Definition:
Disaggregated data refers to data that has been broken down into smaller sub-categories to reveal trends and insights that might be obscured in aggregated data sets. In simple terms, disaggregating data means separating it into more specific groups based on various attributes such as race, gender, age, income, geography, or other relevant factors. This practice is crucial in the context of Diversity, Equity, and Inclusion (DEI) efforts, as it allows organisations, policymakers, and researchers to better understand disparities and inequities that may exist within larger populations.
Importance:
In the pursuit of diversity, equity, and inclusion, disaggregated data plays a pivotal role. Without breaking down data into smaller subsets, organisations risk generalising trends that do not account for the unique experiences of marginalised groups. This can lead to one-size-fits-all solutions that may not effectively address the needs of specific populations.
For example, consider an educational institution looking at graduation rates. Aggregated data might show that the overall graduation rate is 85%, which seems positive. However, disaggregated data may reveal that while 90% of male students graduate, only 70% of female students or 60% of minority students do. Without this breakdown, the institution might miss critical insights into the barriers faced by specific groups, making it impossible to develop targeted strategies for improvement.
By using disaggregated data, organisations can:
- Spot hidden disparities: It helps uncover inequities within groups that might otherwise go unnoticed.
- Design targeted interventions: It enables more precise planning for initiatives that address the specific challenges of underrepresented groups.
- Measure progress effectively: It ensures that DEI goals are tracked across different demographic groups, fostering accountability.
Example:
A healthcare organisation that wants to improve access to mental health services might initially see in their aggregated data that 75% of patients are receiving adequate care. However, when the data is disaggregated by race and income, a different picture could emerge. The breakdown might reveal that:
- 85% of white patients receive care,
- 70% of Hispanic patients receive care,
- Only 50% of low-income Black patients receive care.
This kind of insight would highlight a significant equity gap that requires tailored interventions to ensure that mental health services are accessible to all, particularly those from disadvantaged or minority groups.
Conclusion:
Disaggregated data is a powerful tool in promoting diversity, equity, and inclusion. By breaking down data into more detailed subsets, organisations can identify and address hidden inequities, develop targeted interventions, and ensure that their DEI goals are met. In today’s data-driven world, disaggregating data is essential for driving meaningful, systemic change.
References:
Papaioannou, A. D., Nejabati, R., & Simeonidou, D. (2016, December). The benefits of a disaggregated data centre: A resource allocation approach. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-7). IEEE. https://ieeexplore.ieee.org/abstract/document/7842314/
Lin, R., Cheng, Y., De Andrade, M., Wosinska, L., & Chen, J. (2020). Disaggregated data centers: Challenges and trade-offs. IEEE Communications Magazine, 58(2), 20-26. https://ieeexplore.ieee.org/abstract/document/8999422
Abualghaib, O., Groce, N., Simeu, N., Carew, M. T., & Mont, D. (2019). Making visible the invisible: why disability-disaggregated data is vital to “leave no-one behind”. Sustainability, 11(11), 3091. https://www.mdpi.com/2071-1050/11/11/3091
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