Data Equity: Questions to Consider

This blog is estimated to take 3 minutes to read.

The addition of a Grants Administrator role to the Weitz Family Foundation team is a game-changer. We now have more internal capacity to track and collect more data about our grantmaking. To prepare, we took courses from We All Count, a firm focused on equitable data practices. We learned about the pitfalls of biased data collection and the importance of a comprehensive Data Equity Framework.

Even well-intentioned data projects can be unintentionally biased. The Data Equity Framework guides someone through a critical examination of their data collection process to identify and address potential issues.

While we’re not experts yet, we’re committed to ensuring our data collection is fair and equitable. We’re starting with these questions to guide our approach:

• What are the primary/secondary goals of collecting the data?
  • Whose definition of success are you using?
  • Is it specific/clear what you are trying to achieve from collecting the data?
• What are you trying to measure?
  • Are you trying to describe something that is happening?
  • Are you trying to understand the relationship between two or more things?
  • Are you trying to explain why something is happening?
• What is the best methodology for collecting the data?
  • Would using a survey, focus group, interviews, etc. be best for the project? Why?
• Who’s involved in the design or methodology of the project?
  • Are you including those who would be most impacted by the results as well as community members and other stakeholders, when appropriate?
• Who is collecting the data and what bias might they have?
  • If your organization is collecting the data, who from your organization will do that work?
  • If you are using an outside data collector, how familiar are they with the population whose data is being collected?
• Is the purpose of the data collection clear to those being surveyed?
  • What terminology/language are you using?
  • Is there a shared understanding of the terms/wording at the core of your data collection?
• Who is being prioritized when you analyze the data?
  • What categories are you using to reflect the population be surveyed? For example, if you’re collecting demographic data, are people allowed to self-identify? Why or why not? How might this change your results?
• How are the results shared and who are the results shared with?
  • Do the participants have free access to the results?
  • Will the results be shared internally? With funders? Other stakeholders?
  • If the data is shared across audiences, how will the emphasis shift when presenting to different stakeholders?

While this isn’t an exhaustive list, we hope sharing our thoughts prompts you to reflect on your organization’s data collection practices. Many of these questions have no right or wrong answers, but it’s crucial to recognize the impact of our choices when setting research questions or metrics to track. A helpful exercise is to review recent data collection efforts and assess how well you can answer these questions.

To delve deeper, we strongly recommend starting with the Data Equity Primer from We All Count before exploring The Foundations of Data Equity. As funders, we’re also closely following the work of The Center for Evaluation Innovation. Feel free to share any valuable resources you’ve discovered. We’re excited to leverage our growing staff capacity to learn and embark on new projects!