Towards an information technology infrastructure cost model

Abstract

Ever since the introduction of the Internet in 1994, one of the defining characteristics of the global economy, particularly in the US, is a dramatic increase in expenditures on Information Technology. While this trend is expected to continue, a major issue for companies of all sizes is the manner in which precise forecasting of future IT cost may be undertaken. The present thesis investigates the possibility that a set of the essential deterministic cost drivers with varying weighted factors may prove capable of estimating total IT infrastructure costs. An online questionnaire was developed for this purpose, and was used to survey senior IT leadership teams. The data collected from this survey was then computed with Analytical Hierarchy Process (AHP) to illustrate the relative importance of different cost drivers. The study revealed three primary findings. First, that a set of essential deterministic cost drivers with varying weighted factors could be used as a general tool for estimating the total cost of IT infrastructure. Second, these different sectors prioritize cost drivers differently from each other. In the Financial Services sector, for instance, the security of the IT network was reported to be of greater importance than the service call response time. In the Technology sector, however, the opposite was true. Third, numerous correlations were found to exist within each cost driver category defined. The correlated nature of these cost parameters may mean that a more parsimonious model may be more predictive of total IT infrastructure costs. It is hoped that these findings may be of benefit to a variety of large and small commercial and government entities, which may be able to use the predictive cost drivers to help eliminate problems related to inaccurate IT cost estimates.

(cont.) It is believed that the cost model proposed may be applicable across a variety of economic sectors. In this thesis, its applicability is demonstrated within the 3 financial services and technology sectors. Future research may be useful in evaluating the model further, by increasing the sample size, and by testing the reliability and validity of the cost model within additional economic sectors.