This document discusses new data applications like machine learning and deep learning and their implications for storage. It notes that these applications deal with large and diverse data types including time series, matrices, and graphs. They have relaxed requirements for data correctness and persistence compared to traditional transactions. Opportunities exist to optimize storage for these workloads through techniques like tiering across memory types, streamlining data access, and exploiting lineage metadata to cache intermediate results. Fundamental shifts may also be possible by integrating analytics optimizations into storage management.