Data Disaggregation Action Network

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What is data disaggregation?

Disaggregation means breaking down large data categories into more specific subcategories. “Data disaggregation” refers to the collection, reporting, and analysis of information on specific subgroups by race, ethnicity, and other characteristics. When data are disaggregated, researchers are better able to analyze differences between groups, as well as the relationship between multiple variables, such as race and age. 

Why do we need disaggregated data?

Aggregated data hide inequities. In many existing state and federal data collection systems, data are not sufficiently disaggregated by race and ethnicity (including subgroups and first language), sex (including sexual orientation and gender identity), disability, age, income, and other characteristics like geographic area or social determinants of health. This masks the nuanced realities of many communities behind larger trends, and makes it more difficult to address inequities. As just one example, aggregate data may perpetuate the model minority myth — that all Asian Americans have high levels of income, homeownership, education, and health — but disaggregated data reveal large disparities among AANHPI groups. For instance, the percentage of Asian Americans living below the poverty line ranges from 6.8 percent of Filipino Americans to 39.4 percent of Burmese Americans.

What are we doing about it?

The Education Fund and our partners in the Data Disaggregation Action Network are working to advance federal and state policies as they relate to data disaggregation by race and ethnicity through the engagement and empowerment of communities. Through the creation of a state and national advocacy infrastructure to engage stakeholders, policymakers, and communities on the need for disaggregated data, D-DAN works to improve data quality and accessibility in order to better understand disparities and achieve racial equity.

D-DAN partner spotlight:

Logo: Center for Health Innovation

Center for Health Innovation: Paving the way for a more equitable and representative future for all New Mexicans

The Community Data Forum series is a vital part of the Center for Health Innovation’s (CHI) Data Disaggregation Action Network (D-DAN) project, which aims to advance data equity and accurate representation of diverse communities in New Mexico. These virtual panel discussions will delve into critical data disaggregation-related issues surrounding topics such as sexual orientation and gender identity (SOGI) legislation in New Mexico, data privacy, data collection processes within the state, among other relevant issues.

The first Community Data Forum, held on February 22, 2024, highlighted the amazing work of CHI’s first Data Disaggregation Community Workgroup (DDCWG). This multidisciplinary group of folks working with and for various New Mexico Community Based Organizations (CBOs), worked diligently to develop a list of community-informed recommendations for improving the collection and reporting of demographic data in New Mexico.

Upcoming forums will explore various aspects of data equity, including the importance of data privacy safeguards, the ethical considerations surrounding data collection, and the potential implications of inaccurate or incomplete data on smaller, underrepresented communities. These discussions will bring together experts, community advocates, and concerned citizens to share their perspectives and insights.

By fostering open dialogue and raising awareness about these crucial issues, the Community Data Forum series aims to drive meaningful change and ensure that New Mexico’s data collection and reporting practices are inclusive, accurate, and reflective of the state’s rich diversity. Through collaborative efforts and a commitment to data equity, CHI’s D-DAN project is paving the way for a more equitable and representative future for all New Mexicans.


Learn more about our D-DAN partners here!

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Latest News

NEW LEGISLATION

New Jersey: A-3092wGR/S2415, signed by Governor Murphy along with two other immigrants’ rights bills on January 12, 2024, will require State agencies to disaggregate data for Asian American, Native Hawaiian, and Pacific Islander (AANHPI), Middle Eastern and North African (MENA), and South Asian and Indian Diaspora communities. The bill was led by members of New Jersey’s AANJPI Legislative Caucus, including Senator Vin Gopal and Assembly Member Sterley Stanley. New Jersey regularly outranks other states in its share of AANHPI and MENA populations, and thanks to the leadership of community organizations and sponsors, the bill had broad bipartisan appeal within the Legislature. The Legislature concurred with the Governor’s conditional veto to afford State agencies the flexibility to comply with any distinct data collection and reporting practices required by federal laws, regulations, programs, or surveys, and to account for evolving standards and guidance that may be issued by the federal Office of Management and Budget or the United States Census Bureau in the future. In their final push to get this legislation passed, New Jersey Alliance for Immigrant Justice and its members leveraged findings from The Education Fund’s Disaggregation Nation report to demonstrate that many states disaggregate health data without the concerns raised in opposition to the New Jersey bill, and used the report to help navigate discussions with the Governor’s office on the need for data equity to address education and health disparities.

  • Governor’s office press release
  • Background and statements on the bills from New Jersey Alliance for Immigrant Justice and other advocates who worked to pass them

Resources »

Blog Series

Our Data Disaggregation Deconstructed blog series explores how data disaggregation in various policy areas can enhance equity. The series is based on The Leadership Conference Education Fund’s report “Information Nation: The Need for Improved Federal Civil Rights Data Collection,” which urges the Biden administration to restore and expand the scope, frequency, and public accessibility of federal data collections in order to identify equity gaps and solutions to remedy them, as well as our follow-up report “Data for Equity: A Review of Federal Agency Equity Action Plans”, which makes eight recommendations about how federal agencies can improve the collection, analysis, and sharing of data.


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