Disaggregated data in the big data era
Disaggregated data may be a tongue twister, but first and foremost it’s the important starting point to getting an inside look at the experiences of racialized and marginalized groups. It’s about being specific when collecting data and creating categories that account for the individualized experiences of those groups. Atong Ater, core member of the Federal Black Employee Caucus (FBEC) talked to me about why having this data is a necessary start to achieving racial equity; why the “visible minority” category is problematic; and why factoring in who is interpreting the data, once it’s collected, matters.
Why disaggregated data is important
“We get caught up in talking about racism at the individual level, but the data allows us to explore the systemic nature and impact of it.”
Living in the big data era means that much of public policy decision making comes down to the data that is collected. Making informed decisions that account for the best interests of different groups, requires an accurate representation of what is going on. Atong provides an example of the valuable information disaggregated data can provide, “if two employees come in at the same level, how does the Black employee compare to the non-Black employee in terms of their career progression? How long do they stay in a certain position, and is retention an issue?” Atong explains that disaggregated data reveals systemic racism, “we get caught up in talking about racism at the individual level, but the data allows us to explore the systemic nature and impact of it.” At the same time, she says it’s important to consider that the collection of this data may not be as simple as it appears; groups who have a history of being marginalized or mistreated in the workforce may be reluctant to self-identify. To mitigate this, it’s essential to demonstrate commitment to using the data towards making a difference.
Why the “visible minority” category is problematic
“It has a silencing effect, it tells you that the data doesn’t support your experience.”
Atong emphasizes that the “visible minority” category is problematic on many fronts, and part of the work of FBEC is pushing to have it broken down into more specific categories. “There’s a visible minority masking that happens when you keep everything together,” she says, “it’s possible that one sub-group may experience particularly positive movement and growth in terms of career progression, but that may not be the case for other sub-groups.” Different racialized groups have different experiences, and the data should reflect that. Otherwise, as Atong puts it, “it has a silencing effect, it tells you that the data doesn’t support your experience.”
Why it’s important to factor in who is interpreting the data
“The point is to have varying views around the table so that interpretation is provided from different lenses.”
The data collection is one thing, but Atong highlights that the people who are analyzing the data are just as important. With everyone’s individualized experiences in play, the interpretation of the data and the story it’s telling could be very different. “The point is to have varying views around the table so that interpretation is provided from different lenses,” Atong says, “representation at all levels is crucial.”
“The data is the initial part of the conversation. The data in itself is not justice. What it tells you is what is going on. What you do with it to address the inequities, that’s the conversation we are trying to have with people.”
The collection of disaggregated data is a step forward, but it’s only the beginning. Atong shares a final, but critical reminder, “the data is the initial part of the conversation. The data in itself is not justice. What it tells you is what is going on. What you do with it to address the inequities, that’s the conversation we are trying to have with people.”
The Treasury Board of Canada Secretariat has taken the initial step by releasing disaggregated datasets, a further breakdown of the representation across the Government of Canada (GC) within the groups of indigenous peoples, persons with disabilities, and members of visible minorities to account for data specific to sub-groups. The continued exploration of these detailed datasets will be instrumental to achieving a better understanding of the composition of the public service, and to further inform the concrete actions necessary to address the gaps and eliminate the barriers within the GC. On , the GC announced its priorities for action to increase diversity and inclusion in the public service.
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