Catalogue number: 892000062024001
Release date: July 16, 2024
This short video explains how it can be very effective for all levels of governments and organizations that serve communities to use disaggregated data to make evidence-informed public policy decisions. By using disaggregated data, policymakers are able to design more appropriate and effective policies that meet the needs of each diverse and unique Canadian.
- Data journey step
- Foundation
- Data competency
-
- Metadata Creation and Use
- Audience
- Basic
- Suggested prerequisites
- N/A
- Length
- 03:37
- Cost
- Free
Watch the video
The Importance of Disaggregated Data: An Introduction (part 1) - Transcript
Statistics 101: Exploring measures of central tendency - Transcript
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "The Importance of Disaggregated Data: An Introduction (part 1)".)
(Text on screen: Meet Samir)
Samir is a city planner. He is responsible for helping to improve public transportation in the growing municipality of Greendale. One day, a report lands on his desk. It reads: "80% of the city's residents are satisfied with the current public transportation system." That's great news, right?
(Text on screen: Map of Greendale. The map divides in three visual categories: Northern, Central and Southern. A fourth visual appears titled: Senior citizens. The Senior citizens are dispersed on the map of Greendale.)
But what if Samir was able to tap into resources that allow him to read beyond that headline? Through his knowledge and understanding of how to access the data that the report was based on, Samiri is able to break down (or disaggregate) the data further. He discovers that:
- Only 60% in the city's northern district are satisfied, and they often complain about irregular bus services.
- The central district, where many office workers live, has an over-saturation of buses during off-peak hours but a shortage during rush hours.
- The newest southern district, with its recent infrastructure developments, enjoy a 95% satisfaction rate.
- Senior citizens, who represent a sizable percentage of the city's population, report a satisfaction rate of only 50%, noting a lack of accessible options for those with mobility issues.
(A bar chart with the following title: Satisfaction of the population of Greendale with the public transportation system. The bar chart has percentage of satisfaction rate on the vertical axis and four categories on the horizontal axis: Northern population (at 60% of satisfaction), Central population (at 70% of satisfaction), Southern population (at 95% of satisfaction) and Senior citizens (at 50% of satisfaction). The Overall satisfaction rate is 80%.)
If Samir acted solely on the initial 80% satisfaction data, he might conclude that the public transportation system only needs minor tweaks. Meaning he would be contributing to the continuation of under-served and dissatisfied demographics, not to mention the wasting of resources by having too many buses during off-peak times.
But by breaking down or disaggregating the satisfaction data by geography and age groups, he is able to:
- Reassess and increase the frequency of buses in the northern district.
- Adjust bus schedules in the central district to align better with the office rush hours.
- And create a new program to enhance accessibility for senior citizens.
For Samir, the importance of knowing how and where to access disaggregated data to help understand the diverse needs of different communities and areas, play an integral part in his ability to make decisions that ensure every resident gets the quality of the service they deserve.
(Text on screen appears one after the other: Gender, Age, Ethnocultural identity, Indigenous identity, Geography, And many more!)
Similarly, it can be very effective for all levels of governments and organizations that serve communities to use disaggregated data to make evidence-informed public policy decisions. By using disaggregated data, policymakers are able to design more appropriate and effective policies that meet the needs of each diverse and unique Canadian. As much as possible, the data should be disaggregated by gender, age, techno, cultural identity, indigenous identity, different geographies and any other community relevant identity factors, and presented distinctly for each specific subgroup.
(The Canada Wordmark appears.)
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