How Governments Can Bridge Service Gaps with Data
Code for America uncovers techniques to increase the reach of services through equitable data use.
There’s nothing more vexatious than wasted efforts. Every year, local governments invest the time, political capital, and troves of dollars for services many never use. It’s defeating and can be a crushing blow for morale, especially when it’s discovered those who might benefit most from such services are unintentionally excluded.
As part of its annual summit, the civic tech group Code for America (CfA) offered a few data-driven tips to increase the service reach of the government. Insights came from “Data for Equity and Justice,” a workshop built around lessons learned from the group’s digital transformation efforts in social safety net programs like Medicaid and food assistance. Eric Giannella, CfA’s data science director, and Eleanor Davis, a CfA senior program manager, led the workshop that taught how equitable data use could improve services.
“We really wanted to start talking about this idea of equity and how data can be a really powerful tool in understanding disparities and in creating services and systems that serve people equitably,” Davis said. “At Code for America, one of our deep focuses is to ensure our products and services are equitable. The intentional use of data is a big part of how we do that.”
Generally speaking, Davis and Gianella said data equity in government means the data departments collect is representational of the communities they serve. The argument is if data is representational government decision-making and service delivery will be also.
“When we say inclusion, we’re really talking about bringing folks who are most impacted by whatever it is we’re looking at into the processes, activities, the decision, and policymaking in a way that intentionally shares power,” Davis said. “Equity is thinking about bringing in an element of fairness or justice.”
Outside ethics “data equity” and inclusion also have administrative and budgetary benefits for governments as well. Davis and Gianella said there is a business case to made for equitable data use. Data equity translates to efficiency. Similar to the eCommerce giant Amazon—who is constantly collecting user data—representational data leads to the creation of targeted programs, hi-demand services, and ultimately measurable performance outcomes. Here, data equity isn’t just about doing good, it’s good business.
The barrier of inaction
Despite benefits, the three-hour long workshop also surfaced common challenges in data equity efforts. The biggest challenge identified had surprisingly little to do with the technical aspects of analyzing and interpreting data and everything to do with taking action.
Attendees, many of whom worked in the public sector, said government had a difficult time starting data equity work due to fears of public reprimand. If data analysis, for example, showed service delivery discrimination some governments worry well-intentioned efforts to improve services would be met with a community backlash. These trust issues, one attendee said, can halt work even before it begins.
Another attendee who worked in education said the typical and one of the most detrimental obstacles he encountered was officials’ inability to follow through with effective data equity measures.
“We’ll set up a commission, committee [an issue] to death, then feel like we achieved something with no actionable results,” he said.
Another workshop participant said the lack of follow-through was equally pernicious in his organization. Governments, he said, often forget to ask themselves if they truly intend to change their organization’s behaviors if presented with new, representative data. Often, overly optimistic expectations are created with studies and data work when it isn’t accompanied by leadership support.
“You need to understand what changes you’re willing to make,” he said. “If we discover that there is an inequitable situation, are we willing then to invest? Or if you’re in a government agency, are you now compelled to do something about it?”
Tips to get started
As an entry point to data equity, Gianella and Davis offered three tips to help governments get started. The takeaways centered on pragmatic approaches and tactics that could be applied in almost any jurisdiction:
1. Don’t let data push you around
Gianella said you don’t have to be intimated or overwhelmed by data. State and local departments can start small, and work with the information they have, and ask questions when data isn’t clear.
“Just start by using the data you already have—people’s age, gender, race, ethnicity, and language preference—and see if, based on the experience you have with survey feedback or program approval if you see any differences across those groups?” Gianella said.
He added organizations shouldn’t worry if initial numbers seem to indicate adverse outcomes or trends. That should be an entry point into a more extensive investigation designed to benefit everyone – governments and the community.
“I think that should kind of be the expectation, Gianella said. “That it’s not adversarial in any way, then it becomes a conversation to get an understanding.”
2. Don’t let data be someone else’s job
The second point the two made underscored the need for data literacy across an organization. Too often, Davis said, those who work with data and those who make decisions about it are two different groups. This can create a gap in what leadership understands about an organization’s operations and the people it serves.
“In so many organizational cultures, there’s a pretty deep divide between folks who work with data and folks who don’t work with data,” Davis said. “What this means is there are only a few people in the organization who deeply understand what data you have and what can be done with it.”
Davis said that ideally, everyone has a basic understanding of data, including what types of data exist in the organization, how data is stored, managed, and how to collaborate with data science folks. This enables thoughtful data questions and responsive action.
“We would really love to advocate and see a culture shift where all staff feel connected to data, and they understand why it’s critical to their work,” Davis said.
3. Lead with curiosity and ask questions
The final piece of advice focused on asking questions that mattered, where staff connects the issues they care about with the data that influence those issues.
“When it comes to working with data, it’s really all about leading with curiosity and asking questions,” Davis said. “So really, at the end of the day, the purpose of the data that we collect and use is for us to get smarter about the work we’re doing, the impact we’re having, and all the ways we can improve.”