I spent a bit of today at The Climate Corporation, hosted by former astronomer Todd Small. He told me about things they work on, which include field-level predictions for farmers about rainfall and sunlight, precise prediction and instructions for irrigation and fertilization, and advice about crop density (distances between seeds). He said they get data feeds from Earth observing but also from soil testing and even the farm machinery itself (who knew: combine harvesters produce a data feed!). A modern piece of large-scale farm equipment is precisely located on its path by GPS, makes real-time decisions about planting or whatever it is doing, and produces valuable data. They even have a tractor simulator in the house for testing systems.
We talked for a bit about the challenging task of communicating with clients (farmers, in this case) about probabilistic information, such as measurement uncertainties and distributions over predictions. This is another motivation for developing an undergraduate data-science educational program: It doesn't matter what industry you are in, you will need to be able to understand and communicate about likelihoods and posterior probabilities. I gave an informal seminar to a part of the climate modeling team about our uses of Gaussian Processes in exoplanet discovery.