Xarray has enormous potential as a data model and toolkit for labeled N-D arrays in biology. Originally developed within the geosciences community, it is seeing increased usage in biology, with applications ranging from genomics to image analysis and beyond. However, it has not yet been widely adopted. This presentation will investigate what the blockers have been to wider adoption, showcase the power of Xarray in biology through existing use cases, and present a roadmap for the future of Xarray in biological workflows through recent and upcoming improvements in Xarray.