Research shows that high-quality pre-K can have lasting impacts on children's learning and development, with the largest effects clustered among children of color, multilin­gual learners, and children experiencing poverty. Yet policymakers, administrators, and teachers often wrestle with the complexities of providing these programs on a large scale and in rapidly evolving contexts. This can undermine opportunities to close income and racial inequities in early learning. A key challenge is the lack of reliable, unbiased data about children's skills, knowledge, behaviors, and classroom experiences--information that could guide teacher practice and inform investments in early learning systems. The COVID-19 pandemic has underscored the pressing need to collect this type of data as part of the normal operation of pre-K sys­tems. As the nation emerges from the pandemic, in-person pre-K activities have resumed with scant information about how children fared over the past year and a half. This limits how instruction and support can be effectively aligned to meet the needs of teachers and the young children they serve. The routine collection of such information on a scale large enough to appropriately answer questions about what works and why, for whom, and under what conditions, is critical to the future of effective and equitable pre-K programming. MDRC, in collaboration with Substantial, a human-centered design firm, and with funding from the Bill & Melinda Gates Foundation, is leading an initiative aimed at shifting the data landscape across the early learning and pre-K field. This brief presents some starting points the team will examine with states, districts, other pre-K providers, and researchers.