Many organizations spend way too much time and money getting data ready for analysis — before a single insight is gleaned. Learning how to innovate with data science and machine learning (ML) is challenging because that capability needs to be integrated into the IT stack. The traditional approach of standing up a data stack for AI does not work because it’s too complex to manage data replication between multiple platforms.