four-step process: (i) document the current state, (ii) describe the desired future state, (iii) develop the transition plan and (iv) implement the plan. This traditional four-step approach to EAM essentially defines the modern understanding of EA. Based on a literature review, this paper demonstrates that this four-step approach to EAM, though practiced by some companies, is inadequate as a model explaining the EAM phenomenon in general. As a substitute, this paper synthesizes the generic conceptual model of EAM providing a more realistic conceptualization of EAM describing it as a decentralized network of independent but interacting processes, artifacts and actors.

Enterprise architecture (EA) is a description of an enterprise from an integrated business and IT perspective intended to improve business and IT alignment, and is used in the majority of large companies. However, despite that EA was established as an independent discipline long ago, a commonly accepted “big picture” in EA research is still missing. As a result, the scope of the EA discipline is vague, the extent of diversity in EA publications is poorly understood and the progression of the EA research stream over time is unclear. In this paper I conduct a comprehensive EA literature review covering 1075 publications aiming to structure, clarify and consolidate the whole EA research stream. I analyze the distribution of the EA research stream by time, source, research methodology and attitude, code all the issues discussed in EA publications into 42 narrow EA-related topics and 11 broader themes, establish the conceptual relationship between them and present a picture of the EA discipline “on a page”. Moreover, I classify all EA-related topics into four categories according to their lifecycles helping future EA researchers to better understand the evolution of the EA discipline and make the maximum contribution to it.