This is how I sometimes understand the current state of data collection and analysis that may occur in large institutions who are required to collect data to share a certain narrative which continues to build inequity in society, the education system and workforce.
Is the way Statistics Canada collects race or gender-based data an ethical process that can truly paint an accurate picture of impacts on diverse populations? This is a question I ask often when I examine the pretty infographics that the department will share. The complexity of a community is summarized on one page. Is this a story that truly resonates with the lived experiences of the population being showcased?
Colonial cultural norms that impact equity seeking communities include dehumanization, mistrust and extraction. When such conditions have been experienced over generations it is important to recognize community hesitation and trauma in sharing knowledge with institutions that have traditionally held power over society.
Advancing Equity in Data Presentation
In her presentation Meena Das shared many important frames on how to make data equitable. She identified multiple ways that people can address bias in data collection. Setting the context for equitable data collection and narration is to recognize that to design good data it is essential to make sure that those most impacted by the data are centered in the process and gathering of the data. One of the frames that resonated is the consideration of linking public good and human centricity to build conditions that create trust, respect privacy and honour humanity.
Das provided a definition for IDEA (inclusion diversity equity and accessibility)- led data as “the consideration through an equity lens, of the way in which data is collected, analyzed, interpreted and distributed. “ She shared that data equity needs to have four components of TRUTH embedded within the intersecting frameworks of social justice, intentional design and data inquiry.
The four components of truth shared are:
- When I see myself in the data.
- When analysis in any shape or form of data does not lead to my tokenism
- When I see my ways of generosity acknowledged, and
- When I am considered in the question, the answer and the decisions from the research.
Another great takeaway shared in the presentation was reasons for collecting good data. The following points recognize the parameters and impact of good data collection.
- Honors and centers the community.
- Serves as a systematic way to measure and prove impact (social and economic)
- Enables informed data driven decisions to better serve the audience.
- Presents evidence for results to funders and sponsors when asking for donations.
Overall data collection supports accuracy, completeness, consistency and timeline.
Considering these truths in data design provide validation and community engagement to have ownership over the narrative that will be created. Social, economic and cultural impacts documented can provide an accurate snapshot of what challenges and successes equity seeking groups face in society. As a result the stakeholders and funders invested in building inclusive spaces can address the challenges presented.
The world is increasingly complex and post covid systemic inequities have been exposed. Recognition of these inequities is causing a disruption in the ways dominant cultural norms have been rooted and followed for so long.
What are the emergent data narratives that arise out of the disruption that can spotlight pathways to equity, inclusion and belonging? In the inquiry about developing practices to document intersectional identity data without being intrusive and harmful Das’ presentation was a great opportunity to understand the application of equity lenses in data collection and narration that state assumptions, center community care and recognize the value of an ethical process.