Many tasks in data science, including geometric reconstruction, distributed dimensionality reduction, data alignment and coordinatization, can be phrased as local-to-global inference problems with well-defined topological obstructions. The goal of this talk is to describe some of these constructions, and the emergent theoretical/algorithmic challenges in geometric and topological data analysis.