Over the past three decades, the prevalence of dedicated advanced monitoring systems on complex systems has grown extensively. The data aggregated from these monitoring systems, as well as other data sources such as flight test, supply chain, maintenance, safety, operator information, design specs, and logistics, provide valuable insight into how the assets are being operated over their lifespans and enables a wide range of advanced analytics that supports fleet sustainment. Using Sikorsky helicopter fleets as a case study, this talk will discuss how large data sets collected from fleets of assets are used to enhance safety and enable intelligent decision making about the sustainment of the assets including usage monitoring, optimizing maintenance & supply chain, maximizing availability, focusing troubleshooting, and enabling proactive and timely support. This is all enabled by massively scalable data platforms that facilitate the collection, storage, algorithm development, and retrieval of data collected from these fleets of assets. The architecture of this platform and the software and hardware used will be discussed, including the software framework that was created for scaling these resources out to engineering subject matter experts. Specific deep learning applications will also be presented including how deep learning was used for multivariate time series anomaly detection as well as feature extraction and classification.