How can frontline workers tell if their referrals are successful? How can managers understand the paths young people follow through services? How can directors lead their organizations in shifting towards prevention by identifying at-risk youth sooner? The answer to all of these questions: by sharing and linking data.
In January 2026, as part of the Prevention Matters series, Stephen Gaetz of the Canadian Observatory on Homelessness facilitated a discussion among three researchers from PolicyWise for Children and Families to talk about their projects in Alberta and BC (funded by Making the Shift, and in part by the Government of Canada and the Government of British Columbia). These projects showed the powerful impact of data linking and sharing in the lives of young people experiencing homelessness.
Matthew Russell had access to a massive collection of linked administrative data held by the BC government and set out to identify the factors that predict whether a youth aged 13–18 is likely to go on to experience homelessness when they are 19–27. Caillie Pritchard and Kiran Gurm worked with Trellis and the Alex in Calgary to build relationships that could support a data-sharing agreement to better serve youth. The panel discussion explored each of these projects and the lessons they hold for how building data infrastructure can prevent homelessness.
Watch the whole Prevention Matters discussion on building data infrastructure.
Data Is Only as Powerful as the Relationships Among Stakeholders
All three panellists made clear that to build data collaborations, you need to build strong relationships and trust among the people who use the data, those who collect it, and those whose data it is.
Without that trust, data quality is compromised. “When I think about data, it’s about people,” Matt explained. “It’s about relationships, it’s about stakeholders. And when we talk about technology, we can have the fanciest system, … but if the relationships and the trust haven’t been built for that, what’s coming into it is not going to be very good.”
Kiran emphasized that although great moments of service coordination and integration are happening in the homelessness sector, they are informal and simply rely on individual relationships. This means they are not scalable and are at risk of breaking down if staff members leave. Clear structures and pathways are needed to build on relationships and provide organizations and youth with greater consistency. This starts before even talking about data—it requires a solid foundation based on conversations between organizations about opportunities to better support youth.
Different organizations with different processes can’t just be forced together, Kiran said, so a data partnership requires a phased approach. In her work with Trellis and the Alex, this started with staff visiting each other’s facilities, holding joint education sessions, and informing each other about their existing services and processes. From there, opportunities to coordinate between programs naturally emerged, and formalizing these opportunities ensures clients get more supportive care. As Caillie explained, “closing the loop” by maintaining communication between providers will help catch if youth did not follow a referral or if a referral was not a good fit.
Sharing Data Enables Homelessness Prevention
This experience in Calgary was a small example involving two organizations, but Matt presented a much larger homelessness data collaboration in London, England: CHAIN (the Combined Homelessness and Information Network). Focused on unsheltered homelessness, this ambitious project seeks to break down information silos between London’s different boroughs. In line with the need for trust, CHAIN has a very strong vetting process for who can access the data, so service users can be confident their data will only be used to help them and not to, for instance, criminalize them.
Counterintuitively, CHAIN allowed organizations to see that any time they introduced a lot of new services, the rate of homelessness would appear to increase. But, Matt pointed out, it is not that more people were actually homeless; it was that more services were reaching more people, responding to their unique needs. The data picture was actually becoming more complete. Only once enough people were being included in the data would the effect of the services become visible and the numbers start to go down.
“It’s really hard to get to that point here in Canada,” Matt said, “because there’s no one who would say that we’re serving everyone who is homeless.”
Identifying Homelessness Risk Factors Among Younger Youth
When Matt set out to identify risk factors for homelessness among youth aged 13 to 18 in BC, he had access to a powerful tool: the BC government’s Data Innovation Program. This program receives all the administrative data collected by service organizations across the province (shelters, income supports, education supports, child welfare services…), stores it securely over the long term, anonymizes it, and then links individuals between services. This allows researchers to look at the system use patterns of a given individual.
What factors predict whether a young person aged 13 to 18 will go on to experience homelessness between the ages of 19 and 27? Looking at patterns of service use allowed Matt and his colleagues to see what the outcomes were for people with different kinds of systems involvement. This incredible perspective was only made possible by linked administrative data.
Proving the Link Between Youth Service Involvement and Homelessness
The findings from Matt’s BC research were striking. About 1% of youth in the database appeared as visibly homeless, which means they either stayed in a shelter or disclosed being homeless to a worker. (Matt notes that this is an undercount, since they didn’t have data for all years, and so he estimates that about 2% of youth would be a better figure; he argued that we can’t wait for perfect data and just need to do our best with what we have.) The findings were striking. Among those youth who experienced homelessness as adults (aged 19–27):
- 95% were on income support at some point
- 65% had not received a high school diploma by age 27
- 64% accessed the health care system for substance use
- 16% were incarcerated, and a higher percentage had justice involvement
- 4% died
All of these numbers are much higher than for youth who have not experienced homelessness, and they show the high cost of failing to prevent homelessness. As Matt explained: “88% had three or more of those concurrent outcomes, and 61% had four or more.”
In turn, being involved in more systems was linked to higher risk of homelessness. Matt showed that 2% of children involved in one system went on to experience homelessness, compared to 25% of those involved in six systems. The leading risk factor was involvement in the child welfare system, with 37% of homeless youth having been involved in that system. It was followed by income assistance and mental health supports, notably around substance use or schizophrenia.
These findings may not be a surprise to those who work in the field, but access to linked administrative data means that it can now be proven, allowing organizations to make the case to governments and funders for early intervention and prevention.
Shifting Towards Homelessness Data Collaboration
We can make the case for linking administrative data and data sharing between service organizations, but how can we shift systems towards this in practice? Caillie pointed out that data sharing represents additional work for organizations that are already called upon to serve more people each year without more resources. This means there needs to be a movement towards dedicated funding for data collaboratives and towards multi-organization proposals that seek to fill gaps using information sharing.
Although there are challenges, the researchers agreed that the data landscape is better than it was five years ago and is likely to continue to improve, at least in terms of linked administrative data. This will continue to improve our understanding of service use and pathways and allow us to continue making the case for data sharing and prevention.
For more information, see PolicyWise’s Data Infrastructure Roadmap for Youth Homelessness Prevention and Building Youth Homelessness Data Collaboration. To learn more about collecting and using data within youth-serving organizations, see the Data Collection training and the Youth Homelessness Prevention Toolkit on the Homelessness Learning Hub.
This blog is based on the 16th episode of the Prevention Matters series.