Article Title

Data as a Strategic Resource: Self-determination, Governance, and the Data Challenge for Indigenous Nations in the United States


Data about Indigenous populations in the United States are inconsistent and irrelevant. Federal and state governments and researchers direct most collection, analysis, and use of data about U.S. Indigenous populations. Indigenous Peoples’ justified mistrust further complicates the collection and use of these data. Nonetheless, tribal leaders and communities depend on these data to inform decision making. Reliance on data that do not reflect tribal needs, priorities, and self-conceptions threatens tribal self-determination. Tribal data sovereignty through governance of data on Indigenous populations is long overdue. This article provides two case studies of the Ysleta del Sur Pueblo and Cheyenne River Sioux Tribe and their demographic and socioeconomic data initiatives to create locally and culturally relevant data for decision making.


This article had many contributors. Primary thanks go to the tribes and authors that shared their experiences in this document, the Cheyenne River Sioux Tribe and Tribal Ventures, as well as the Ysleta Del Sur Pueblo. Special thanks are due to Stephen Cornell, John Ehiri, Celestino Fernández, Miriam Jorgensen, Desi Rodriguez-Lonebear, Douglas Taren, and Nicolette Teufel-Shone at the University of Arizona; Gwen Phillips at the Ktunaxa Nation and the British Columbia First Nations’ Data Governance Initiative; Sarah Kastelic at the National Indian Child Welfare Association; Malia Villegas, Sarah Pytalski, and Amber Ebarb at the National Congress of American Indians; staff members and students at the Native Nations Institute; and numerous tribal leaders for frank and thoughtful input and insights. The authors gratefully acknowledge the W.K. Kellogg Foundation and the Morris K. Udall and Stewart L. Udall Foundation for their financial support.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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