As a company, we are developing our racial equity lens to critically examine everything we do, including what is generally considered the “objective” work of data collection and analysis. To help us reflect on our practice and how we can increase equity in our methods, we attended an online training by We All Count focused on their Data Equity Framework. The framework interrogates the different stages of the research process with an equity lens, and identify potential ways to address potential inequities.
What were our main takeaways?
The training emphasized a few key principles to reflect on when to use data methods in pursuit of equity:
- Data is not objective. Numbers are as subject to bias as stories because research methods are developed and implemented by people who have their own world views and biases.
- Research will never be 100% neutral. We can, however, make data more equitable by being transparent about its origins, the methods used to collect and analyze it, and the motivation and goals of the research project.
- As researchers, we are always making a choice about the data. To infuse equity into our methods, we need to be explicit with ourselves, teams, clients, and communities about our choices and the reasons behind them.
As we reflected on these principles as a team, we saw an opportunity to deepen our existing community- and culturally-based consulting practices.
How have we used the tools that were shared?
As part of the workshop, We All Count offered a number of reflection tools for each phase of a research project:
These tools are intended to help us pause and think about who our methods are giving voice to and who we are leaving out. Some of the tools we are finding helpful in our own work include:
Funding web. A funding web looks at the flow of power: Who has money? Who has data? Who has influence? The interplay between these questions makes clear to clients and communities what power imbalances might influence how (and what) data is collected and analyzed. This is similar in orientation to power mapping because it helps to make transparent who may be influencing the orientation of the research and opportunities to mitigate resulting inequities. lays clear potential inequity pitfalls.
Data biography. We have started to be more intentional about creating a data biography during the Data Collection & Sourcing phase of the work. This is a deliberate process to look carefully at the who, how, where, when, and why of the data—both data collected by others, and data you have collected yourself. These questions help us have more intentional conversations with our clients about the assumptions around, and limitations of, the data we are using. For example, we regularly receive data sources from clients that are specifically developed to address a funder’s research questions. These questions may or may not be the issue of most importance to the community.
Discuss denominators. During the Analysis phase, the denominator is the locus of power and significantly impacts all resulting comparisons and summary statistics. This training emphasized the importance of considering who is NOT in denominator. Who don’t you have data on? What don’t you know? Transparency around what you can and can’t see is crucial to equity in data. For example, a recent public health client had two databases tracking point of contacts. When we merged the data files, nearly half of the incidents were excluded because they did not match across datasets. Technical practice suggests we exclude those not matched. Equitable practice requires examining who was left out, the implications of this omission, and how to address those omissions.
Separate results from interpretation. The training emphasized the importance of being clear about when you are presenting data versus making an interpretation. We reflected that we often weave together results and discussion to tell an engaging story and that we need to be intentional with this practice. One way we ensure a separation of results from interpretation is through “sense making sessions” with our clients to provide raw results and then collectively interpret them together.
How do we connect this to our emerging anti-racism practice?
As we thought about this blog, we connected the training to our internal book club readings, in particular Ibram X. Kendi’s work, “How to Be an Antiracist.” Kendi explains that, “like fighting an addiction, being an antiracist requires persistent self-awareness, constant self-criticism, and regular self-examination.” As researchers educated in traditional notions of objectivity, the training drove home the importance of constantly reflecting on challenging our own practice to ensure we are using an equity mindset to conduct data analysis with an actively anti-racist approach.