What is it about?
Organizations rely on complex IT systems to run their operations and stay competitive. Enterprise Architecture (EA) is a way to design and manage these systems so they align with business goals, reduce risks, and improve efficiency. However, evaluating whether an organization's EA is effective remains a challenge. This study systematically reviewed 109 research papers to understand how EA is evaluated. It identified 54 different evaluation methods, 36 key assessment criteria, and 57 model notations used to analyze EA. The study found that while many evaluation methods exist, they are not widely adopted in practice due to the time and effort required for data collection and modeling. A key finding is that automating EA evaluation could make the process easier and more useful for businesses. The study also highlights gaps in current evaluation approaches and suggests future research directions. Ultimately, this research provides valuable insights for both academics and business leaders, helping them better understand EA evaluation and its role in improving decision-making and long-term business success.
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Why is it important?
EA is a critical tool for organizations to align their IT systems with business goals, manage complexity, and drive innovation. However, despite its importance, evaluating EA remains a challenge—most organizations lack structured methods to assess whether their architectures are effective, scalable, and secure. This study is the first comprehensive systematic review of EA evaluation methods, analyzing over 100 research papers to identify key approaches, criteria, and challenges. Unlike previous research, it provides a detailed breakdown of evaluation methods, examines how they are applied in real-world scenarios, and highlights critical gaps. The findings are timely because businesses are increasingly embracing digital transformation, cloud computing, and AI-driven decision-making, all of which require strong architectural foundations. EA evaluation can help organizations respond faster to change, optimize IT investments, and ensure their architectures support long-term success. By shedding light on existing methods and proposing future research directions, this work can influence how businesses, researchers, and policymakers approach EA evaluation, potentially leading to more efficient, data-driven, and standardized assessment practices.
Perspectives
EA has long been a cornerstone of IT and business strategy, yet its evaluation remains an underdeveloped area. Through this research, I wanted to bridge that gap by systematically analyzing how EA is assessed, what methods are used, and what challenges remain. One of the most striking findings is the lack of automation in EA evaluation, which makes the process time-consuming and difficult to scale in real-world organizations. I believe that the future of EA evaluation lies in automation and data-driven decision-making. Organizations need faster, more adaptive ways to assess their architectures, ensuring they remain agile and competitive in a rapidly evolving digital landscape. The insights from this study can help researchers and practitioners move toward that goal by providing a structured foundation for improving EA evaluation practices. Personally, this research has deepened my understanding of how EA can shape business success and how critical its evaluation is for making informed strategic decisions. I hope this work sparks further discussions and innovations, ultimately leading to more effective, widely adopted EA evaluation methods that drive real business value.
Norbert Rudolf Busch
Warsaw University of Technology
Read the Original
This page is a summary of: A Systematic Literature Review of Enterprise Architecture Evaluation Methods, ACM Computing Surveys, January 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3706582.
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