Abstract
Complexity of both business and IT is one of the most frequently discussed topics in strategic management and enterprise architecture today. For many business leaders, complexity is of central concern due to its assumed impacts on operating costs, organizational agility, and operational risks. In fact, complexity growth may be considered one of the major drivers for misalignment. As a consequence, organizations are increasingly forced to manage the complexity of their business and IT actively. However, existing qualitative methods fall short of supporting this on a larger scale. Quantitative measures may be considered a promising means to assess and manage the complexity of business and IT architectures in a systematic and universal way. This chapter presents a generic framework for conceptualizing and measuring enterprise architecture complexity and applies it to the domain of business architecture. Using this book’s business architecture framework as a reference, it is shown how business complexity can be operationalized and quantified using well-defined and practice-proven measures.
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Notes
- 1.
In line with the classic dichotomy coined by Drucker (1974), an architecture that fulfills all environmental requirements may be called effective (it “does the right thing”).
- 2.
Referring to the classic dichotomy again, an architecture with minimal complexity may be called efficient (it “does the things right”, i.e., with minimal effort) (Drucker 1974).
- 3.
It should be noted though that the strategic impact of the complexity surplus will be contingent on the role of depending variables like agility and efficiency within the organization. For example, a quality leader operating in a stable market environment may have less incentives to control the complexity surplus than a cost leader in a rapidly developing marketplace.
- 4.
From an architectural perspective, alignment may be defined as the degree of consistency between the components of an architecture given by their properties and collocation. An architecture is well aligned if it is both effective (fulfills all requirements imposed by the environment) and efficient (does not contain any waste components or relationships).
- 5.
In contrast to prevalent methods in the EAM field, this could be a key constituent of what may be called “Quantitative EAM.”
- 6.
- 7.
It should be noted that the actual complexity figures are strongly dependent on the used capability model and the associated level of detail. As a consequence, comparisons over time or between peers need to be based on the same reference model (or at least modeling guidelines) to be of any meaning.
- 8.
While there is good reason for the emergence of such architectures (e.g., historical evolution based on mergers and acquisitions), it is clearly in conflict with the goal of architectural efficiency as defined in Sect. 13.1.
- 9.
Capability configurations may be defined as existing combinations of business processes, organization units, information objects, resources, people, and culture in an organization realizing a certain business capability.
- 10.
- 11.
In addition to the core elements of the value creation network, complementing elements like revenue streams and pricing models, cost structures, value chain coordination mechanisms, or assets (see Chap. 1) could be analyzed in a similar way.
- 12.
This corresponds with the EA school of “Enterprise Ecological Adaption” as introduced by Lapalme (2012).
- 13.
Like with other element types, measures will be strongly dependent on the actual modeling of goals and the chosen level of detail. As demonstrated in Chap. 2, goals should be defined in an atomic way with each goal addressing only one aspect.
- 14.
However, such a differentiation may be a strategic necessity given varying market conditions.
- 15.
This may be strongly facilitated by using a detailed catalog of typical application scenarios as a reference.
- 16.
An example for such a specialized tool kit is the Plexity Analyzer™, which supports a flexible configuration and calculation of all relevant complexity measures based on arbitrary data/metamodel structures.
- 17.
In addition to the framework presented in this chapter, centrality measures from the domain of network analysis may be used to assess local complexity and counteract global “over-optimization” (cf. Simon and Fischbach 2013).
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Schmidt, C. (2015). Business Architecture Quantified: How to Measure Business Complexity. In: Simon, D., Schmidt, C. (eds) Business Architecture Management. Management for Professionals. Springer, Cham. https://doi.org/10.1007/978-3-319-14571-6_13
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