Digital trace data are receiving increasing attention in information systems research. Compared to traditional research methods, digital trace data research offers several advantages. Among other things, it allows researchers to observe behavior at scale—either over extended periods, many actors, or both. In some cases, digital trace data allow researchers to explore phenomena that could previously not be examined or develop novel viewpoints on already well-researched phenomena. However, digital trace data have particularities that need to be taken into account when carrying out research projects, such as the highly interdisciplinary nature of such projects and issues related to data quality. The purpose of this chapter is threefold. First, I explain the research process in digital trace data research projects and point to potential pitfalls as well as how to mitigate them. Second, I suggest that scholars capitalize on design science research to move from individual digital trace data research projects to larger research programs. I illustrate how to make such a move and its implications by drawing on research on process complexity. Finally, I examine how AI can support research and what considerations this entails, a timely concern for many scholars today.