A Comparative Study of Enterprise Architecture Frameworks for Cloud Adoption: Evaluating TOGAF, Zachman, and FEAF for Large Enterprises
Keywords:
enterprise architecture, TOGAFAbstract
This research paper presents a comparative analysis of three prominent enterprise architecture frameworks—The Open Group Architecture Framework (TOGAF), the Zachman Framework, and the Federal Enterprise Architecture Framework (FEAF)—in the context of cloud adoption for large enterprises. As organizations increasingly migrate to cloud-based infrastructures, a structured approach to enterprise architecture (EA) becomes essential for ensuring alignment between business strategies and IT capabilities, optimizing resource utilization, and mitigating risks associated with cloud transformation. This paper examines the critical elements of each framework and evaluates their suitability for guiding cloud adoption strategies, with a focus on their structural components, adaptability, and capability to manage complex, large-scale cloud migration initiatives.
The analysis begins by exploring the foundational aspects and architectural dimensions of TOGAF, Zachman, and FEAF, each of which offers unique methodologies, perspectives, and deliverables. TOGAF, known for its flexible yet structured approach, provides a comprehensive framework through its Architecture Development Method (ADM), which facilitates step-by-step architectural planning and governance. Its emphasis on iterative development cycles and modularity offers significant advantages for cloud transitions, particularly by enabling architects to address evolving requirements and integration challenges. In contrast, the Zachman Framework is a taxonomy-oriented approach that organizes enterprise architecture through six fundamental interrogatives (What, How, Where, Who, When, and Why) and six levels of reification, from conceptual to detailed levels. This structure offers a versatile, cross-sectional view of the enterprise, fostering a holistic understanding of business and technical needs essential for cloud adoption. However, due to its conceptual nature, Zachman often requires complementary methodologies to operationalize its architecture for cloud initiatives, which can present challenges in large-scale implementation.
The paper further analyzes the Federal Enterprise Architecture Framework (FEAF), developed for government entities but increasingly applied across sectors for its rigorous, governance-centric approach to enterprise architecture. FEAF’s reference models (Performance, Business, Service, Data, and Technical) create a cohesive framework that enables organizations to structure their architecture around shared goals and compliance requirements. In the context of cloud adoption, FEAF is evaluated for its strengths in supporting inter-agency collaboration, data standardization, and regulatory adherence—factors that are often critical for large enterprises operating in highly regulated industries. The comparative analysis thus highlights each framework’s strengths and limitations, particularly concerning scalability, adaptability to cloud-native paradigms, and support for governance and compliance in cloud migration projects.
In addition to structural comparison, the study examines how each framework addresses critical aspects of cloud adoption, including interoperability, security, cost efficiency, and agility. For instance, TOGAF’s ADM offers detailed guidance on interoperability and integration, essential for ensuring that cloud and legacy systems can coexist seamlessly. Similarly, the Zachman Framework’s layered approach supports the mapping of cloud-specific elements across different enterprise views, which can enhance the alignment between cloud services and business objectives. Meanwhile, FEAF’s emphasis on governance provides strong alignment with the compliance requirements inherent to cloud adoption, particularly in public sector and regulated industries.
This study concludes with a discussion on the strategic considerations that large enterprises must evaluate when selecting an enterprise architecture framework for cloud adoption. Key findings underscore the importance of choosing a framework that aligns with the organization’s cloud maturity, operational complexity, and regulatory environment. The paper also identifies emerging trends in EA practices that could influence future framework adaptations, including the integration of cloud-native architectures, microservices, and DevOps practices into EA methodologies. Overall, this research contributes to the discourse on enterprise architecture for cloud transformation, providing insights that can guide large enterprises in selecting and adapting EA frameworks to effectively navigate the complexities of cloud migration.
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References
J. A. Zachman, "A Framework for Information Systems Architecture," IBM Systems Journal, vol. 26, no. 3, pp. 276–292, 1987.
Ratnala, Anil Kumar, Rama Krishna Inampudi, and Thirunavukkarasu Pichaimani. "Evaluating Time Complexity in Distributed Big Data Systems: A Case Study on the Performance of Hadoop and Apache Spark in Large-Scale Data Processing." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 732-773.
Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.
Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.
Machireddy, Jeshwanth Reddy. "ARTIFICIAL INTELLIGENCE-BASED APPROACH TO PERFORM MONITORING AND DIAGNOSTIC PROCESS FOR A HOLISTIC ENVIRONMENT." International Journal of Computer Science and Engineering Research and Development (IJCSERD) 14.2 (2024): 71-88.
Tamanampudi, Venkata Mohit. "AI-Driven Incident Management in DevOps: Leveraging Deep Learning Models and Autonomous Agents for Real-Time Anomaly Detection and Mitigation." Hong Kong Journal of AI and Medicine 4.1 (2024): 339-381.
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
Kurkute, Mahadu Vinayak, Anil Kumar Ratnala, and Thirunavukkarasu Pichaimani. "AI-Powered IT Service Management for Predictive Maintenance in Manufacturing: Leveraging Machine Learning to Optimize Service Request Management and Minimize Downtime." Journal of Artificial Intelligence Research 3.2 (2023): 212-252.
Pichaimani, T., Inampudi, R. K., & Ratnala, A. K. (2021). Generative AI for Optimizing Enterprise Search: Leveraging Deep Learning Models to Automate Knowledge Discovery and Employee Onboarding Processes. Journal of Artificial Intelligence Research, 1(2), 109-148.
Surampudi, Yeswanth, Dharmeesh Kondaveeti, and Thirunavukkarasu Pichaimani. "A Comparative Study of Time Complexity in Big Data Engineering: Evaluating Efficiency of Sorting and Searching Algorithms in Large-Scale Data Systems." Journal of Science & Technology 4.4 (2023): 127-165.
Kondaveeti, Dharmeesh, Rama Krishna Inampudi, and Mahadu Vinayak Kurkute. "Time Complexity Analysis of Graph Algorithms in Big Data: Evaluating the Performance of PageRank and Shortest Path Algorithms for Large-Scale Networks." Journal of Science & Technology 5.4 (2024): 159-204.
Tamanampudi, Venkata Mohit. "Generative AI Agents for Automated Infrastructure Management in DevOps: Reducing Downtime and Enhancing Resource Efficiency in Cloud-Based Applications." Journal of AI-Assisted Scientific Discovery 4.1 (2024): 488-532.
Inampudi, Rama Krishna, Thirunavukkarasu Pichaimani, and Yeswanth Surampudi. "AI-Enhanced Fraud Detection in Real-Time Payment Systems: Leveraging Machine Learning and Anomaly Detection to Secure Digital Transactions." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 483-523.
Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." In Nutrition and Obsessive-Compulsive Disorder, pp. 26-35. CRC Press.
S. Kumari, “Cybersecurity Risk Mitigation in Agile Digital Transformation: Leveraging AI for Real-Time Vulnerability Scanning and Incident Response ”, Adv. in Deep Learning Techniques, vol. 3, no. 2, pp. 50–74, Dec. 2023
Parida, Priya Ranjan, Rama Krishna Inampudi, and Anil Kumar Ratnala. "AI-Driven ITSM for Enhancing Content Delivery in the Entertainment Industry: A Machine Learning Approach to Predict and Automate Service Requests." Journal of Artificial Intelligence Research and Applications 3.1 (2023): 759-799.
M. K. K. Kavis, Architecting the Cloud: Design Decisions for Cloud Computing Service Models (SaaS, PaaS, and IaaS), Wiley, 2014.
The Open Group, "TOGAF® 9.2," The Open Group Standard, 2018. [Online]. Available: https://pubs.opengroup.org/architecture/togaf9-doc/arch/index.html
J. Ross, P. Weill, and D. Robertson, Enterprise Architecture as Strategy: Creating a Foundation for Business Execution, Harvard Business Review Press, 2006.
M. Lankhorst, Enterprise Architecture at Work: Modelling, Communication, and Analysis, Springer, 2017.
J. T. O’Callaghan and R. D. G. McDermott, "Cloud Computing and Enterprise Architecture: A Strategic Alignment Perspective," International Journal of Cloud Computing and Services Science (IJ-CLOSER), vol. 3, no. 4, pp. 176–185, 2014.
F. X. Hartman, "Adoption of Cloud Computing in Large Enterprises," Journal of Enterprise Architecture, vol. 11, no. 1, pp. 23–33, 2015.
M. W. Allen and C. K. Lee, "The Impact of Cloud Computing on Enterprise Architecture," Journal of Cloud Computing: Advances, Systems and Applications, vol. 1, no. 1, pp. 45–59, 2014.
L. Laplante and G. S. Weaver, "Federal Enterprise Architecture Framework: A Critical Review," International Journal of Information Technology & Decision Making, vol. 15, no. 3, pp. 525–540, 2016.
J. A. Zachman, "The Zachman Framework for Enterprise Architecture," John Wiley & Sons, 2008.
A. D. A. Lankhorst et al., "Enterprise Architecture for Digital Transformation: Challenges and Opportunities," Business & Information Systems Engineering, vol. 60, no. 4, pp. 271–277, 2018.
J. L. Evans, "A Comparative Analysis of the Zachman Framework and TOGAF," International Journal of Information Systems and Engineering, vol. 2, no. 3, pp. 89–103, 2014.
M. Spewak and S. Hill, "Enterprise Architecture Planning: Developing a Blueprint for Data, Applications, and Technology," John Wiley & Sons, 1993.
B. M. Smith, "Designing Enterprise Architectures for Cloud: A Roadmap," International Journal of Cloud Computing and Services Science, vol. 5, no. 4, pp. 233–245, 2015.
C. M. C. Chin and S. J. Goh, "Applying the Zachman Framework to Enhance Cloud Computing Adoption," Cloud Computing Technologies and Applications Journal, vol. 12, pp. 102–112, 2016.
P. N. G. R. Carvalho et al., "Cloud Computing and Enterprise Architecture: Challenges and Prospects for Adoption in Government Sectors," International Journal of Cloud Computing and Services Science, vol. 8, no. 2, pp. 45–61, 2018.
B. A. G. Reijers et al., "Governance in Cloud Computing: A Study of Enterprise Architecture Frameworks," Journal of Cloud Computing: Theory and Applications, vol. 2, pp. 95–106, 2017.
J. L. A. van der Meer, "The Role of Enterprise Architecture Frameworks in Cloud Integration," Journal of Enterprise Systems Engineering, vol. 6, no. 3, pp. 109–118, 2019.
P. J. Weill, "The Role of Enterprise Architecture in Cloud Computing Transformation," Business Process Management Journal, vol. 21, no. 1, pp. 54–70, 2015.
M. Tanenbaum and R. S. Van der Meer, "A Comparative Study of the Application of EA Frameworks for Cloud Adoption in Large Enterprises," International Journal of Information Systems, vol. 10, no. 4, pp. 113–130, 2017.
