Developing An Analytic Model of Success for Acquisition Decision Making

Abstract

Developing an Information Technology (IT) system to meet organizational needs is becoming more complicated. It is often very extensive, taking a long time to realize, and is almost always more costly and more difficult than originally planned. This is especially true for large IT projects. A significant amount of data and large numbers of artifacts these large IT programs produce make it extremely challenging to digest in order to support their decision making. The most challenging issue is that there is often an abundance of data, but limited analytical tools to properly combine the evidence to support the business decisions. To help with this complexity, many businesses use the Information Technology Infrastructure Library (ITIL) to guide the design, procurement, and operation of their IT systems. The ITIL is intended to optimally synchronize IT departments to function in accordance with the needs of business. However, even though the ITIL process helps standardize the IT service management, it is itself a complicated and involved system that may seem confusing and difficult to navigate. To address these challenging issues, this research aims to provide IT program managers a decision support tool in order to help them make better business management decisions. The technical approach of the research began with literature review and gathering results from past research, including the Defense Business Systems Acquisition Probability of Success (DAPS) Model, a technical framework developed at GMU. DAPS was employed and enhanced into the Information Technology Decision Management System (ITDMS), an analytic decision support system with an automatic quantitative reasoning engine. The key difference between DAPS and ITDMS is the explicit incorporation of the utility and decision factors in the Bayesian influence diagram model as well as the incorporation of the ITIL process. The goal is to help systems engineers and program managers holistically process the available data/evidence in order to make better management decisions in a dynamic and complex environment. The ITDMS models the complex interrelationships as well as dynamic/temporal relationships in the ITIL process. It allows a decision maker to assess program performance in specific subject matter knowledge areas and the overall likelihood of program success by taking into account both data and temporal uncertainty. This research aims to develop a useful decision support tool to help the IT professional, specifically in a data-rich dynamic environment. The contributions of this research effort include developing a quantitative reasoning system to aid IT professionals holistically process the available evidence to make optimal business decision as well as an analytical tool to compute the resulting predicted future project probability of success with a Bayesian dynamic model.