How to Navigate the Health Data Landscape

Data is pivotal to informing our understanding of the social, environmental, and economic dimensions of health systems. Ironically, in the age of info-glut, it can be difficult for organizations in the social sector to access useful data. Many nonprofits lack the research and development investments and staff with specialized expertise in data to navigate this increasingly complex landscape. However, strategically utilizing data is essential for the social sector’s sustainability and growth.

Fortunately, there are some resources and tools that provide rich information that nonprofits and other mission-driven organizations can use to advance health justice. Having the right data and analysis can create a strong evidence base for nonprofit organizations to demonstrate the value of their programs and services. By deepening our understanding of issues and their societal impact, data can also help advance our approach to long-standing, seemingly intractable problems.

While there is a growing call for trust-based philanthropic models that don’t stringently tie funding to unrealistic metrics or cumbersome data collection and reporting schemas, many grantmaking organizations continue to require nonprofits to demonstrate their impact using data. Data is also the language of policymaking and, consequently, integral to the development of evidence-based policy. Therefore, nonprofits pushing for legislation and regulation must be conversant in the relevant data. 

Publicly Available Data Resources

Publicly available health data sets capture a range of population-level information related to health and wellbeing, healthcare workforce data, international health data, and more. Some of these resources, along with examples, are described below:

• Global Health Data

Compiling global data, especially data based on surveys, can be a monumental undertaking because the survey questions are translated for each language group, and the surveyors have to coordinate efforts across the world despite serious challenges, including economic crises, disasters, and conflict. Some of the major contributors to this data are the World Health Organization’s World Health Survey Plus and World Mental Health Survey, and the US Agency for International Development’s Demographic and Health Surveys Program.     

• Federal Data Repositories

Data from the US Census Bureau, by its nature, is the most extensive data set on people in the United States. One of its most widely used surveys, the American Community Survey—which includes extensive survey data on social, economic, and other demographic information—can help nonprofits understand the changes taking place in their communities. The National Health Interview Survey, another survey conducted by the US Census Bureau in collaboration with the National Center for Health Statistics and the US Centers for Disease Control and Prevention, is also a valuable resource for health-related information. Similarly, the Health Resources and Services Administration offers data on the US healthcare workforce.

Since quantitative data inherently collapses diverse experiences into categories, utilizing qualitative insights can help nonprofits capture the varied experiences of socially and economically marginalized groups.

• Social and Attitudinal Data

The General Social Survey (GSS), created in 1972 by the National Opinion Research Center (NORC) at the University of Chicago, is one of the longest-running surveys in the United States. The GSS includes publicly available data on health, marriage and family, working conditions, and other data on social characteristics and attitudes. Additionally, NORC’s recently created General Social Media Archive allows users to access social media content, which can give researchers a sense of how opinions and attitudes of the public change over time.

• Other Public Data Dashboards and Tools

Health equity dashboard tools, such as Morehouse School of Medicine’s Satcher Health Leadership Institute’s Health Equity Tracker, Deloitte’s Health Equity Dashboard, Kaiser Family Foundation’s Abortion in the United States Dashboard, and the Child Opportunity Map from the Institute for Child, Youth and Family Policy at Brandeis University, are valuable resources for health justice researchers and practitioners. Data collaboratives, which involve public-private data exchanges; data sandboxes, which create a safe environment for testing and experimentation; and free digital data libraries like the Georgetown Outbreak Activity Library (GOAL), which provides response activities and case studies on outbreak preparedness, response, and recovery, also offer rich resources to the public.

Strategies for Building a Data Culture at Small Nonprofits

In addition to the resources above, there are techniques and tools that can help nonprofits and other social sector organizations strategically utilize data, even with limited resources.

Research should not be conducted in a vacuum. Instead, social sector organizations should seek partners…to share data and strategies for effective use.

Besides gathering and analyzing publicly available quantitative data, small nonprofits should also capitalize on opportunities to create their own unique data repositories. By regularly conducting interviews and/or surveying the people they serve, nonprofits can build a repository of data that speaks to the previously unmet needs their organization addresses and how their programs impact people’s lives. This approach allows nonprofits to combine quantitative data with qualitative insights. Since quantitative data inherently collapses diverse experiences into categories, utilizing qualitative insights can help nonprofits capture the varied experiences of socially and economically marginalized groups.

Small nonprofits should also consider using generative AI to support data compilation, aggregation, and analysis. While AI systems can perpetuate harm, these tools can also help compile, aggregate, and summarize data, as well as extend the capacity and capabilities of small teams. Generative AI can be used for rote and tedious tasks, leaving more time for nonprofit professionals to engage in other activities.

Research should not be conducted in a vacuum. Instead, social sector organizations should seek partners—such as peer organizations; academic institutions; local, regional, or state government agencies—to share data and strategies for effective use. Through collaboration, small nonprofits can conduct peer-to-peer learning on research and develop evidence ecosystems for their areas of focus.

To support nonprofits’ effective use of data, funders should support organizational data-capacity building efforts, such as creating data-related staff positions, purchasing software, and investing in professional development for staff to gain a deeper understanding of data and evaluation methods.

Funders should also continue to support multisector data collaboratives and equity-focused data tools like interactive dashboards, sandboxes, and digital libraries. These resources can serve as a springboard for developing a deeper understanding of health inequities and the solutions needed to address them.

Understanding What’s Behind the Data

Data collection and analysis should always be conducted from the understanding that data are never complete or objective. By understanding the limits of the data and the subjectivities built into the data collection and analysis process, nonprofits and other mission-driven organizations can lead meaningful conversations about public health, mental health and wellbeing, and the current state of healthcare systems in the United States and elsewhere.

Since data is inherently subjective and incomplete, several factors should be considered when utilizing data:

1. Watch for Critical Data Gaps

Since some communities are underserved, considered difficult to access by the research community, or otherwise marginalized, their absence in the data represent important gaps. Researchers should be aware of these gaps and how they affect the data’s utility. It is imperative to understand whose perspectives are and are not captured in the data because such disparities are pervasive.

2. Social Inequalities Can Result in Imbalanced Data

Researchers should always consider what the data represent and the processes used to compile the information. As Joan Mukogosi points out in Data & Society, numerical data that reflects clinical encounters, economic transactions, and other fraught social practices has resulted in racialized health data that dehumanizes marginalized people and relies on rigidly defined, socially constructed racial categories as a proxy for complex societal dynamics. Consequently, data should always be used reflectively, with an understanding of its subjective nature and the ways it can contribute to scientific racism.

3. Assess Data Quality

Not all data is equally credible, so the accuracy and reliability of information should be thoughtfully considered before it’s used. Publicly available data sources offer data release documents that discuss data collection, data cleaning, and processing procedures. They also typically describe each variable; this information can be used to assess the utility and appropriateness of a data set for specific research questions. Since many publicly available data sets are drawn from survey responses, the documentation also provides information on survey design and sampling procedures.

4. Beware of Bots and AI-Generated Content

Social media data and other online material could contain inauthentic information, disinformation, or other AI-generated content. While social media data can provide a glimpse into public sentiments, it is important to understand that this data may magnify derisive and hyperpolarized views.

Ongoing efforts—such as the Global Digital Compact, a 2024 United Nations agreement for an open, free and secure digital future for all—allow civil society and grassroots organizations to push for better public use of data and enhanced transnational collaboration on data. Additionally, policy measures under consideration in the United States and the United Kingdom’s AI Act could move both countries toward more responsible and ethical uses of big data.

Data is power. By utilizing the data we currently have while pushing governments and the commercial sector to make more high-quality data available to the public, data will continue to serve as an important tool in the fight for health justice.