Translating Big Data into Smart Data for Veterinary Epidemiology

VanderWaal, Kimberly; Morrison, Robert B.; Neuhauser, Claudia; Vilalta, Carles; Perez, Andres M.

As in all long-term monitoring programs, sustainability of big data surveillance and monitoring efforts is a constant challenge (49) . For example, voluntary reporting programs such as MSHMP rely on weekly reporting by veterinarians, and adoption of new data standards and sharing of data across organizations requires investment of time, resources, and complicated data-sharing agreements. Even ensuring that all data fields are complete in clinical or diagnostic records (such as location data) requires investment of time and diligence by workers (14, 47) . Despite substantial individual and institutional investments, the collective and long-term benefits for big data animal health monitoring at the population, regional, or national level may be murky for the individual practitioner. Thus, sustainability may depend on creating short-term value for participating entities. For companion animal and equine medicine, aggregated health data could be used to research and subsequently deliver "precision" veterinary care that is tailored to the individual (5, 8) . For livestock industries, short-term value may focus on research that intends to improve herd and flock management.