Innovations in data science like predictive analytics, data mining, and data reduction have improved a variety of fields. Innovations in data science have also enabled the growth of two data-driven movements primarily used in clinical and counseling psychology: the common factors and common elements approaches. Each of these movements contains practical applications, such as the use of feedback within psychotherapy and the use of modular treatment. This paper describes the rationale of these movements, evidence on their effectiveness, and how these methods could potentially benefit school psychology practice within the context of multi-tiered systems of support (MTSS). Finally, the paper discusses the need for more research on these methods within school-based settings.