The value of geospatial big data and how to efficiently convert it to useful information is an important cross disciplinary question. There has been significant investment in the collection and archiving of geospatial and other environmental data and numerous organizations recognize the importance of this information resource. However, the software and computing systems to effectively and efficiently access, interrogate, and utilize this information for specific applications has not seen the same development. To address this, Environment and Climate Change Canada has piloted the CEOS Open Data Cube on a small compute cluster to facilitate the access, management, and analysis of geospatial big data within the python data science ecosystem. This has involved collaboration with NASA-CEOS, Geoscience Australia, and AFFC partners. This effort has also sought to incorporate deep learning to enhance capacity to extract information related to numerous wildlife and habitat applications. We will present an overview of our Open Data Cube Data Science Solution and some of the applications that it is being used to address.