Systematically reconciling distinct local observations into a global picture is a common challenge in data analysis. In this talk, I will explore the use of applied sheaf theory as a unifying framework for measuring internal consistency in complex datasets. Building on the work of Michael Robinson, I treat sheaves as a formal mechanism for ""gluing"" local information and quantifying the resulting disagreement. I then demonstrate applications of this framework to two disparate domains: spatiotemporal source estimation and knowledge representation. First, I present a method for estimating the location of wildfires by treating wind and pollution sensor data as local information and optimizing for global consistency. I then extend this logic to knowledge hypergraphs, using poset-enriched sheaves to formalize subsumptive semantic type checking. Together, these examples illustrate the versatility of sheaves in detecting inconsistencies across both physical and conceptual spaces. This is joint work with collaborators at the University at Albany and the Pacific Northwest National Laboratory.