In a world of increasingly divisive conversations about some of society’s biggest challenges, data can be a touchstone that creates common ground. Data that is carefully scrutinized for accuracy and analysed to minimize bias is often a place where people on opposing sides of a topic can say “okay, I accept that this information is true. What next?”
In many ways. the COVID-19 pandemic helped solidify our understanding of using data to guide public policy. Data dashboards flourished, both providing concern and easing fears, depending on the trend line. People without a statistics background began to read data more fully, looking not just for the big picture, but wanting to know how to act based on that data.
Similarly, the Happiness & Wellbeing Community Lab combines the use of quantitative research (i.e. larger quantities of data) with qualitative data (e.g. interviews with people who have experience of the topic at hand), mixes in some expert analysis, and uses the results to support conversations and to guide decision making.
Historically, data has been used to justify the marginalization and disenfranchisement of populations outside the seats of power. In particular, in Canada race data was used to support government programs that decimated Indigenous nations and peoples including the Indian Act and residential school system. The Lab works with First Nations and Indigenous organizations to work towards decolonizing data practices
The misuse of data is a concern. Numbers can be manipulated or chosen expressly to support a pre-existing idea. At the Lab, the use of external experts helps reduce that bias. The use of large, random sample data sets (e.g. Statistics Canada’s census releases) also helps to reduce manipulation.
People are multifaceted like jewels; a person’s age, race, upbringing, gender (assigned at birth, expression, and identity), income, the communities they are a part of or excluded from, their language, how long they’ve been in Canada … everything that makes a person an individual also informs how closely a data analysis fits that person. Gender is one facet. Race another. Income a third. The more facets that can be analyzed, the more people the data will fit. Intersectionality attempts to include as many facets of an individual as possible within the available data. Whenever data is available, the Community Happiness and Wellbeing Lab uses an intersectional approach.
Confronting Racism with Data: Why Canada Needs Disaggregated Race-based Data. Edmonton Social Planning Council. February 2021.
“Why Data Matters: the purpose and value of analytics-led decisions.” Martyn Etherington for Forbes.com. October 2020.