Proper use of time series and location data in prediction and optimization can considerably boost the yield of data science and AI initiatives. While location and time data have been available for business use, using them in AI requires scaling them as spatiotemporal functions that can be processed with high performance. This has been a major industry challenge, due to the fact that key geospatial functions are locked away in database silos or fragmented everywhere. Just as we are automating AI lifecycle management with AutoAI and promoting AI model trust and transparency with Watson OpenScale, IBM Research has been tasked to solve this additional, demanding challenge. Spatiotemporal functions implemented as part of Analytic Engines in Watson Studio are now coming to Cloud Pak for Data.