The PT2020 funding program is important for Portugal’s sustainable and technological development, funding projects in various fields, including research, innovation, and the competitiveness of small- and medium-sized enterprises. All of this data is fully transparent, but there is a vast amount of data, which presents significant analytical challenges. These include difficulties in discovering relationships between entities, mapping flows, and extracting actionable knowledge from static tabular formats. This study uses graph databases to address these challenges, allowing for dynamic and intuitive exploration of funding data. By using social network analysis, the research identifies collaborative networks and shared funding sources between beneficiaries, offering a comprehensive perspective on the dynamics of funding allocation and sectoral impacts. The proposed approach provides valuable information on the underlying structures of PT2020, contributing to informed decision-making and policy development.