Equitable and Complete Neighborhoods in Madison, Wisconsin

Four images of Madison City Hall; interviews in progress; and team members sorting Post-Its

In the fall of 2016, the City of Madison launched a two-year effort to conduct a people-powered planning process to update Madison’s comprehensive plan. In the Neighborhoods and Housing issue area, residents expressed a dire need for “affordable housing in corridors with access to transit, schools, parks, libraries, neighborhood centers, and other amenities needed for daily living,” asking for more neighborhoods to meet that standard of completeness. While the Madison metropolitan area has grown steadily for the last six years, a 2013 report found that racial disparities between Madison neighborhoods created significant challenges for communities of color. The City of Madison responded to this finding by incorporating a distinct focus on racial equity into its strategic planning in 2017 including a goal that all Madison residents have equitable opportunities to live in and be part of strong, complete neighborhoods. Madison’s open data and innovation staff reached out to Sunlight for help using Tactical Data Engagement to address the demonstrated need for more complete neighborhoods in Madison.

Madison’s goals for open data

After working with Sunlight and the Johns Hopkins Center for Government Excellence in 2016 to refine its open data governance, Madison’s open data team decided to take their work to the next level by trying to directly connect with and address residents’ information needs. Madison’s open data champions wanted to use the on-going comprehensive planning process, Imagine Madison, as a launching pad for a new investigation into what kind of data residents would want to use to address local issues. Madison’s desire to work closely with residents around open data presented a perfect opportunity to pilot the Tactical Data Engagement approach starting in September 2017.

Madison’s story

To start, Sunlight helped Madison analyze public feedback from Imagine Madison. We found that residents were distinctly interested in more complete neighborhoods, and that information gaps likely contribute to some challenges in neighborhood well-being. To see how residents’ concerns overlapped with internal political will and strategic goals, Sunlight also analyzed Madison’s internal priorities which were expressed in an internal “Roadmap to Outcomes”. The roadmap showed that city employees were equally concerned and interested in helping Madison to build Complete Neighborhoods, and they had data sources that might aid in that effort. This direction was further bolstered by a community survey that found residents were interested in more open data related to complete neighborhoods. Since residents had asked Madison to prioritize complete neighborhoods – and internal conversations had highlighted opportunities to improve data practice around the City’s efforts to address neighborhood well-being – choosing complete neighborhoods as a focus area aligned both internal and external stakeholders.

Building on this focus area we next developed a list of community members who worked on neighborhood-related issues as potential interviewees. In building a list of interviewees, Reboot, Sunlight, and city staff worked together to assemble stakeholders representing various levels of data expertise or strategic decision-making power, from large funders in the area, to civically engaged individuals. Interviewees included community organizers, working data-driven professionals, business owners, civil servants, and individual problem-solvers. The goal in interviewing these stakeholders was be to listen to their stories and understand how they received, used, or shared neighborhood-related information in Madison. An important step of the process was including city staff in trainings giving them the tools to participate in Reboot’s design research sprint and take away some new skills in design research. This piece was essential for ensuring that city staff could replicate elements of the TDE process again in their future work.

Reboot and Sunlight interviewed Madison’s neighborhood data stakeholders with support from city staff to gain a better understanding of the open data environment. At the end of the interview stage, the research team and city staff synthesized insights to build out specific opportunities for residents to gain better access to or use public data to address neighborhood issues. The result of this research was a set of six open data user personas, all of whom are working on neighborhood issues, including illustrations of their data journeys and the pain points they face in trying to get data from the city and its communities. These journeys and pain points were refined into specific opportunities where Madison might convene or collaborate with specific neighborhood data users to improve city neighborhoods. The user personas report and the recommendations will be public as part of the conclusion of the refining stage.

Following the TDE process

To find a focus area that residents felt was important, we used existing public channels for public communication. In Madison’s case, that meant plugging into the ongoing Imagine Madison comprehensive planning process. This approach was a great way for Madison to start listening for signals that showed who we could talk to about data needs in the community. Other cities interested in testing TDE approaches may look to their own urban planning processes as an open door to reach out to community members.

Imagine Madison produced a wide range of research about issues important to residents. To narrow down Madison’s TDE project to a specific open data-related issue, we sought out shared priorities from the research that overlapped with the city’s internal strategic priorities. Hearing about important issues, both inside and outside of the city, helped us ensure that Madison’s TDE work connected to genuine local issues.

To determine how residents might want to use data to address neighborhood issues, Sunlight partnered with the social impact firm Reboot and their team of design-research experts to conduct in-depth research on local data needs and construct user personas. This process involved using Reboot’s design-thinking methodology to conduct in-person interviews with members of the community working on neighborhood issues, and build out user personas to describe how they use data in that work.

User Research Process Over the course of a two week research sprint, the Sunlight and Reboot team interviewed 36 stakeholders in Madison to understand their stories and information needs. Reboot’s design-research approach helped structure this process:

  • Research design: Sunlight first worked with Reboot to develop research objectives, lines of inquiry and question guides, as well as target respondent characteristics.
  • Key informant interviews: Before conducting interviews focused on understanding information needs relevant to complete neighborhoods, Sunlight and Reboot interviewed “key informants” who could provide context and direction, and help us refine our research objectives and lines of inquiry. Key informants included city staff and community leaders with expertise on open data and complete neighborhoods issues.
  • User interviews: The bulk of the Sunlight and Reboot team’s time over two weeks on-site was spent conducting in-person interviews. “Snowballing”— asking those we interviewed who else we should be talking to — was a key aspect of our approach to ensure we reached relevant neighborhood actors. Whenever possible, we met interviewees where they were — in their offices, at their local coffee shop, or in at least one case, even on their living room sofa — meeting people in their own context helps with mutual understanding.
  • Documentation and synthesis: Each interview was recorded, wherever possible, and transcribed. The Sunlight and Reboot team documented our observations and insights each day. We discussed emergent patterns and organized interview notes into various categories that helped analyze and synthesize this qualitative data into tangible findings.
  • Constructing personas and journeys: These patterns and findings helped us identify six common open data user types and to map their processes for acquiring, analyzing, and applying data and information.

Conducting interviews through a two-week human-centered design research sprint worked for Madison, because our goal was to deeply explore the benefits of doing in-depth interviews with residents to develop user personas. This has been the Open Cities team’s most hands-on project to date. Getting a comprehensive view of how residents were using data in their work to build more complete neighborhoods helped us better understand how the City’s open data efforts might plug into their real needs and goals.

Next steps

Building on user research and findings, Sunlight will next help Madison re-engage with a subset of target open data users to support one of the use-cases identified. The result will be a plan of action followed by an implemented intervention to support specific users.

Outcomes toward better open data
Our in-depth work on Tactical Data Engagement in Madison has produced insights about open data user needs and opportunities for the city to better support the community use of open data. While Sunlight’s engagement with the city will focus on one specific opportunity to execute using TDE, Madison’s open data team will have gained a new understanding of how they might plug into residents’ needs and experiences in the future.

As Madison continues to support opportunities for the community use of data, either around complete neighborhoods or other pressing issues in the future, we hope city staff will remain engaged and enthusiastic about the potential for engaging residents engagement to drive the social impact of open data.


Sunlight’s analysis of public concerns and in-depth open data user research has uncovered the following findings relevant to the design and implementation of Madison’s open data program:

How residents acquire information

  • Observations:
    • Skilled data users know how to find the data they need
    • Successful data users have the time and resources necessary to acquire data.
    • Academics, large nonprofits, and motivated community members are more likely to use data to inform action
    • Awareness about Madison’s open data portal is low, particularly among less skilled data users
  • Strategies to engage:
    • Facilitate access to city and other data sources relevant to neighborhood development organizations.
    • Increase value and relevance of city data to neighborhood development organizations.
    • Increase user confidence in city data.

How residents analyze information

  • Observations:
    • Successful data users either have or can access the technical expertise to analyze data.
    • Successful data users know who is responsible for collecting and maintaining data for the city, and are able to reach out to them for clarification.
  • Strategies to Engage:
    • Establish points of contact for city data sources so that people know who to contact about data.
    • Enhance interpretability of city data.
    • Connect low-capacity CBOs to technical expertise.

How residents act on information

  • Observations:
    • Successful data user have access to data at the granularity they need it.
    • Successful data users utilize multiple sources of data to improve their analysis and make their arguments more compelling.
    • Successful data users, in addition to their technical skills acquiring and analyzing data, are also strong communicators.
  • Strategies to Engage:
    • Publish more indicator data, catered to CBO needs.
    • Highlight successful data to action use cases.

Read more

Activating Neighborhoods: Open Data User Research to Support Complete Neighborhoods in Madison, WI

Read our full report to the City of Madison, including a project overview, a research overview, user synopsis, list of opportunities and challenges, strategies the city should consider, and next steps.

User personas and journeys in Madison, WI

These six user personas and sample data journeys are designed to help think about problems from users’ perspectives, and spot patterns and themes through their experiences.

Sunlight will produce additional findings as we work with Madison to convene neighborhood data practitioners and residents to improve the city’s data support for those working toward more complete neighborhoods.