Many individuals working in research data management (RDM) support roles find themselves, at one time or another, in the position of presenting on the topic of “Introduction to RDM” or “Data Management 101”. Indeed, a cursory web search for either of those titles returns a number of slide decks, many of which include introductory slides enumerating reasons why someone (typically a researcher) should care about RDM. These lists include everything from funder and journal publisher requirements to reproducibility and reuse, from the benefits of staying organized to the need for data security. What is often left unsaid is that these reasons, which can be thought of as the goals or outcomes of RDM processes, each require different approaches, skills, and infrastructure. In some cases, these outcomes can even be at odds with one another. When attempting to demonstrate the impact of RDM on science or comparing RDM practices across disciplines, it is important to understand that RDM processes will vary depending on the desired outcome (e.g., data access/sharing, secure data storage, long-term preservation of data, data reuse and reproducibility, etc.). Using the example of psychology/neuroscience researcher needs at our institution, we argue that an outcomes-based approach to RDM is often more productive and meaningful than treating RDM as a single process or an end in and of itself.