What would data disaggregation for Asian Americans look like, and why does it matter? Disaggregating the broad category of “Asian” or “Asian American” into subgroups which take national or ethnic origin into account can help to illuminate the disparities present between different Asian American communities. This would allow for a more accurate assessment of need and thus equitable resource allocation for historically disadvantaged groups, for instance Southeast Asian refugee populations such as the Lao, Cambodian, Hmong, and Vietnamese. In this paper, I will discuss the concept of Asian American panethnicity and how it negatively impacts marginalized subgroups by perpetuating the “model minority” myth, masking the disparities revealed in disaggregated data on educational attainment, for example. I will then use Rhode Island’s 2016 “All Students Count Act” as a case study to explore the debate surrounding this issue, arguing that data disaggregation to substantiate the need for affirmative action should not be considered race-based discrimination, but a race-conscious practice that can support and facilitate success in more disadvantaged Asian American communities.