A QUANTITATIVE ASSESSMENT MODEL FOR ICT SYSTEM SOLUTION SELECTION WITH BAYESIAN NETWORK FOR ENTERPRISE ARCHITECTURE DECISIONS

Abstract

Purpose- This research proposes a quantitative assessment model for ICT System Solution Selection.

Methodology- For large Enterprise Organizations, ICT Projects may have two alternative solutions such as centralized or distributed ICT Systems. For evaluating these options with Enteprise Architecture principles and frameworks and also bringing expert knowledge in decision support, “Quantitative Assessment Model Survey for IT Solution Selection” study is conducted among ICT professionals of various industrial sectors. To build the model for centralized or distributed ICT system solutions, the influential relations between the surveyed EA components and principles are identified with Structural Learning of Bayesian Network to make the relations of the important EA elements visible and quantifiable.

Findings- Using data from our survey, adaptable quantitative model which enables a Bayesian network across organized EA components is developed.

Conclusion- The study shows relationships between EA components with experts’ feedbacks which may help practitioners and decision makers to reach a common model for ICT solution evaluation.

Keywords

• Structural learning,Bayesian network,enterprise engineering,enterprise architecture,decision making

References

1. Dietz, J. L. G., Proper, E., Tribolet, J. (2009). Enterprise governance and enterprise engineering.
2. Dietz, J. L., Hoogervorst, J. A. (2011). A critical investigation of TOGAF-based on the enterprise engineering theory and practice. In Enterprise Engineering Working Conference (pp. 76-90). Springer, Berlin, Heidelberg.
3. Domingos, P., Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero– one loss. Machine Learning, 29, 103 30.
4. Eden, C. (2004). Analyzing cognitive maps to help structure issues or problems. European Journal of OperationalResearch 159(3) 673–86.
5. Fenton, N., Neil, M., Marquez, D. (2008). Using Bayesian networks to predict software defects and reliability. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 222(4), 701-712.
6. Greefhorst, D., Proper, E. (2011). The role of enterprise architecture. In Architecture Principle.
7. Harary, F. (1972). Graph theory. Addison-Wesley, Reading.
8. Harary, F., Norman, R., Cartwright, D. (1965). Structural models: an introduction to the theory of directed graphs. Wiley, New York.

9. Kosanke, K., Vernadat, F., Zelm, M. (1999). CIMOSA: Enterprise engineering and integration. Computers in Industry 40(2): 83–87.
10. Nadkarni, S., Shenoy, P. P. (2001). A Bayesian network approach to making inferences in causal maps. European Journal of Operational Research 128 (2001) 479–498.
11. Robertson, S. E., Sparck Jones, K. (1976). Relevance weighting of search terms. J. American Society for Information Science, 27, 129–146.
12. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234-281.
13. Saaty, L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
14. Saaty, T. L. (1994). Fundamentals of decision making.
15. Sahin, S. O., Ulengin, F., Ulengin, B. (2005). A Bayesian causal map for inflation analysis: the case of Turkey. European Journal of Operational Research: 1268–84.
16. Sessions, R. (2007). A comparison of the top four enterprise-architecture methodologies. Houston: ObjectWatch Inc.
17. Yildiran, P., Kilic, H. S., Sennaroglu, B. (2018). Collaborative system approach for enterprise engineering and enterprise architecture: a literature review. In Enhancing Competitive Advantage with Dynamic Management and Engineering (pp. 138-216). IGI Global.