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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ahlemann, F., Stettiner, E., Messerschmidt, M., Legner, C.: Strategic Enterprise Architecture Management: Challenges, Best Practices, and Future Developments. In: Management for Professionals. Springer (2012)
Beyer, M., Lovelock, J.D., Sommer, D., Adrian, M.: Big data drives rapid changes in infrastructure and 232 billion in it spending through 2016. Gartner Research (2012)
Evans, E.: Domain-Driven Design: Tackling Complexity in the Heart of Software. Addison-Wesley (2004)
Fowler, M.: Patterns of Enterprise Application Architecture. Addison-Wesley Longman Publishing Co., Inc., Boston (2002)
Franke, U., Flores, W.R., Johnson, P.: Enterprise architecture dependency analysis using fault trees and bayesian networks. In: Proceedings of the 2009 Spring Simulation Multiconference, SpringSim 2009, pp. 55:1–55:8. Society for Computer Simulation International, San Diego (2009)
Haren, V.: Togaf Version 9.1. Van Haren Publishing Series. Bernan Assoc. (2011)
Heer, J., Card, S.K., Landay, J.A.: Prefuse: a toolkit for interactive information visualization. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2005, pp. 421–430. ACM, New York (2005)
Johnson, P., Lagerström, R., Närman, P., Simonsson, M.: Enterprise architecture analysis with extended influence diagrams. Information Systems Frontiers 9(2-3), 163–180 (2007)
Lagerstrom, R., Johnson, P.: Using architectural models to predict the maintainability of enterprise systems. In: Proceedings of the 2008 12th European Conference on Software Maintenance and Reengineering, CSMR 2008, pp. 248–252. IEEE Computer Society, Washington, DC (2008)
Sessions, R.: Simple Architectures for Complex Enterprises. Best Practices. Microsoft Press (2008)
Walsh, B.: Markov Chain Monte Carlo and Gibbs Sampling. Lecture Notes for EEB 581 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
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
eBook Packages: EngineeringEngineering (R0)