KPI-Oriented Process Control for Public Infrastructure: a Data Governance Case Study at SCR Piemonte

This thesis explores the application of Business Intelligence (BI) tools and performance measurement systems to the management of public infrastructure processes, with a specific focus on the Expropriation Office of Società di Committenza Regionale Piemonte S.p.A. (SCR Piemonte). The research originates from the growing need for public organizations to improve transparency, efficiency, and accountability through data-driven decision-making. The study begins by reviewing the theoretical foundations of performance measurement and Key Performance Indicators (KPI), highlighting their role in monitoring, controlling, and improving organizational processes. A particular emphasis is placed on the relationship between KPI, data governance, and Business Intelligence, and on how these elements collectively support the strategic and operational management of public sector organizations. The Balanced Scorecard (BSC) framework is also introduced as a structured approach to aligning operational metrics with strategic objectives. From a methodological perspective, the thesis details the construction of a relational database designed to consolidate heterogeneous sources of information, followed by the implementation of a data pipeline to ensure integration, cleaning, and transformation of data. On this basis, a Power BI dashboard was developed to monitor the activity of the Expropriation Office. The dashboard highlights key performance areas through a set of carefully selected KPI, offering transparency, traceability, and improved internal communication. The results show how the use of BI tools and KPI-driven governance provides tangible benefits, including faster access to information, enhanced process monitoring, and greater alignment between operational activities and strategic objectives. Furthermore, the research identifies limitations of the current model and proposes possible improvements, such as the integration of advanced analytics, automated alerts, and AI-driven predictive functionalities. The contribution of this work lies in demonstrating how an engineering-based approach to performance management can bring added value to the public sector, traditionally less advanced in adopting digitalization practices. By providing both a technical and methodological framework, the thesis offers practical insights for SCR Piemonte and potential applications for other public entities facing similar challenges.