Data Equity

Data equity recognizes the importance of considering issues of power, bias, and discrimination in data collection, analysis, and interpretation. This includes seeking community ownership of data, applying practices that ensure the protection of people and communities, recognizing and seeking to correct legacies of discrimination through data, recognizing the dangers of a single story, and weaving data and storytelling to tell a more complete picture. Ultimately, building data equity means using data to build community power. It emphasizes the need to address systemic inequities and power imbalances. In public health, data equity is complex and evolving and can mean different things in different contexts. Collecting health data on marginalized communities can be important for addressing health disparities, and it can also perpetuate biases and harm. Data equity requires balancing the potential benefits and risks of different data practices in specific contexts.

Data equity involves ensuring the benefits and risks associated with data use are distributed justly, which can help prevent biases and inequalities, and lead to better health outcomes for everyone. Data equity is important for identifying and addressing health disparities and social determinants of health in a fair and just manner. 

By improving data equity, we can improve our ability to improve health outcomes and minimize disparities, and empower communities through data-sharing, ownership, and leverage. Data are powerful and legitimize efforts towards justice. Inequitable use of data can lead to inaccurate reporting and poor decision-making, unintended consequences and a loss of trust. Data equity improves well-being, reduces disparities, and promotes equity in education, resources, and opportunities.

Throughout history, data have been used to perpetuate inequality and discrimination. The civil rights movement highlighted how census data were used to enforce discriminatory policies and practices, like redlining. With recent technological advances in machine learning, artificial intelligence, and other fields, new opportunities for data analysis have emerged. However, there are concerns that these advancements may lead to biased and discriminatory use of data. To promote fairness and justice, it is crucial that we recognize issues of data privacy and prevent data from being weaponized against communities. Policies such as HIPAA and other ethical standards can help ensure that data is used in an ethical manner.

Data broken out by sub-groups (i.e. race and ethnicity) are not always available and reliable, often due to low population counts, inconsistent definitions, telecommunications, limited access to healthcare, and/or stigma and discrimination. Historically, groups that have been marginalized due to systemic inequalities, such as people of color, women, LGBTQ+ individuals, and low-income and rural communities, have been disproportionately impacted by data inequities. These groups are also more likely to have limited access to high-speed internet, digital devices, or the resources needed to navigate and make informed decisions about data collection and use.

While certainly more emphasis has been put on data equity in recent years, the status today is complex and varies across contexts and regions. Data equity issues intersect with other forms of systemic inequality, such as racism, sexism, ableism, and homophobia. To go beyond meaningful participation of marginalized communities in data collection and use policies, we need to shift towards community-driven and participatory approaches. This includes building trust, co-designing policies, ensuring data ownership, and providing resources and support. 

Data equity matters at a system level because it is essential for promoting social and economic equity, democratic governance, and human rights. Advancing equitable use of data requires a fundamental shift in the systems that collect, use, and share data. Changemakers and stewards can promote data equity at scale through education, advocacy, and policy changes. This includes advocating for privacy protections, data transparency requirements, and mechanisms to hold data collectors accountable for discrimination. They can also promote the adoption of ethical standards and strategies to turn data into power. Additionally, policy advocacy can make more data available for priority populations by disaggregating data. Promoting data literacy and digital skills builds the capacity of communities to use data-for-power.

Lower-hanging fruit opportunities to promote data equity include consistently collecting demographic data using clear, specific categories, including allowing individuals to self-identify. Ensure privacy by collecting data securely and minimize personally-identifiable information. Use trusted messengers and technology to collect data and provide clear information about the purpose of data collection. It’s also beneficial to build local community capacity to navigate and engage with data collection, use, and interpretation, and to support individuals and communities to take control of their data. 

Resources & Tools

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Principles for Using Public Health Data To Drive Equity
Resource - Report
Brought to you by CDC Foundation
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The Data Equity Framework
Brought to you by We All Count
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Strategies for Including Deaf and Hard of Hearing Participants in Research
Story - Written
Brought to you by Urban Institute
Published on 11/23/2021
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Seattle Open Data
Tool - Data/mapping Tool
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Racial Equity Data Hub
Tool - Data/mapping Tool

 Related Topics

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Health Equity