Storing data close to where it is used improves the performance of cloud applications. However, data access patterns change dynamically over time. Many datastores statically shard data making locality-adaptation difficult, and some provide limited capability for controlling the data-placement or migration. This leads to increased latency, reduced throughput, and expensive operations. To address this problem, we investigate the requirements for live data-migration and design four data-migration polices. Our policies use heuristics to determine the optimal data placement based on the access locality in the workload and load-balancing constraints. We show that even simple heuristics can be effective, and the topology-aware policies demonstrate overall better results with up to 70% latency improvement in medium locality workloads and nearly 95% improvement in workloads exhibiting very strong single-region access locality.