Abstract
With the growing importance of architectural considerations over the last decade, larger Enterprise Architecture DataBases (EADBs) have enjoyed accumulation of enterprise data over years. Enterprise data documenting IT-applications, interfaces, data ownerships, business function implementation and usage etc. have been modelled based on various underlying metamodels and using different EAtools. However, harnessing this data for business benefit is often hard and widely performed manually using expert knowledge. We here propose a statistical method for harnessing the EADB knowledge to infer indirect dependencies between ITapplications and projects. Our approach provides key insights into otherwise unseen multimodal dependencies in the IT landscape and provides a quantifiable methodology for optimizing the IT development plan and portfolio management.We validate our method in the highly integrated IT landscape of the Swiss Federal Railways, one of the world-leading rail transportation providers, and show how business decision making can directly be supported using our approach.
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Keywords
- Portfolio Optimization
- Dependency Graph
- Enterprise Architecture
- Markov Chain Monte Carlo Algorithm
- Business Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2014 Springer International Publishing Switzerland
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Mathews, Z., Kaeslin, L., Rytz, B. (2014). Harnessing Multimodal Architectural Dependencies for IT-Business Alignment and Portfolio Optimization: A Statistical Approach. In: Benghozi, P., Krob, D., Lonjon, A., Panetto, H. (eds) Digital Enterprise Design & Management. Advances in Intelligent Systems and Computing, vol 261. Springer, Cham. https://doi.org/10.1007/978-3-319-04313-5_4
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DOI: https://doi.org/10.1007/978-3-319-04313-5_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04312-8
Online ISBN: 978-3-319-04313-5
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