In this talk, I will present work from two separate internships that I completed in 2017. The first was a 6-month field study with the United States Agency for International Development, where I collaborated with Global Health experts to investigate major challenges surrounding the visualization and analysis of global health data. The field study led to a longer-term collaboration with a focus on externalizing expert knowledge of data discrepancies — a notion we formalize as implicit error — in global health data. The second internship was with the visualization group at the NASA Advanced Supercomputing Division, located at NASA Ames Research Center, where I was involved in a collaboration with a solar physicist to develop new visualization approaches for interactively exploring solar magneto-convection simulation data.