Data is often at the center of transformative policy change. Disaggregated data is one of the key strategies different government agencies within Canada are pursuing to better understand marginalized communities and deliver services to them. An example of this is Statistics Canada’s implementation of its Disaggregated Data Action Plan.

What is disaggregated data?

Disaggregated data is the breakdown of smaller units of data from a larger aggregated data set, with individual points in the set compiled for the aggregate picture. Disaggregated data can provide insight into populations and provide a clearer picture for policy making processes.

While aggregated data will group information together to provide a broad picture, disaggregated data has the potential to uncover systemic inequalities and relationships that exist between categories.

Different perspectives on disaggregated data

Proponents of disaggregated data collection by the government argue that public agencies that provide essential services have historically excluded certain groups. In this view, more disaggregated data can expand the accessibility of public services and emergency supports, as seen with the proliferation of various mutual aid initiatives during the COVID-19 pandemic. In BC, for example, some community groups called for careful use of disaggregated data in response to the pandemic. Check out the call for disaggregated data from Black in BC Mutual Aid here. The motion included support from Black in BC Mutual Aid, Tulayan – Bridging the Filipino Diaspora, and the Hogan’s Alley Society. This generated conversations around disparities regarding who is included and excluded from public services.

This conversation is not unique to BC. At the World Bank, there is strong support for sex-disaggregated data, which analysts see as the key to understanding and addressing uneven economic recovery post-pandemic.

In Edmonton, the Social Planning Council recognizes that race-based data collection comes with great opportunity to remedy inequities, but can be equally harmful to participants. As such, the Social Planning Council recommends that race-based data collection efforts are centered within anti-racist frameworks so this approach to data collection can be used as a mechanism for social justice.

Historically, race-based data pertaining to Indigenous peoples have been misused to harm Indigenous individuals and communities. The history of oversurveillance and monitoring of Indigenous leaders, communities, and activities, along with a lack of transparency within public agencies regarding ownership and access to data has entrenched distrust towards government and public agencies. The First Nations principles of OCAP® outlines this issue and addresses best practices to follow when working with First Nations data such as conducting threat risk and privacy impact assessments, as well as establishing legally-binding agreements such as data sharing agreements that clarify each party’s authority to share and to receive data. Additionally, there are calls that the collection of race-based data can be used as a tool for reconciliation – but only if these efforts are guided by Indigenous leaders.

One of the key things this contrast helps us see is that there is diversity within marginalized communities with different lived and historical experiences that contribute to distrust of government and security agencies more broadly. This diversity in experiences leads to different conclusions over whether there is need for more disaggregated data collection by government agencies. Therefore, it will be critical to take a few things into account before pursuing disaggregated data collection.

The way forward

One thing that is clear from these varied perspectives is that communities need to be centered in considerations over whether to have disaggregated data, what kinds of disaggregation are acceptable, and how disaggregated data is collected. Based on the perspectives raised above, the following are key principles if disaggregated data is to be pursued:

  • Engaging in holistic processes that center the needs of communities.
  • Consider a community’s positionality and risk towards harm
  • Defining the purpose and scope of data collection with the community
  • Explore avenues alongside communities that enable easy access to their own data
  • Incorporate opportunities for communities to consent to sharing specific forms of personal information

There is no straightforward answer, but a best practice is for the government to engage in community-specific conversations, given that different communities have different historical concerns. Increasing the collection of disaggregated data asks communities with previous experiences of harm to entrust more data to government agencies. Without efforts to build trust with these communities, government agencies may potentially reinforce existing patterns of inequities, rather than reducing them.

{"email":"Email address invalid","url":"Website address invalid","required":"Required field missing"}

About the author 

Multiple Authors

This article was written by Melissa Hollobon, David Markwei, Claire Okatch, and Savannah Tuck.

You may also like:

How to Become a Data Science Freelancer

George Firican


Data Governance in 2024

Data Governance in 2024
5 Steps to Achieve Proactive Data Observability – Explained Over Beers