Data Equity: What Is It, and Why Does It Matter?
by Joyce Lee-Ibarra, JLI Consulting LLC
This post is the first in a series regarding data equity. We hope it provides initial understanding and context regarding the concept of data equity broadly. Subsequent posts will consider what data equity might look like locally, and how it can support better decision making and advance local communities’ well-being.
In recent weeks, racial justice protests have sparked new conversations regarding race, racism, and social justice across nearly every aspect of American society. From housing to health care access, from employment opportunities to wealth accumulation, from education to policing practices, issues and institutions are being re-examined through the lens of anti-racism and equity. Data and the systems that create and rely upon data have been no exception.
What Does “Data Equity” Mean?
The term “data equity” captures a complex and multi-faceted set of ideas. It refers to the consideration, through an equity lens, of the ways in which data is collected, analyzed, interpreted, and distributed. It underscores marginalized communities’ unequal opportunities to access data and, at times, their harm from data’s misuse. It raises the issue of data sovereignty, and the democratization of data. And data equity pushes us to consider the ways that data can reinforce stereotypes, exacerbate problems like racial bias, or otherwise undermine social justice.
“Data justice,” a term that at times is used interchangeably or in close relation to “data equity,” has been tied to the ethics of personal data privacy, big data, and decision making that results from the “datafication” of modern society. But it is also used to encompass the complex meanings that data equity captures, including concerns regarding power and privilege, knowledge equity, and the ways that harmful decision making may be justified or maintained through data.
What Are Some Key Ideas Behind the Data Equity Concept?
Data is not objective. While numbers and figures may be neutral in and of themselves, they don’t exist in a vacuum. Data is collected, analyzed, interpreted, and distributed by people, who bring to their work their subjective experiences and potential biases. The goals or motivations we have in our data work, as well as the questions we ask and how they are framed, are likewise informed by our perspectives, even unintentionally. Various forms of interpretation bias, for example, can color our understanding of data, leading us to selectively value or dismiss certain outcomes and explanations over others.
Data can create and perpetuate power dynamics. Knowledge, as the saying goes, is power. Data, as a form of knowledge, can create power imbalances, which become more visible when we reflect on questions such as:
Why are we seeking to collect data?
Who is empowered to collect data?
What is the dynamic between question asker vs the question answerer?
Who will be the end user of the data?
Who and what will the data be in service to?
Who are the “experts” in the data project?
While framed in terms of research, the points raised by the authors of Why Am I Always Being Researched? certainly apply equally to the collection and generation of data from people and communities:
“Right or wrong, research can drive decisions. If we do not address the power dynamics in the creation of research, at best, we are driving decision-making from partial truths. At worst, we are generating inaccurate information that ultimately does more harm than good in our communities. This is why we must care about how research is created.”
Equity needs to be addressed throughout the data life cycle. The data life cycle refers to the various stages of a data project that offer opportunities to practice greater consciousness of, and commitment to, equity, fairness, and access. We All Count breaks down the data life cycle into seven stages—from funding through the communication and distribution of results—and describes potential inequity pitfalls in each. Urban Institute offers principle-aligned practices for the data life cycle, which is described as occurring in four stages—each of which can be guided by three principles from human subjects’ protections: beneficence, respect for persons, and justice.
How might data equity be relevant to people living and working in Hawaii? And how can the practice of data equity ultimately lead to better, more informed decision making? We’ll be exploring these and other questions in future posts… stay tuned!
Joyce Lee-Ibarra is principal of JLI Consulting LLC and a consultant to the Hawaii Data Collaborative.
