Effective management of transportation infrastructure requires structured and data-driven approaches capable of supporting maintenance planning and decision-making. This study presents a data-driven methodology for the organization and analysis of large-scale inspection data, with application to bridge bearing systems. The proposed approach is based on the development of a relational database designed to integrate bearing characteristics, inspection records, and contextual information within a consistent and scalable framework. The adopted data architecture enables the aggregation of heterogeneous information and supports systematic analyses at the network scale. A preliminary application to a dataset of more than 16,000 bearings from the Italian motorway network is presented to demonstrate the analytical capabilities of the methodology. The results highlight differences in damage occurrence across bearing typologies and the prevalence of corrosion-related defects, while also showing that their impact depends on the mechanical configuration of the devices. Overall, the proposed methodology provides a structured basis for the analysis of inspection data and supports the identification of potentially critical components, contributing to more informed maintenance planning. Although applied to bridge bearings, the approach can be extended to other structural components and represents a foundation for future developments, including the integration of environmental data and predictive maintenance strategies.