Abstract Large amounts of data are routinely collected in pig production, including operational farm data and, more recently, data from digital tools such as remote and on-site sensing technologies. Furthermore, additional sources of information, such as demographic, economic, and weather variables, can be combined with farm data. Integrating and analyzing such data can generate valuable insights and data-driven decision-making tools for disease surveillance, animal welfare, and production system optimization. This presentation discusses two examples of big data integration and analysis in the context of optimizing management practices in pig production. The first example pertains to a data analytics project related to total transportation losses (TTL) of market-weight pigs, an important animal welfare concern that economically impacts producers and abattoirs. After merging and checking data for errors, the final dataset included 4.5M+ pigs from 420 farms loaded in 26,819 shipments delivered to two abattoirs. Results from a generalized additive mixed model (GAMM) technique showed that the risk of loss per shipment was constant for temperature-humidity index (THI) values between −10 and 10. Additionally, a strong interaction between wind speed and precipitation was associated with TTL. TTL were strongly associated with the type of driver and the distance traveled, in addition to weather variables. The second example pertains to swine disease surveillance, in which data from a multi-site system, where pigs are transported between farms after each particular production phase, were investigated. Reports of 76,566 shipments across sites were obtained and used to characterize pig movement flow, conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and assess the potential impact of each farm type on disease dissemination across the system. Topological properties of networks were investigated, such as the number of nodes and edges, degree assortativity, density, average path length, diameter, clustering coefficients, giant strongly connected component, giant weakly connected component, giant in component, and giant out component. Overall, the results indicated that gilt development units, nursery, and sow farms have a more central role in the pig production hierarchical structure. As such, they are potential major factors in the introduction and spread of diseases in the system. Wean-to-finishing and finishing sites displayed high in-degree values, indicating that they are more susceptible to infection. Additionally, the characteristics of a disease should have strong implications for biosecurity practices across production sites.