Public health policy relies on accurate data, which are often unavailable for small populations, especially indigenous groups. Yet these groups have some of the worst health disparities in the United States, making it an ethical imperative to explore creative solutions to the problem of insufficient data. We discuss the limits of widely applied methods of data aggregation and propose a mixed-methods approach to data borrowing as a way to augment sample sizes. In this approach, community partners assist in selecting related populations that make suitable "neighbors" to enlarge the data pool. The result will be data that are substantial, accurate, and relevant to the needs of small populations, especially for health-related policy and decision-making at all levels.