Managing the data movement is a major bottleneck in many computing systems. Today, there is an explosion in data set sizes. For instance, terabytes of active memory are required for training machine learning models. Unfortunately, at the same time there has been relatively little growth in the capacities of memory devices. These trends are leading to increasingly heterogeneous memory systems with a small amount of high-performance memory and high-capacity memories with lower performance. In this talk, I will explain why hardware-managed data movement techniques perform poorly in these heterogeneous systems, and I will describe a software-based technique, AutoTM, which outperforms a hardware DRAM cache. Further, I will discuss our ongoing work developing a "data management ISA" to enable software-directed and hardware-accelerated heterogeneous memory management. All lectures are free and open to the public.