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MEASURING ARCHITECTURAL COMPLEXITY Quantifying Objective and Subjective Complexity in Enterprise Architecture

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MEASURING ARCHITECTURAL COMPLEXITY Quantifying Objective and Subjective Complexity in Enterprise Architecture MASTER’S THESIS MEASURING ARCHITECTURAL COMPLEXITY Quantifying objective and subjective complexity in enterprise architecture Jeroen Monteban Business Information Technology Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) Enschede May 24, 2018 Measuring Architectural Complexity Quantifying objective and subjective complexity in enterprise architecture AUTHOR Jeroen Monteban Programme Business Information Technology Track IT Management & Innovation Faculty Electrical Engineering, Mathematics and Computer Science Student number 1220837 E-mail [email protected] GRADUATION COMMITTEE Maria Iacob Department Industrial Engineering and Business Information Systems E-mail [email protected] Marten van Sinderen Department Computer Science E-mail [email protected] Erik Hegeman Department Enterprise Architecture E-mail [email protected] Kiean Bitaraf Department Enterprise Architecture E-mail [email protected] Page i Page ii Preface The writing of this Master’s thesis marks the end of my days as a student at the University of Twente. These days started almost seven years ago, when a younger version of myself moved to Enschede to study Business Information Technology. After a relatively calm first year, I started a series of extracur- ricular adventures, during which I met countless of amazing people, and learned much about the world and myself. These years have been a pleasant mix of studying, board years, committees, foreign trips and of course the occasional drink. They have brought me many great experiences, and taught me a great deal, both personal and professional. The final adventure I embarked upon as a student was this Master’s thesis. In October of last year, I started as a graduate intern at the Enterprise Architecture department of Deloitte Consulting. The road towards a completed thesis is a tricky one, with many hurdles to take and crossings to navigate. Un- avoidably, I took some side paths that lead nowhere, and miscalculated the distance to some milestones. However, I never lost sight of the destination, and am glad to have finally arrived at a completed thesis. Of course, such a long road cannot be journeyed alone. First of all, I would like to thank my University supervisors, Maria and Marten, for their guidance and support. Their experience provided valuable insights in this project, and helped to improve its quality. Next, I would like to thank Erik en Kiean for their supervision, inspiring ideas and critical view; their detailed feedback and practical knowledge greatly contributed to this research. I have really enjoyed our collaboration over the past months. Additionally, I would like to thank my colleagues at the Enterprise Architecture department. Their insights, ideas, feedback and critical questions always inspired me to keep improving this research. And of course, I really enjoyed the coffee breaks, foosball matches and Friday afternoon drinks! A special thanks goes out to the companies that helped me by providing interviews or a case study. Their involvement supplied the data for this research, and provided interesting insights in their organiza- tions. Last but not least, I would like to thank my family and friends for their support during this thesis, and during my days as a student. This journey now comes to an end, and I hope you enjoy reading its results! Jeroen Monteban Amsterdam, May 23rd, 2018 Page iii Page iv Executive Summary The fast pace of economic growth and technological advances have increased the dependence of busi- ness on IT and vice versa. Due to these developments, enterprise architecture (EA) - a discipline aiming to integrate business and IT - has witnessed a surge of complexity, often leading to an inefficient use of the architecture and a lack of control. Yet, an architecture that is too simple for its complex environment cannot support the functionalities required to operate in that environment. Complexity management has become an essential undertaking for enterprise architecture. It strives for an optimal level of complexity to efficiently and effectively deal with the complexity of the architecture’s environment. The basis for effective complexity management is measurement, yet no standardized or proven method for EA complexity measurement currently exists, nor is there consensus about the attributes contributing to complexity. Additionally, the many stakeholders involved in an enterprise architecture all have a different perception of complexity, which impedes their collaboration. Understanding this differ- ence in complexity perception is essential to enable effective complexity management. To accomplish this, this research aims to incorporate objective and subjective complexity metrics in an EA complexity measurement model. A literature review was conducted to gain insight into the state of the art of EA complexity research, and create an overview of the existing complexity metrics. Next, a series of twelve interviews were held at four organizations, during which participants were queried on the complexity of the enterprise archi- tecture in their organization, and the attributes influencing their perception of this complexity. Using the data obtained through literature and interviews, a conceptual model of EA complexity was designed. This conceptual model contains constructs that influence EA complexity and stakeholders’ perception thereof, and describes the relations between these constructs. Next, constructs have been operational- ized through the design of metrics. Using these metrics, the constructs influencing complexity can be measured, thus creating a measurement model for EA complexity. Finally, the measurement model was validated through three expert interviews, and a case study applying the model in practice. The identification of the constructs influencing objective and subjective complexity showed many aspects affecting EA complexity that are currently not considered in literature and practice. Whereas constructs such as size and heterogeneity are well-represented in literature, many other factors are ignored. These include enterprise- and environment-related constructs such as politics, technical debt or industry. Additionally, several constructs were found to specifically influence the perception of complexity, such as documentation, communication between stakeholders, the presence of an architectural vision, and several stakeholder qualities. A full list of these constructs and the metrics required to measure them can be found in this research. The results from this research contribute to both the theory and practice of EA complexity. It consol- idates the existing research concerning objective complexity measurement, and provides a first insight into the previously unexplored area of subjective complexity. In practice, this research be used to enable effective complexity management and overcome stakeholder differences. Page v Page vi Contents List of Figures ix List of Tables xi 1 Introduction 1 1.1 Introduction............................................1 1.2 Background............................................2 1.3 Research objectives.......................................8 1.4 Research design.........................................9 2 Literature Review 13 2.1 Literature review methodology................................. 13 2.2 Complex systems......................................... 15 2.3 Objective complexity metrics.................................. 16 2.4 Stakeholders........................................... 20 2.5 Subjective complexity...................................... 24 2.6 Requisite complexity....................................... 26 3 Methodology 28 3.1 Conceptual model........................................ 28 3.2 Interviews............................................. 30 3.3 Model design........................................... 33 3.4 Validation............................................. 37 4 Interview Results 41 4.1 Analysis.............................................. 41 4.2 Results.............................................. 42 5 Model Design 46 5.1 Construct identification...................................... 46 5.2 Construct relations........................................ 49 5.3 Conceptual model........................................ 52 5.4 Operationalization........................................ 55 5.5 Measurement model....................................... 65 6 Validation 66 6.1 Expert validation......................................... 66 6.2 Case study............................................ 69 Page vii 7 Discussion 75 7.1 Constructs............................................ 75 7.2 Conceptual model........................................ 76 7.3 Measurement model....................................... 78 7.4 Construct weights........................................ 79 7.5 Recommendations........................................ 80 7.6 Limitations............................................ 82 7.7 Future research.......................................... 82 8 Conclusion 84 8.1 Conclusions............................................ 84 8.2 Contributions........................................... 91 9 Bibliography 93 Appendix A Structured Literature Review Protocol 100 A.1 Review overview......................................... 100 A.2 Research question........................................ 101 A.3 Search strategy.......................................... 101 A.4 Data extraction.......................................... 102 Appendix B Structured Literature Review
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