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Social service leaders and community builders are increasingly looking at the “big picture” to understand why certain groups fare worse than others. Systems-level data – information gathered across multiple programs, sectors, and communities – offers a bird’s-eye view of how social inequities play out in real life. By zooming out to see broad patterns, while also zooming in on specific groups through disaggregated data, we can uncover disparities that once remained hidden in plain sight. The result is a more truthful, compassionate narrative of community well-being – one that resonates with Executive Directors, Program Managers, Funders, and Policy Leaders alike.
Consider the challenges faced by a newcomer single mother living with a disability in a rural Canadian town. Her experiences are shaped not just by one factor but by an intersection of many: gender, immigration status, health, income level, and geography. Traditional data reports might treat her as an “average Canadian,” but integrated systems-level analysis ensures her story – and those of other marginalized individuals – isn’t lost in the averages.
In this blog, we will explore how systems-level insights, powered by integrated and disaggregated data, reveal social inequities and inform action. We will look at Canadian examples and frameworks to examine how race, gender, Indigeneity, disability, income, and geography intersect in data. Ultimately, we’ll see how these insights can drive better funding decisions, more targeted services, and stronger advocacy for equity.
Social inequities are rarely random – they are rooted in systemic patterns. Systemic inequity means that disparities in outcomes (such as health, income, safety, or education) are caused by the structure of our society and its institutions, not just by individual choices. These structures include historical and current policies, biases, and economic conditions that advantage some groups and disadvantage others. In Canada and elsewhere, systemic discrimination on the basis of race, gender, Indigenous identity, disability, and other factors has created deep-rooted gaps in wellbeing. A recent federal strategy document observes that racism often “intersects and makes the experience of racism even more severe” when combined with sexism, homophobia, ableism, and other forms of oppression. In other words, people who belong to multiple marginalized groups face compounded barriers.
To address such complex challenges, leaders are adopting frameworks that explicitly recognize intersectionality and systemic bias. Intersectionality, a term coined by Kimberlé Crenshaw, refers to how different aspects of a person’s identity (like race, gender, class, etc.) overlap and shape their lived experience. The Government of Canada has embedded this thinking into its policy process through things like Gender-Based Analysis Plus (GBA Plus). GBA Plus is “a process for understanding who an issue affects and tailoring initiatives to meet diverse needs,” and the “plus” signifies looking beyond gender to other identity factors. In practice, applying an intersectional lens means asking at every step: Who might we be leaving out or unintentionally harming with a one-size-fits-all approach? Policies such as Canada’s Anti-Racism Strategy (2024–2028) also emphasize that different forms of discrimination overlap, and call for “removing barriers and making systems more inclusive – especially for marginalized communities.”
Crucially, recognizing systemic inequity also means involving those affected in data and decision processes. For example, Indigenous data sovereignty has become a key principle in Canadian data strategies. Indigenous leaders and organizations, such as the First Nations Information Governance Centre (FNIGC), assert the rights of First Nations, Inuit, and Métis communities to control their own data. Canada’s federal data strategy echoes this, introducing a Transformational Approach to Indigenous Data to support Indigenous communities in collecting, managing, and using their own data. These community-driven approaches ensure data is used ethically and for the benefit of the people it describes. They also broaden our perspective of inequity: instead of viewing Indigenous communities only through deficits, we incorporate Indigenous knowledge and priorities into what is measured and reported.
Understanding inequities at a systems level, then, starts with these foundations: an intersectional lens, an appreciation of historical/systemic factors, and an inclusive approach to data governance. With this mindset, we can turn to practical tools – integrated data systems and disaggregated data – to illuminate the realities on the ground.
Life doesn’t happen in silos. The factors that shape well-being – from education and employment to health care access and housing – are interrelated. Yet, traditionally, data about these factors is collected by separate agencies and rarely analyzed together. Integrated data systems are changing that. By securely linking data from different sources and sectors, these systems enable us to see the full picture of a person’s or a community’s interactions with public services. This is what we mean by systems-level insights: the ability to connect the dots across various domains of life.
In Canada, Statistics Canada has been championing this approach. The agency’s modernization vision explicitly calls for greater “integration of social, economic, and environmental data” to inform policymakers about post-pandemic challenges. Concretely, StatsCan is developing a Social Data Integration Platform (SDIP) that can combine survey data with administrative records (e.g., health or employment data), and a Secure Infrastructure for Data Integration (SIDI) to link data from StatsCan and other organizations while protecting privacy. These initiatives enable analysts to, for instance, study how an individual’s housing situation, education history, and health outcomes interact. For community-level insight, integrated data can map out how different neighborhoods or population groups use services – highlighting gaps where people might be falling through the cracks.
Integrated data can also trace individuals’ journeys through multiple systems. A person facing homelessness, for example, might show up in emergency shelter data, hospital records, and police contact logs. Linking these datasets can reveal a cycle that no single agency could see on its own. Canadian health researchers have begun doing this by adding flags for homelessness in hospital records – uncovering far more hospitalizations among people experiencing homelessness once the data was properly coded. Such evidence builds the case for upstream investments (like supportive housing or preventive care) that cut across departmental budgets but save costs and lives in the long run.
For leaders in social services and nonprofits, the takeaway is powerful: By integrating data and working across silos, we gain a realistic understanding of community needs. It allows us to move from anecdotal evidence to systemic evidence. Instead of each program addressing one piece of the puzzle, collaborative data analysis can inform a coordinated response – whether that’s multiple agencies co-designing interventions or funders pooling resources to support a holistic strategy. Systems-level data shines a light on the interconnected nature of social issues, ensuring that our solutions match the complexity of the problems.
If integrated data gives us breadth, disaggregated data gives us depth. Disaggregating data means breaking down aggregate statistics into sub-categories – for example, instead of reporting an unemployment rate for all of Canada, we look at unemployment by race, by gender, by region, and so on. This is critical because averages can mask serious disparities. As one Canadian report put it, disaggregated data “reveals patterns obscured by aggregate data” – essentially uncovering inequalities that would otherwise remain unseen.
During the COVID-19 pandemic, the importance of disaggregated data became painfully clear. Early in the pandemic, Canada did not systematically collect race-based health data, leaving blind spots on how COVID was affecting racialized and immigrant communities. Community stories and local research, however, painted a bleak picture: low-income neighborhoods with higher proportions of visible minorities had far higher infection rates. In Toronto, for instance, researchers linked postal code testing data with demographic info and found COVID positivity was concentrated in areas of high diversity, dense housing, and poverty. In response to mounting public pressure, provinces like Manitoba and Ontario began gathering race and ethnicity data on COVID cases. In Ontario, data revealed that throughout the pandemic, the White population (roughly 66% of Ontario) made up only about one-fifth of COVID cases, while certain groups suffered dramatically higher hospitalization rates – Latino and Middle Eastern communities faced hospitalization rates seven to nine times higher than White residents. These disparities confirmed what community advocates suspected: systemic inequalities (like who has precarious “essential” jobs, crowded housing, or limited access to healthcare) were translating into vastly different health outcomes.
Beyond health, disaggregated data illuminates inequities in income, education, justice, and more. For example, aggregated figures might show overall economic growth or poverty reduction, giving a sense that “things are improving.” But a disaggregated look often reveals that not everyone benefits equally. A recent Government of Canada analysis of the 2021 Census found that the median income for all Indigenous identity groups in Canada (ages 25-64) was lower than for the non-Indigenous population.
Disaggregated data also exposes gaps for people with disabilities. The employment gap reflects barriers in the labor market – from inaccessible workplaces to bias in hiring – which are invisible in a topline unemployment statistic. Similarly, gender-disaggregated data highlights persistent gender inequality. Crucially, the pay gap widens when you account for race and Indigeneity: racialized, Indigenous, and immigrant women earn even less relative to non-racialized men. This tells us that an Indigenous woman or a Black woman is facing both a gender wage gap and a racial wage gap simultaneously – insights that would be impossible to pinpoint without breaking the data into those intersecting categories.
In practice, what disaggregated data gives leaders is the ability to see the “equity gaps” clearly. Instead of assuming a program is successful because it reaches a lot of people, we can ask: Which people? Are we serving Indigenous clients well? Are women and men benefiting equally? Do outcomes differ for Black Canadians versus others? Often, asking these questions unearths blind spots. In countless settings, from city councils to nonprofit agencies, similar revelations occur when data is broken down: perhaps a mentoring program learns it is mostly reaching boys and needs to adjust to engage more girls, or a health clinic finds its services aren’t accessible to people without a car, indicating a rural access issue.
Simply put, disaggregated data turns general insights into specific, actionable knowledge. It equips leaders to move beyond averages and target resources where they are needed most. It also holds systems accountable: if certain groups consistently have worse outcomes, the data makes it impossible to write it off as anecdotal. Instead, it compels a response – a change in strategy, an infusion of support, or a policy correction – to close those gaps.
While national statistics are valuable for the big picture, inequities are often felt most acutely at the community level. Canada is a vast and diverse country; averages can hide major differences between regions and neighborhoods. That’s why community-level analysis is essential. It means examining data for specific cities, towns, regions, or even within cities (by neighborhood), and often combining quantitative data with local knowledge about context.
Geography itself can be a driver of inequity. A community analysis of health outcomes might reveal higher diabetes rates or anemia in a remote community and trace it back to these systemic access issues. Similarly, rural communities might have fewer healthcare providers, limited public transit, or scarce employment opportunities, which all impact residents’ well-being. A systems view recognizes these spatial inequalities and avoids the trap of assuming one region’s reality applies to all.
One powerful tool for community-level insight is mapping data. By mapping indicators – such as poverty rates, school graduation rates, or crime rates – across different communities, patterns of advantage and disadvantage become visible. In many Canadian cities, maps of poverty or health outcomes often resemble maps of historically segregated or underserved areas. For instance, a national report on health inequalities highlighted how certain urban neighborhoods with higher proportions of racialized and low-income residents faced worse health outcomes on multiple fronts. These insights help policy leaders to identify “hot spots” where targeted interventions are needed. A funder could use this information to direct grants toward community organizations in the most affected areas. A social service agency might use local data to advocate for a new facility or program where a data gap exists.
Community perspectives and participatory data are also key. Quantitative data might show that a particular region has a high unemployment rate among youth. But qualitative insights from that community – gathered through consultations, surveys, or storytelling – will explain why. Perhaps there is a lack of public transit to reach jobs, or discrimination in hiring, or simply a shortage of local opportunities. For example, statistics may show a certain immigrant community under-utilizes a public service; conversations with that community might reveal language barriers or mistrust stemming from past experiences, pointing to solutions like hiring multilingual staff or cultural liaisons.
In short, community-level analysis grounds systems-level insights in reality. It prevents us from making one-size-fits-all conclusions and instead tailors understanding to local contexts. For Executive Directors and program managers, this is incredibly practical: it’s the difference between deploying a program uniformly versus adapting it to each community’s unique needs. Funders and policy makers, too, increasingly require proposals to include local data or needs assessments, ensuring that interventions are evidence-based and community-specific. Systems thinking doesn’t mean everything is handled at the national scale; it means recognizing patterns at all scales – national, regional, local – and understanding how they connect.
Identifying inequities is only the first step. The true promise of systems-level insights is driving action – shifting how we fund, design programs, and advocate for policy change in order to close the gaps we’ve identified. Data, in this context, becomes a powerful lever for accountability and innovation. It moves equity from a value in mission statements to a measurable outcome that leaders commit to improving.
How can leaders use these insights in practice? Here are a few ways:
Importantly, the process of turning insight into action should remain community-centered and empathetic. Data can sometimes seem cold or impersonal, but the way we use it must never lose sight of the human stories involved. Equity-focused leaders talk about being “trauma-informed” and “culturally responsive.” This means when we see a gap – say, a higher unemployment rate for a certain group – we don’t label that community as “problematic.” Rather, we recognize the trauma or structural barriers they may have faced (such as a history of residential schools affecting Indigenous employment, or racism in hiring for racialized Canadians) and design responses that are healing and empowering. We ensure affected communities are part of crafting the solutions, not just subjects of analysis.
Finally, an underappreciated aspect of using data for equity is learning and adapting. Complex social inequities won’t be solved overnight, and some interventions will work better than others. By tracking data over time, organizations can evaluate what’s making a dent and what isn’t. Perhaps a pilot program did not reduce the inequity it targeted – the data, disappointingly, shows little change. Rather than seeing this as failure, data-driven leadership treats it as feedback: adjust the approach, try a different strategy, and continue monitoring. This iterative, outcomes-focused mindset is common in collective impact initiatives and social innovation labs, including some in Canada. It demands humility (willingness to change course) and persistence (commitment to the long game of systems change). The reward is that, over time, the numbers begin to move in the right direction – gaps close, outcomes equalize, and lived experiences improve.
Social inequities have deep roots, but they are not insurmountable. Systems-level insights give us a map of the terrain: where the gaps are widest, which barriers affect whom, and how different factors interlock to create disadvantage. In Canada, we have seen a decisive shift toward harnessing data for equity – from high-level investments like Statistics Canada’s disaggregated data initiatives, to community-level tools that pinpoint needs, to frameworks that build intersectional thinking into everyday governance. These efforts all recognize one truth: we cannot fix what we cannot see. By illuminating inequities, data empowers us to address them with clarity and purpose.
Imagine a future where every funding proposal or policy plan in Canada includes an equity impact assessment supported by robust data. Leaders would routinely ask: “How will this affect Indigenous people, or seniors, or new immigrants, or people with disabilities? Does the data indicate any unintended gaps we need to mitigate?” In such a future, blind spots would shrink because our analytical lenses would be wide and sharp enough to catch them. Programs would be designed from the outset to be inclusive, rather than retrofitting equity as an afterthought. Community voices would be validated by data, and vice versa, creating an undeniable mandate for action.
We are not fully there yet, but the progress is encouraging. As we’ve discussed, initiatives across Canada are already revealing real-world disparities – be it in pandemic outcomes, income levels, or service access – and turning those revelations into concrete responses. It is now up to leaders like you to accelerate this momentum. By adopting systems-level data insights as a cornerstone of strategy, you can help ensure that our social services and policies uphold Canada’s values of fairness and inclusion.
Are you ready to improve client outcomes while reducing administrative work for your team?
