Modern data systems comprise of heterogeneous and distributed components, making them difficult to manage piece-wise, let alone as a whole. Furthermore, the scale, complexity, and growth rate of these systems renders any heuristic and rule-based system management approaches insufficient. In response to these challenges, statistics-based techniques for building gray or black box models of system performance can better guide system management decisions. Although statistics-based approaches have been successfully deployed, a single model is often inadequate to capture intricacies of a single workload on a single system. The problem is exacerbated with multiple heterogeneous workloads super-positioned on a consolidated system. An even greater challenge is to translate the behavioral correlations found by statistics into insights and guidance for designing and managing even more complex data systems. In this article, we reflect on recent work on using statistics for data system modeling and management, and highlight areas awaiting further research.