We center our work with research and data in the data equity framework. Data equity is a new concept but underpins much of the project work we do with clients across fields, disciplines, and locales.

Data equity states that data is not objective. Usually those with the most power, influence and resources (i.e. funding) push vulnerable communities to provide accounts of their everyday lives and conditions without empowering a co-design model in which the community is at the forefront of decision-making. Data equity might assert that those closest to the problem are those also closest to the most effective solutions.
Our unique approach to data equity can be summarized as follows: 

• Data should realistically capture a population’s diverse demographics; too often, the collection and use of data increase the disproportionate disadvantages experienced by marginalized populations. 

• Data equity requires the analysts to compare the social identities of the sample set to the social identities of the larger population (representative sampling). The analyst must ask, “Do I have an accurate and inclusive representation of the racial, ethnic, gender, sexual orientation, ability/disability, regional, socio-economic status, and age diversity of the area?” 

• The analyst must also consider historical, structural, and institutional inequities (the legacy of redlining and racial segregation, lack of disability access in architecture and advertisements, language barriers, etc.) that may affect the analyst's ability to capture minoritized and marginalized populations. This consideration will assist the analyst in rectifying any social identity disparities. 

• The importance of using diverse perspectives during all phases (data collection, interpretation, project design, communication/presentation, analysis, and motivation) cannot be overstated. Data equity must include an intentional community engagement aspect to foster belonging and help the community develop a sense of data ownership. 

Data should be collected and used equitably. Equity and equality are not synonymous; people with different social group identities have different needs. Therefore, their data should not be analyzed using identical considerations. Data can increase or decrease inequities already present and pervasive in our society. It is the responsibility of the analyst and organization to ensure that data is collected fairly and used appropriately with the subject's (respondent’s) best interests in mind. Historically, and without a data equity framework, data has been extracted from marginalized communities and used for the benefit of those with the most privilege, power, and resources (funding). Our data equity frameworks have been developed to confront and reverse this phenomenon.