Data Equity refers to the consideration, through an equity lens, of the ways in which data is collected, analyzed, interpreted, and distributed. It encourages further inspection into potential racial bias of research methods, publication’s role in the reinforcement and portrayal of stereotypes, and marginalized communities’ ability to control and access their own data.

Through our work in this area, it has become evident that there is a significant gap of knowledge about newcomer populations and how they intersect with other systems and services. It is also not clear what data is collected about newcomers within those systems, which in turn raises important questions about the capacity to provide equity and access in a culturally safe manner.

These principles were designed for anyone looking to embed equity in their research and data process, as we truly believe that data has the potential to be used as an accountability mechanism for change.

Principles for Data Equity


At every stage of decision making, from what you ask, what you measure, how you analyze, and how you present, data is not neutral


No response is a response. Categories of prefer not to answer and not applicable should be included and counted because they have meaning. Who is missing in your data and why?


Demographic responses should co-developed with the people whom they are meant to represent. Individuals should be self identifying and understand why the questions are being asked. Consider if multiple choices should be included


Use social identity data, not just to understand who, but to compare outcomes in the interest of measuring for equity


Don’t just count things. Use data to assess representation, measure over time, assess outcomes, and hold yourself accountable. Data is not just numbers, make sure stories add to the context


Collect the minimum data that you need, and use the data that you collect. In the interest of the people providing personal information, avoid redundancies