Science is fueled by data. Throughout history, scientists have operated sensors-from astronomical observatories to particle accelerators-that accumulate observations for analysis or to evaluate a hypothesis. However, as available technologies have increased both the volume of data and the efficiency of data storage and transmission, a new model of data access has emerged. The concept of a data buy is where an entity purchases access to a set of data or a data stream, instead of operating the sensors themselves. But why might a data consumer, whether a researcher or an end-user, prefer this kind of data access over the more traditional methods of running a network themselves? The simple answer, in some cases, is efficiency and, possibly, cost. Space weather forecasting and analysis has a growing private sector, and the extension to data gathering can be considered as a natural next step in the maturation of the field and the growing public-private partnerships. Operational applications require consistent, clean, and (in some cases) real-time data access that can be hard to support through the existing model of sensor deployment. Even in scientific applications, where access to raw information can be critical to discovery, there are benefits to the data buy model. Consistent access to a trusted data set allows more time to be spent on the scientific analysis, instead of maintaining machines that require consistent development, maintenance, and monitoring. The outsourcing of data infrastructure and pipelines can be particularly beneficial when the sensors are in distributed networks, spread over wide areas, and when there is a need to provide local data in observational gaps in existing networks. In the ideal case, a data buy can supplement the traditional observational networks in a beneficial and symbiotic way. It is important to note that data buys should not replace traditional observational networks, nor compete for funding with future observatories and infrastructure that the scientific community has deemed necessary (for example, through decadal survey processes).