Organizations today are increasingly tasked with putting data to use through the development and adoption of workplace analytics. While an abundance of data provides new opportunities, it also creates new challenges for organizations that must decide how to strategically deploy limited development resources and prioritize projects with a high potential for impact. Knowing what kinds of questions to ask, how to align data streams with existing workflows, and how to evaluate the impact of analytics in contextually-sensitive ways requires careful attention. In this talk, I show how interdisciplinarity can help address these challenges. In this talk, I draw on concepts from situated learning theory and innovation studies to demonstrate the value such interdisciplinary approaches provide in the analytics development lifecycle. I draw on my experiences as a social scientist working on interdisciplinary analytics teams in an industrial research setting to provoke a discussion around how cross-cutting approaches like these can foster creativity and collaboration in technical data work.