The Linear Conditional Probability Matrix Generator for IT Governance Performance Prediction

The goal of IT governance is not only to achieve internal efficiency in an IT organization, but also to support IT's role as a business enabler. The latter is here denoted IT governance performance, and cannot be controlled by IT management directly. Their realm of control includes IT governance maturity, indicated by e.g. different IT activities, documents, metrics and roles. Current IT governance frameworks are suitable for describing IT governance, but lack the ability to predict how changes to the IT governance maturity indicators affect the IT governance performance. This paper presents a Bayesian network for IT governance performance prediction, learned with experience from 35 case studies. The network learns using the proposed Linear Conditional Probability Matrix Generator. The resulting Bayesian network for IT governance performance prediction can be used to support IT governance decision-making.

• Type: Conference
• Year: 2008
• Where: VVEIS
• Authors: Mårten Simonsson, Robert Lagerström, Pontus Johnson
• Keywords: Governance Maturity, Governance Performance, Information Technology, IT Governance, VVEIS 2008