Data is never neutral. There is an array of international, national, tribal, state, provincial, regional, and local data available to collect age, gender, race/ethnicity, income, location and other demographics. In every instance, there are both technical and political issues to consider: the raw data that is collected, in addition to the stories that the data tells, generally reflects the values of the entity, for example the government or private institution, that is collecting the data. Databases also reflect individual and societal decisions about what information groups are open to making public. Finally, data is often categorized in ways that reflect dominant cultural norms. And, even when these norms are changing (as in the case of acknowledging multi-racial identities or a spectrum of gender identities), public databases may lag years behind how researchers and activists are reconfiguring identity groups and other classifications.

Why does this matter for racial equity work? Because the people most likely to be excluded in public data sets often represent populations who are already the most marginaliz [...]