Measuring Enterprise Architecture Complexity

Maria Eugenia Iacob; Jeroen Monteban; Marten Van Sinderen; Erik Hegeman; Kiean Bitaraf

doi:10.1109/EDOCW.2018.00026

Measuring Enterprise Architecture Complexity

Maria Eugenia Iacob, Jeroen Monteban, Marten Van Sinderen, Erik Hegeman, Kiean Bitaraf

Industrial Engineering & Business Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

108 Downloads (Pure)

Abstract

Complexity management has become an essential undertaking for enterprise architecture (EA). It strives for an optimal level of complexity to efficiently and effectively use the EA for its intended purposes. The basis for 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 EA all have a different perception of complexity, leading to the notion of subjective complexity. This research aims to incorporate objective and subjective complexity metrics in a single EA complexity measurement model. A systematic literature review has been carried out to make an inventory of existing complexity metrics. Semi-structured interviews were used to gain insights in stakeholder perceptions and subjective complexity attributes. Based on these results, a conceptual model of EA complexity was designed. The constructs in this model have been operationalized with metrics to create a measurement instrument of EA complexity. The model and its operationalization was then validated through expert interviews, and tested during a case study, where it has been applied in practice.

Original language	English
Title of host publication	2018 IEEE 22nd International Enterprise Distributed Object Computing Conference Workshops, EDOCW 2018
Publisher	IEEE
Pages	115-124
Number of pages	10
ISBN (Electronic)	9781538641415
DOIs	https://doi.org/10.1109/EDOCW.2018.00026
Publication status	Published - 15 Nov 2018
Event	22nd IEEE International Enterprise Distributed Object Computing Conference, EDOC 2018 - KTH Royal Institute of Technology, Stockholm, Sweden Duration: 16 Oct 2018 → 19 Oct 2018 Conference number: 22

Conference

Conference	22nd IEEE International Enterprise Distributed Object Computing Conference, EDOC 2018
Abbreviated title	EDOC 2018
Country/Territory	Sweden
City	Stockholm
Period	16/10/18 → 19/10/18

Keywords

enterprise architecture
measurement instrument
metric
objective complexity
subjective complexity

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10.1109/EDOCW.2018.00026

Cite this

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title = "Measuring Enterprise Architecture Complexity",

abstract = "Complexity management has become an essential undertaking for enterprise architecture (EA). It strives for an optimal level of complexity to efficiently and effectively use the EA for its intended purposes. The basis for 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 EA all have a different perception of complexity, leading to the notion of subjective complexity. This research aims to incorporate objective and subjective complexity metrics in a single EA complexity measurement model. A systematic literature review has been carried out to make an inventory of existing complexity metrics. Semi-structured interviews were used to gain insights in stakeholder perceptions and subjective complexity attributes. Based on these results, a conceptual model of EA complexity was designed. The constructs in this model have been operationalized with metrics to create a measurement instrument of EA complexity. The model and its operationalization was then validated through expert interviews, and tested during a case study, where it has been applied in practice.",

keywords = "enterprise architecture, measurement instrument, metric, objective complexity, subjective complexity",

author = "Iacob, {Maria Eugenia} and Jeroen Monteban and {Van Sinderen}, Marten and Erik Hegeman and Kiean Bitaraf",

year = "2018",

month = nov,

day = "15",

doi = "10.1109/EDOCW.2018.00026",

language = "English",

pages = "115--124",

booktitle = "2018 IEEE 22nd International Enterprise Distributed Object Computing Conference Workshops, EDOCW 2018",

publisher = "IEEE",

address = "United States",

note = "22nd IEEE International Enterprise Distributed Object Computing Conference, EDOC 2018, EDOC 2018 ; Conference date: 16-10-2018 Through 19-10-2018",

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Iacob, ME, Monteban, J, Van Sinderen, M, Hegeman, E & Bitaraf, K 2018, Measuring Enterprise Architecture Complexity. in 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference Workshops, EDOCW 2018., 8536112, IEEE, pp. 115-124, 22nd IEEE International Enterprise Distributed Object Computing Conference, EDOC 2018, Stockholm, Sweden, 16/10/18. https://doi.org/10.1109/EDOCW.2018.00026

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AU - Iacob, Maria Eugenia

AU - Monteban, Jeroen

AU - Van Sinderen, Marten

AU - Hegeman, Erik

AU - Bitaraf, Kiean

N1 - Conference code: 22

PY - 2018/11/15

Y1 - 2018/11/15

N2 - Complexity management has become an essential undertaking for enterprise architecture (EA). It strives for an optimal level of complexity to efficiently and effectively use the EA for its intended purposes. The basis for 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 EA all have a different perception of complexity, leading to the notion of subjective complexity. This research aims to incorporate objective and subjective complexity metrics in a single EA complexity measurement model. A systematic literature review has been carried out to make an inventory of existing complexity metrics. Semi-structured interviews were used to gain insights in stakeholder perceptions and subjective complexity attributes. Based on these results, a conceptual model of EA complexity was designed. The constructs in this model have been operationalized with metrics to create a measurement instrument of EA complexity. The model and its operationalization was then validated through expert interviews, and tested during a case study, where it has been applied in practice.

AB - Complexity management has become an essential undertaking for enterprise architecture (EA). It strives for an optimal level of complexity to efficiently and effectively use the EA for its intended purposes. The basis for 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 EA all have a different perception of complexity, leading to the notion of subjective complexity. This research aims to incorporate objective and subjective complexity metrics in a single EA complexity measurement model. A systematic literature review has been carried out to make an inventory of existing complexity metrics. Semi-structured interviews were used to gain insights in stakeholder perceptions and subjective complexity attributes. Based on these results, a conceptual model of EA complexity was designed. The constructs in this model have been operationalized with metrics to create a measurement instrument of EA complexity. The model and its operationalization was then validated through expert interviews, and tested during a case study, where it has been applied in practice.

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KW - measurement instrument

KW - metric

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KW - subjective complexity

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ER -

Measuring Enterprise Architecture Complexity

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Keywords

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