Large investments are made annually to develop and maintain IT systems. Successful outcome of IT projects is therefore crucial for the economy. Yet, many IT projects fail completely or are delayed or over budget, or they end up with less functionality than planned. This article describes a Bayesian decision-support model. The model is based on expert elicited data from 51 experts. Using this model, the effect management decisions have upon projects can be estimated beforehand, thus providing decision support for the improvement of IT project performance.

No abstract

Each year companies spend millions of dollars on IT investments hoping they will lead to higher profits. There are many methods for analyzing what these investments actually bring back to the companies, but unfortunately they are not stringent enough to make the analysis repeatable. This means that different investments cannot be compared to each other.The management paradigm of Enterprise Architecture (EA) is commonly used to structure a company from a holistic perspective. In this paper, an EA framework for assessing ITsystems´ impact on an organization´s business value through changes in its structure is validated. The foundation of the framework is a Bayesian inference engine allowing quantified analysis. For practical usage, this analysis framework is also expressed through modeling the organization with a metamodel. Together they form a structured method for quantitative analysis of the IT impact on organizations.An IT system for maintenance management within a European electric power utility has been used as a case study to validate the method. The organization and IT support have been modeled using the proposed metamodel and thereafter analyzed with the Bayesian network. The study has been conducted using guided interviews and a survey. The results from this study of how the business value has been influenced are compared to the user's perceptions on how the business values have changed are also presented in this paper.

No abstract

No abstract