Spilling the Tea on UK Fundraising w Fixing Fundraising's Andy and Tom

Data can make a significant difference in addressing community needs and tracking progress towards a goal, but it can also be a tool of oppression, misrepresentation and erasure. From who is generating the data and why, to the assumptions and narratives created we must interrogate data practices and processes that can cause harm to our communities.

Anna Rebecca dropped a lot of concepts and knowledge, and Vu shared great examples, so here are some highlights that were mentioned in the show (sign up for our mailing list to get ahold of episodes early and learn more about these topics):

• Michelle talks with Anna Rebecca Lopez, a data nerd, activist, consultant and disruptor who believes in data for the people.
• This rad infographic shows part of a body of work Anna Rebecca created for the Community Centric Fundraising content hub.
• We also talk with Vu Le, nonprofit critic, speaker and author, and writer of NonprofitAF.com
• “Weaponized Data” was coined by Dr. Jondou Chen who is cited in this article by Erin Okuno and this blog post that Vu refers to
• Milwaukee Evaluation was also referred to and they are RAD
• Anna Rebecca, Vu and Michelle are all co-founders of Community Centric Fundraising

Here are some concepts:

• “Data is problematic in so many ways. It can be anywhere from how the data is gathered, who's using the data and even the types of questions we're asking before we even start in the data collection mode... Data in itself is used for so many reasons. It's used to make decisions for a community. Data is used to validate certain experiences or perspectives. Data is used to tell stories and oftentimes when those stories are inaccurate because of the data, it can cause serious harm to the communities.”
• Power Plays: “...oftentimes it's people who have power who are using data. It's people who have resources who are using data as people who have education, it's people who know how to use data and be able to read data and talk about data. And unfortunately there's a big gap between those who have access to data and know how to talk about it, how to use it, and the people who are in and of themselves contributing to a data set, or contributing information that then gets filtered into data. And so automatically right there, there's a big separation. There's a separation of the people who are providing this information and the people who are using this information...these are people in communities. And when we remove data from that understanding of humanity, it's easier to use data against people. ”
• Current trends in evaluation and research: “Evaluation as a tool that upholds white dominant culture, as a tool that even upholds white supremacy has been discussed since like the 1970s, especially when talking about culturally responsive evaluation, which is centering evaluation within the communities who are most impacted by the process. Now, just because that research has been around for 40-50 years now, doesn't mean that it's always being referenced or implemented...most of this work of culturally responsive evaluation...is led by people of color. And as we've seen people of color often not credited with the work that they've been doing.”
• An example of a data resource paradox: Vu tells a story about how a concept like the logic model, can be weaponized: And they rejected this grant because they're like, "Sorry, your logic model is not good enough". We weaponize these concepts, which often like A.R said, it's from people in power and who are people in power? It's going to be white folks. Who are the people at research institutions who were getting paid to throw the sort of terminologies and concepts and tools into the sector? It's mostly white elite educated individuals. And so this is a huge problem when funders are using this to gate keep funding, go into the community when they're like, "Sorry, you don't have a good enough data for us to fund you. You're out of luck". Well, how are organizations going to get good data if they don't get funding? So they're stuck in this data resource paradox. You can't get good funding unless you have good data, but you can't get good data unless you have good funding. ...So we are biased towards short term, white lead, tangible, easily measurable data an...