Data is an essential tool that can be used to highlight inequities, shine a spotlight on best practices, and empower communities to be their own advocates. But when there is a breakdown in trust, the power of data is restricted. Policymakers, researchers, and advocates can break trust with their audiences through their actions, ranging from honest mistakes with good intentions to deliberate deception. Rebuilding trust does not happen by mistake; it requires intentionality. Data communicators must actively learn about their audiences and take steps to build trust with their communities.
Various sources provide data to communities, including government officials, researchers, journalists, and advocates. These data communicators have a responsibility to use data in a way that engenders trust with their audiences. But trust building is not possible unless data communicators are willing to check their intentions and dedicate themselves to learning alongside communities, practitioners, and other stakeholders—collaboratively using data to better understand and evaluate systems.
Alongside a commitment to building trust with humility and collaboration, DQC has identified three sources of mistrust that can help data communicators better understand their audiences and prioritize building trust. There is no simple three-step process, but this post provides an explanation of the common ways that data communicators can build trust as well as real-life examples of these concepts.
Audiences are more likely to trust communicators they know or can identify with. In contrast, they often distrust information from faraway sources. This concept can be seen in DQC’s 2021 national poll, in which parents said they most often turn to teachers or other leaders in their child’s school for information about college and career options for their child—trusting these closer sources of information more than resources provided by the state (e.g., state report cards).
To bridge this proximity gap, data communicators should consider who is most equipped to communicate with their audience. One strategy is to build relationships with trusted community leaders who can act as a link, communicating data findings to communities and empowering communities to engage in the data collection and analysis process. These leaders can have many different roles, from principals and religious leaders to representatives from community organizations. For example, the Housing Opportunity and Services Together (HOST) project from the Urban Institute—an effort to improve the lives of youth and adults who face the highest obstacles to success—worked with community leaders to help facilitate data walks, which are interactive events during which participants walk through data displays and reflect on the findings in small groups.
Audiences will dismiss data that does not jive with their experience or the experiences of their communities. Sometimes, this disconnect can be fixed by clarifying definitions, timeframes, or methodology. For example, the US Department of Housing and Urban Development (HUD) claimed that homelessness among youth is declining, while the US Department of Education (ED) stated that homelessness among students is increasing. Audiences are likely to trust one source or the other based on their previous experiences—or simply discount both altogether. However, these two agencies include different types of housing insecurity in their measures because their goals are different. Both HUD and ED make their definitions available, but unless data communicators include this context prominently and inextricably in charts, headings, and public-facing materials, it is likely to get lost and leave audiences unsure what to believe.
People don’t trust those who do not value their community, especially when it comes to their children. Data communicators break trust when they ignore the skills, aspirations, and assets of students, families and communities. By focusing only on struggles that communities face, data communicators fail to point out the incredible potential for success that communities possess, while also ignoring the responsibility of systems and decisionmakers to prevent and alleviate barriers. Framing in a way that centers the assets of a community and recognizes the source of struggles is called asset framing.
Here’s an example:
- Deficit frame: “Students of color are less likely than their peers to participate in advanced coursework.” In that statement, the role of schools in providing access to advanced coursework and preparing students for success in those classes is ignored. How can students enroll in courses their schools don’t offer or allow them to enroll in? Or succeed in courses when their schools routinely underprepare them?
- Asset frame: “Students of color aspire to go to college, but schools often exclude them from advanced coursework through a variety of policy decisions, including fewer course offerings in their schools, restrictive enrollment policies, and under-preparation in earlier coursework.” This phrasing begins with the goals and aspirations of students. It also recognizes that the underrepresentation of students of color in advanced coursework is not inevitable; this reality is the result of the current policy landscape, which education leaders can be held accountable for addressing.
Data communicators have the ability to empower, illuminate, and drive change, but they can also mislead, confuse, and distract from the root problem. Therefore, they have a responsibility to put in the work to build two-way relationships with communities, be transparent about context, and present findings with an asset frame. Building trust takes time and intentionality—but without it, the power of data is diminished.