Module 1.3: A Critical Approach to Open Civic Data
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Last updated
Last updated
This module offers participants an introduction to thinking critically about open civic data and the civic data creation process.
What are the principles of a critical approach to open civic data?
Why is it important for communities to think critically about open civic data?
How do we help ourselves and our users to interrogate open civic data?
A public library hosts a community conversation to discuss how data might be used to shape the library’s digital equity services. Several members of the community discussed how data about broadband Internet speeds was of interest to them particularly because of their experience with lower speeds in areas of town that have more Black and Latinx people. Other participants noted their concern about being surveilled and policed through data collection like web analytics. The librarian realizes they need to educate themselves about how to critically evaluate open civic data to better understand how to work with their communities to address some of the limitations and possible harms of open civic data.
Overview: As described in the Toolkit for Centering Racial Equity Throughout Data Integration, too often when a problem is identified, we jump to creating solutions. If we don’t understand the root cause of a problem, we won’t be addressing the problem itself, and our efforts will be inadequate. Working with stakeholders, we can identify the root cause of an issue and tell us “the story behind the data” (Hawn Nelson et al., 2020). To accomplish this, we would complete a factor analysis with stakeholders.
In this activity, we will apply a factor analysis to examine an issue. This activity can be done individually or in a group.
Supplies: No supplies required, but paper and pens for notetaking are recommended
Time: 30-45 minutes
Activity:
In the ideal implementation of root cause analysis, we would gather together a group of stakeholders to unpack an issue affecting our community. Using this approach, we collectively interrogate the root causes of an issue in our community or a trend that we observe in the data. As the authors of the Toolkit for Centering Racial Equity Throughout Data Integration explain, “Factor [or root cause] analysis of systemic issues supports stakeholders in using data and lived experience to uncover the causal factors behind an issue so that solutions can go deeper” (p. 44).
For the purposes of this activity, you may not be able to involve stakeholders, so try to think of an issue that is relevant to you/your group.
Activity Steps to Conduct Root Cause Analysis:
Identify an issue that impacts your community. This issue may be infrastructural (e.g. lack of bike lanes), socioeconomic (e.g lack of affordable housing), or environmental (e.g. air pollution) that impacts your region. Alternatively, it can even be an issue that is local to your organization. This issue may be reflected in data that we can access through our local data portal.
Impacts of issue on community members: Define the current outcomes of the issue for a population and relevant subgroups of a population (e.g., race, ethnicity, gender, class, race × gender). How are groups affected by this issue of concern?
Underlying root causes: Discuss the underlying root causes of the issue. Ask “Why?” five times to understand the causal factors and the problem and solutions for the whole population or subgroup(s).
Causal factors: What are the underlying reasons the problem or solution is occurring? (Ask this multiple times! It is likely that there are multiple contributing factors to the issue!)
Contributing context: What is helping to shape the underlying reasons? Are there factors in your local context that may have created or contributed to the root causes?
Gains being made: What or who is contributing to the bright spots? Are there gains being made in addressing this issue? Which of the causal factors are being targeted through these contributions?
Reflect: How may an assessment of root causes inform solutions/interventions? How can we apply this approach in interrogating stories present in datasets?
References:
Hawn Nelson, A., Jenkins, D., Zanti, S., Katz, M., Berkowitz, E., et al. (2020). A Toolkit for Centering Racial Equity Throughout Data Integration. Actionable Intelligence for Social Policy, University of Pennsylvania.