The American dream has become increasingly out of reach for many families, and data consistently show that expecting people to “pull themselves up by their bootstraps” ignores the structural barriers that shape opportunity over the long term, including access to economic success, power, autonomy, dignity and belonging. Although this challenge is widespread, some communities are hit harder than others. 

Data can help local leaders understand the true impact of housing affordability, access to child care, opportunities for higher education, and other important drivers of upward mobility on different communities. And data are a powerful resource for communities striving to increase upward mobility and prosperity by serving as both a diagnostic tool and a guide to setting priorities and making informed decisions on where to allocate resources, make investments, or deploy new programs. 

However, as many local leaders know, working with data presents its challenges. During a recent Urban Institute workshop, Clifton Phanawong Nelson with the Prosperity Initiative in Pima County, Arizona, and AJ Calhoun with Leading on Opportunity in Charlotte, North Carolina, shared practical strategies for overcoming common data challenges. Below is a summary of their conversation.

Missing, Inaccurate, or Incomplete Data
When data are missing, inaccurate, or incomplete, leaders can miss opportunities to focus resources and efforts to help those who need it most. A way to mitigate this challenge is by leveraging local partnerships to build “data relationships” with nonprofits, education institutions, and other organizations that are already collecting or could collaborate on collecting relevant data in areas of interest. 

For example, Phanawong Nelson  shared that Pima County conducted an inventory to understand what other departments in the county were doing to reduce generational poverty, what  data they were collecting, and which tools (e.g., dashboards) they were using to communicate with different stakeholders. This was “super useful to know we have departments from health to Sherriff’s office to sewage and wastewater [working on this, and it helps us] get everyone involved... to help out with this initiative and get people on the same page.” 

For hard-to-measure indicators, proxies—related, available data points used as auxiliary variables to help fill in missing information—might be accessed through private data agreements. Calhoun shared: “We have a lot of data about low-income educational pathways, because we've built relationships with the presidents of all of our regional colleges and universities, as well as our career and technical education and workforce development ecosystem, and they provide us [with] private data that helps to answer questions that we wouldn't otherwise be able to share.” 

These relationships can take time to build and require a lot of intentionality, as some partners may have had bad experiences with data sharing, but the effort can lead to more data-sharing agreements with nontraditional partners, including corporate partners, who may have really granular data.

When data truly don’t exist, new data can be collected through surveys and listening to community members. This can be a powerful approach that enables you to combine qualitative insights with quantitative data. Calhoun recommended starting by “just going to the places where the people who you want to be understanding more are spending a lot of time and listening, because you might find that you don't need to run a $20,000 survey just to find out something that the five community leaders already probably know.”

The Upward Mobility Initiative website offers several resources to help fill data gaps:

• Access the Mobility Metrics for your community on the Upward Mobility Data Dashboard, which provides data for all counties in the US and for cities with more than 75,000 residents.
• The Obtaining More Local Data resource identifies other state and local data sources that can be combined with the Mobility Metrics for a more comprehensive understanding of the mobility conditions in your community.
• Chapter 4 of the Toolkit for Increasing Upward Mobility in Your Community has resources to help guide you through how to collect additional data through surveys, focus groups and other community engagement activities, a list of local data sources, and case studies of how other communities have approached data collection.

Building Partner Trust and Buy-In
When stakeholders and community members aren’t involved in the planning process, it can create distrust in the data presented and the solutions proposed. Both Phanawong Nelson and Calhoun recommended showing the background work (your “receipts”) and being transparent about the process and the rationale behind your decisions. Involving people along the way can have a big impact on creating shared ownership, make the process more inclusive, and build their confidence in the data over time. Learn more about how to engage partners in the planning process by reviewing chapter 3 of the Toolkit for Increasing Upward Mobility in Your Community.

Avoiding Data and Data Tool Fatigue
Participants also raised that their communities are often saturated with dashboards and data tools, which has led to data fatigue. The speakers recommended focusing on clarity and relevance by asking yourself a key question: Do we really need this? If not, consider how you can help stakeholders make sense of existing resources instead of creating new ones. 

When new tools are necessary, integrate and link them with existing platforms rather than building from scratch. Design with a clear audience and purpose in mind—understand the question being asked and who will use the answer to drive change. Finally, provide actionable context so users know how to respond to the data. Tools like mobility metrics and the Economic Mobility Catalog offer proven practices to guide decisionmaking.

Data challenges are real, but they’re solvable when communities work together. These strategies can help local leaders turn obstacles into opportunities for lasting change.