What's ahead in 2022 for the Equity in Data Community of Practice?
The Equity in Data Community of Practice ended the year on a high note. Our final session on December 21, 2021, was filled with lively discussion, interesting ideas, and data nerds!
We discussed our key takeaways from our previous session, the Do No Harm with Data Viz session of the CTData Conference 2021 and talked about what we’d like to do as a group in 2022.
Do No Harm with Data Viz reflection
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Our big takeaways from this session were these:
The importance of using descriptors that resonate with the community– example of use of Latinx, which is not commonly used by the Latino community.
Color, size, and demographic descriptors all have an impact on how data can be presented and perceived in visualizations. We need to use intention in their use - and get feedback from the people in the data on how these elements impact them. Also, words and text can influence what is the main takeaway from a visualization, even though we think the primary focus is the chart.
How can we use data/data visualization to grow the voice of impacted stakeholders as change agents? Along with that, remind status quo leaders that sharing the load is a value.
We have a responsibility to add context. Data alone can’t show the structures, we need to include context so people can understand the structures that lead to the outcomes. Need to ask ourselves “What else do we need to show?”
We can (should!) re-order the way our demographic data is displayed based on who is represented (not default to the order asked in the survey, which often puts “White” first).
The data we are displaying/analyzing can be heavily influenced by the biases embedded in the way it was collected
Applying empathy to our data analysis and visualization. We can keep this question in our back pocket to help us attend to empathy: “If I were a data point in this visualization, how would I feel?” Since we can’t always know for certain how we would feel, it is also important to be in partnership and communication for people who are in the data.
Listen to the “audience” on which graphics had the biggest impact on them.
We had some lively discussion around this idea:
The idea of a “standard”: We shouldn’t always use “white” as the standard. We often compare to the highest/lowest, but so often there’s another metric we’re trying to reach (like 0% poverty, 100% something, etc…). I still need to think this through more to apply, but their example from the Pulse Survey was helpful.
We have a responsibility to share the context of the data that we’re looking at. We need to include the history of how we got to where we are today when we present data
Showing the disparities and using one group as the “standard” can highlight that there are structural inequities that have created this result, and that all groups should be at least at that level.
Going forward, there was interest around creating access points to networks of discussion around context (such as networks working on housing and homelessness that understand the full context that we may not see working on housing and homelessness in one community).
What would we like to explore as a group in 2022?
Evaluating the data we’re collecting to ensure it is useful in understanding our impact.
Powermapping and data
How to structure an actual performance data tool for target neighborhood(s)? To effectively promote the positive impact of a CDFI in those neighborhoods.
Data dissemination
Choosing color for data viz. Takes a long time to pick colors, is there a “recommended” scheme? Could CTData offer one? (Can look at CTData’s projects, or the Urban Institute’s projects for ideas in the meantime.)
Specific data tools used, e.g., Tableau, etc.
Problems of practices.
How do you identify what the “standard” should be if it’s not the highest group (i.e. white has least % poverty, etc.).
Demographic data collection – how do we collect demographic data so that we can collect what we need then map out the aggregation so we can aggregate for our funders.
Building relationships among the group
I think it would be helpful for each participant to share a little about the data work they do, their priorities for DEI and the challenges. We may need to call upon each other some day collegially, and this would provide a path to access.
Opportunities to collaborate
Accessibility
Accessibility in our data projects (color blindness, blindness, ability, language, etc.).
Language access to data collection. We’re leaving out large numbers of people when a survey or form of data collection is only offered in English.
Engaging the community
How do you get more “voice” into your work, to hear from people about their context, and address our preconceived notions that may not be accurate.
How to make data more friendly and accessible for “community”. We need to build capacity in the public about data collection, usefulness, why adding their inpu/voice to surveys, data collections can have an impact on their day to day.
Clear, concrete strategies for engaging the voices of the “data point.” Engaging in community partnerships that are meaningful and equitable.
Context and outcomes
Does data need to incorporate land acknowledgements/history/context? How can we do that?
Integrating both qualitative and quantitative data to provide context.
How do we highlight structures with data? Disaggregated data could be read to be a cause of disparities or the outcome. Is there a better way to show this? How can we add context?
What metrics can we as data people agree on that we should be working toward as a state?
Disaggregating data. Is there an opportunity to explore other uses of data that can, in a more active way inform the root-cause understanding? Causal analysis. Can we do more than report on outcomes (which is descriptive)? How can we effectively weave narrative and data to point to the context and history of how we got the outcomes we have? This would point us to actions that we could make that could change the outcomes, leverage points that might result in those outcome data being more equitable.
Organizational culture
What do we need to attend to in our organization to ensure we are able to practice data equitable? What kinds of activities require different levels of “buy-in” from decision-makers vs. what we as data people can generally decide ourselves?
Funding conversation around equity in data and community. It takes a lot of work and effort to get to those root causes, to get to the communities and engage with them takes a lot of time. We don’t take the time because we either don’t have the time or don’t see the benefit of seeking the root causes. Understanding how we get there and being intentional, getting that info, putting our money where our mouth is, is important. How can we practically do this?
You can learn more about the Equity in Data Community of Practice here or look through the resources that have been shared from our sessions. We meet monthly, and you can sign up to join us here (curiosity and interest in data are the only requirements!). If you are interested to learn more about CTData, check out what we do and the services we provide. For training and tips on how to use data to inform your personal and professional life, register for one of our CTData Academy workshops or browse our blog. You can keep up with us by subscribing to the CTData newsletter and following us on Twitter, Facebook, and LinkedIn.