IT-Governance has a major impact not only on IT management but also and foremost in the Enterprises performance and control. Business uses IT agility, flexibility and innovation to pursue its objectives and to sustain its strategy. However being it more critical to the business, compliance forces IT on the opposite way of predictability, stability and regulations. Adding the current economical environment and the fact that most of the times IT departments are considered cost centres, IT-Governance decisions become more important and critical. Current IT-Governance research and practise is mainly based on management techniques and principles, leaving a gap for the contribution of information systems to IT-Governance enhancement. This research intends to provide an answer to IT-Governance requirements using Data Driven Decision Support Systems based on dimensional models. This seems a key factor to improve the IT-Governance decision making process. To address this research opportunity we have considered IT-Governance research (Peter Weill), best practises (ITIL), Body of Knowledge (PMBOK) and frameworks (COBIT). Key IT-Governance processes (Change Management, Incident Management, Project Development and Service Desk Management) were studied and key process stakeholders were interviewed. Based on the facts gathered, dimensional models (data marts) were modelled and developed to answer to key improvement requirements on each IT-Governance process. A Unified Dimensional Model (IT-Governance Data warehouse) was materialized. To assess the Unified Dimensional Model, the model was applied in a bank in real working conditions. The resulting model implementation was them assessed against Peter Weill‘s Governance IT Principles.Assessment results revealed that the model satisfies all the IT-Governance Principles. The research project enables to conclude that the success of IT-Governance implementation may be fostered by Data Driven Decision Support Systems implemented using Unified Dimensional Model concepts and based on best practises, frameworks and body of knowledge that enable process oriented, data driven decision support.