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Measuring Architectural Complexity : Quantifying the objective and subjective complexity in enterprise architecture

Monteban, J. (2018) Measuring Architectural Complexity : Quantifying the objective and subjective complexity in enterprise architecture.

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Abstract:The fast pace of economic growth and technological advances have increased the dependence of business 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 difference 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 architecture 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 operationalized 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 consolidates 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.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Business Information Technology MSc (60025)
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