The proliferation of technology-enhanced learning tools and the data captured upon use, has the potential to unlock a wealth of learner insights. Behavioral, tool-interaction, and performance data can provide a window into learning processes within a digital learning experience and enable claims about how people learn, what they know, where they have gaps, and the ways in which they can best be supported to close those gaps. However, the meaningfulness and reliability of those inferences depends on the veracity of the validity argument developed for the use of the tool and the data being collected and analyzed. Considerations such as clearly articulated outcomes, a robust, theory-driven data collection design, the quantity and quality of data captured, the context in which they are analyzed, and how they are reported, will all affect the reliability and validity of the inferences made from analyzing learner data and the usefulness of decisions, actions, and claims that are made subsequently. In order to make the most meaningful claims based on learning analytics, learning scientists should work closely with research scientists, user experience researchers and designers and, developers to intentionally design and implement a solution that enables appropriate and meaningful insights. The goal of this talk is to provide several practical steps toward proactively addressing challenges that inevitably arise when designing and implementing a digital learning experience to yield accurate and actionable insights about learning and the efficacy of a learning experience.