Titolo della tesi: IT GOVERNANCE PERFORMANCE MONITORING AND IMPROVEMENT: DESIGN OF A DATA-DRIVEN DECISION SUPPORT SYSTEM
Nowadays, managers working in an everchanging environment are required to make fast, reliable, and decisions based on data [1], [2]. These challenges hold, especially for technology companies. These organizations are passing through a digital evolution, thus the importance of company data analysis, of translating data into crucial information is becoming a key issue. Benchmark approaches have often been used to carry out performance analysis based on cost indicators. Nevertheless, this approach does not take into consideration the dynamic evolution in such a context. Furthermore, a lack of the main frameworks proposed in academia has been observed, particularly in the perception of the diverse nature of organizations. Analogously, the frameworks proposed by organizations miss the connection with organizational structures and strategic issues [3]. Thus, both academies and companies are still struggling to find specific and well-defined frameworks and standards able to cover all the principles individually [4]. Therefore, this research aimed to design and develop a management model to support the management in the decision-making phase with predictive analysis capability. Moreover, within the context of company digitalization, the focus was the investigation of methods and systems in support of the decision-making phase for process optimization and performance improvement. Consequently, the first step we performed was an in-depth analysis of IT governance, of the performance management along with the indicator’s theory, the project governance and the principal methods and models, already adopted in performance monitoring to support the decision-making phase. The main contribution to the literature was the design and development of an innovative support system to improve systematic organizational intelligence. The second step was the investigation of how to contribute to the company's performance improvement and thus bridging the gap between the theory of best practices and theoretical frameworks and the practical applications to prevent possible failures. Therefore, the project selection theory was in-depth analysed along with the principal Multicriteria Decision-making methods and project selection theory. The main contribution was the development of a decision-making support system for project selection. Finally, the companies need to have a complete view of the status along with predicting future company scenarios, bringing us to study and develop a structured data model as a basis for fast and reliable statistical analysis and further Machine Learning applications. The theory on the design of the data model to implement data-driven governance was investigated in depth. In addition, the application of Machine learning in time series was initiated, as the future development of this work, for automatic expenditure forecasting. This provided an overview of the company's direction for the coming years. Moreover, future development call for a cooperative DSS. The latter accomplishes the predictive governance needs, where the system can make decisions on the behalf of the decision-makers.