- Impact Austin
Discovery Days 2023: Session Two Recap - Data Equity and Impact
Our 2023 learning series takes a different approach from past Discovery Days. Instead of exploring specific community needs and issues, our three sessions dive deep into one aspect of grantmaking: impact. Impact Austin's Grant Review Committees (GRCs) regularly explore how to assess and compare intended outcomes and potential impacts among the dozens of grant applications received in each focus area. With that in mind, our 2023 theme developed.
Session One asked How Do Nonprofits Measure Impact? The recap and recording of Session One can be found here. Session Two dove deeper into data equity. Please find bios for our participants here. Our presenter was Ashley Hickson, DrPH, MPH, Senior Health Equity Advisor, Center for Science in the Public Interest.
What is data equity? It refers to the consideration, through an equity lens, of the ways in which data is collected, analyzed, interpreted and distributed.
Why does data equity matter? Failure to deploy a data equity framework in terms of collection, analysis, and interpretation practices can:
Mask existing or emerging inequities
Reduce the agency of the people being studied
Dr. Hickson gave examples of each of these at the 10:04 mark of the recording.
Data equity should inform every stage of the data life cycle: collection, analysis, and interpretation.
What do participants gain from the results? If the research/intervention program doesn't yield something to benefit the participants, we need to re-evaluate it as being equitable at all.
Dr. Hickson gave a high level overview of frameworks, detailing two of her favorites in this session: the intersectional framework and racial framework. The intersectionality framework considers interlocking systems of privilege and oppression. She gave the example of studying individuals in poverty - but perhaps they are also Black - and also Queer identifying - and also with a disability. We must make sure the data does not fail to capture sub-populations. Ask: what data am I looking at? What population am I looking at? What is the problem? How can I get more granular?
A racial justice framework is explained below. Dr. Hickson gave an example of a study of obesity vis a vis COVID-19 mortality rates. When the racial justice framework is applied, data starts to tell a different story. That discussion happens at the 20:15 mark.
Checking for bias - Dr. Hickson described the Nonprofit Industrial Complex, a series of relationships between donors/funders and large national nonprofits in which what is funded is driven by the interests of the donor/funder. This can yield challenges:
Can affect the kind of data taken into consideration
May reduce the optics for smaller, community-led organizations
Consider a smaller nonprofit's barriers to raising capital and some fundraising inequities.
Does the leader of a smaller nonprofit have inequitable access to social networks wherein s/he might connect to funders?
Is there interpersonal bias or mistrust because the nonprofit is small?
Does the small nonprofit have the capacity or longevity to have collected the kind of data that a funder might require or that the funder is accustomed to seeing?
Does the funder really understand the culturally-relevant approach that the small nonprofit might have, compared with the traditional approaches of a large national nonprofit?
Measuring impact - Who has defined the problem, and what are you seeking to measure? What are you looking to do? Does publicly-available data already exist? If so, that will make impact easier to measure. How does the priority population view the problem, and are you measuring what is important to them? Can the impact even be measured during the period of the grant?
Measuring impact calls us to consider the power dynamics between the funder and the nonprofits. Does the funder value or discount culturally-relevant approaches vis a vis traditional approaches? Does the funder only value certain kinds of data? Does the funder see the nonprofit as a partner?
Using data for social change:
Be mindful and honest about the limitations and opportunities that the data presents.
Don't overstate the significance of the data, especially when it comes to addressing complex social challenges. Examples: solving the big picture of poverty with financial literacy education; or solving health disparities with classes in healthy cooking.
Identify opportunities for future/more equitable data collection and analysis.
Questions and answers from the listening audience:
As funders, how could we invest in reducing our biases?
Set goals around DEI in terms of how you fund. Include it in your organization's strategic planning. Have an external consultant help to assess past practices and make recommendations moving forward. Dr. Hickson read that only 25% of funders have goals around DEI as it pertains to funding.
Please describe the Health Equity Assessment Tool - Dr. Hicks developed this for her organization, using publicly-available data sets. It considers where applications are coming from and where the funder can have the greatest impact. The tool allows for objective scoring and assessing which applications come from areas of highest need.
Can you think of a funder doing great/smart grantmaking using equitable data?
The Bridgespan Group recognizes their privilege and the power dynamic but also the opportunities in this space. They have invested in many diverse-leader organizations. They are on the front end, laying the foundation in where we need to be.
For collective giving groups like Impact Austin, how can we move beyond funding the usual large, well-known organizations?
Have trust in smaller organizations to have the capacity to serve their population.
Invest in smaller organizations; build trust with them.
Perhaps give smaller unrestricted grants that are easier for smaller nonprofits to process, that build trust, that build capacity. In this way, both the funder and nonprofit may be more comfortable with a large grant later.
If we don't invest in smaller nonprofits, we won't help grow their capacity.
How can we better serve diverse leaders and new approaches?
Build trust. Trust that they know their population and their approaches. They have the identities and experiences of their community.
They will have "boots on the ground" in their community.