Edge computing has become a prominent solution when it comes to mobile applications and data management, due to its ability to considerably reduce data transmission costs, and to analyze data requiring fewer computing resources, since the analysis occurs at lower data volumes, without having to relocate data to centralized infrastructures. One major challenge, indicates the optimal data placement regarding data-intensive applications and, in general, applications requiring vast transmission of large amount of data. In this paper, we propose a novel user mobility-based data placement strategy, considering a trade-off between latency and data migration, which has not been investigated before. We classify the users into three mobility classes; namely static, local or mobile, via the use of a causal-aware Deep Learning network. This information is then exploited in order to optimize the data placement through specific data placement and retrieval algorithms for each mobility class. We evaluate the performance of the proposed solution using simulations, and prove that our solution manages to reduce the average data accessing cost by 60% for static or local users and 10% for mobile users, while the average path length is reduced by 50% for static and local users, and by 12% for mobile users.