Building Inclusive Data: The Power of Intersectional Approaches
We know that anyone who works in inclusion is told that data is the starting point. "Let the data drive your strategy," consultants advise. "You can't manage what you don't measure," the saying goes. But what if you struggle to decipher what the data is really telling you? What if you know the numbers but don't know how to make the best use of them to create meaningful change?
If you've ever stared at a diversity dashboard filled with percentages and charts, feeling like you're missing something crucial, you're not alone. Many inclusion practitioners find themselves drowning in demographic data that feels static, disconnected from the lived experiences of the people it's meant to represent. The problem isn't that we lack data; it's that we're often approaching it in ways that oversimplify the complex realities of human identity and experience.
This is where intersectionality becomes not just important, but essential. When we adopt an intersectional approach to data collection and analysis, we move beyond surface-level demographics to understand how multiple identities and experiences interact, overlap, and compound to create unique realities for different groups of people. More importantly, we begin to see pathways for action that were previously invisible.
Understanding Intersectionality in Data Context
Intersectionality, a framework originally developed by legal scholar Kimberlé Crenshaw, recognises that people hold multiple identities simultaneously: race, gender, age, socioeconomic status, disability, sexual orientation, and countless others. These identities don't exist in isolation; they intersect and interact in ways that create distinct experiences of privilege, discrimination, and opportunity.
In data collection, an intersectional approach means recognising that a Black woman's experience in the workplace isn't simply the sum of being Black plus being a woman. Her reality is shaped by the unique intersection of race and gender, creating challenges and perspectives that might be invisible when we only look at racial data or gender data separately.
The Cost of Single-Axis Thinking
Traditional data collection often employs what we might call "single-axis thinking": examining one demographic category at a time. This approach, while seemingly comprehensive, can mask critical insights and perpetuate systemic inequalities in several ways:
Invisibility of Compound Discrimination: When we only analyse data through single lenses, we miss how discrimination compounds for people with multiple marginalised identities. For instance, examining only gender pay gaps might miss how women of colour face both racial and gender-based wage penalties that create a larger gap than either factor alone would suggest.
Misallocated Resources: Single-axis data can lead to interventions that help some while inadvertently harming others. A programme designed to support "women in leadership" based solely on gender data might primarily benefit white women if it doesn't account for the additional barriers faced by women of colour.
False Universality: Treating demographic groups as monolithic ignores the vast diversity within any single category. "Hispanic" encompasses dozens of nationalities, socioeconomic backgrounds, and cultural experiences that require different approaches and solutions.
The Transformative Power of Intersectional Data
When we embrace intersectional approaches to data collection, we unlock several powerful benefits:
Precision in Problem Identification: Intersectional data helps us identify specific challenges affecting particular combinations of identities. Rather than generic solutions, we can develop targeted interventions that address root causes.
Innovation Through Inclusion: When we understand the full spectrum of human experience, we design better products, services, and policies. Technology companies using intersectional user research create more accessible and inclusive products that serve broader markets.
Resource Optimisation: By understanding where multiple forms of disadvantage intersect, organisations can focus resources where they'll have the greatest impact, addressing multiple equity gaps simultaneously.
Authentic Representation: Intersectional data ensures that the voices and experiences of multiply marginalised groups aren't erased or overshadowed by those with more privilege within the same demographic categories.
The difference is like switching from a blurry photograph to a high-definition image. Suddenly, you can see details that were always there but were invisible before. You can spot patterns, understand root causes, and design solutions that actually work for the people who need them most.
Practical Frameworks for Intersectional Data Collection
Implementing intersectional approaches requires thoughtful methodology and systematic change. Here are key frameworks to consider:
Disaggregated Data Analysis: Rather than stopping at broad categories, disaggregate data to examine intersections. Look at outcomes not just for "women" or "people of colour," but for "women of colour," "young Black men," "LGBTQ+ seniors," and other specific intersections relevant to your context.
Community-Centred Design: Involve affected communities in designing data collection methods. The people who live these intersections often understand nuances that external researchers might miss. Their insights can help you ask better questions and interpret results more accurately. Think of them as your expert consultants—because they are.
Multiple Methods Approach: Combine quantitative data with qualitative insights. While numbers show patterns, stories reveal context and causation. Focus groups, interviews, and participatory research methods can illuminate the "why" behind statistical trends. Numbers tell you what's happening; stories tell you why it matters.
Longitudinal Tracking: Intersectional experiences change over time as people move through different life stages and social contexts. Tracking data over time reveals how intersections create different trajectories and outcomes. Someone's experience as a young graduate might be very different from their experience as a working parent or carer later in life.
Power and Privilege Mapping: Explicitly examine how different combinations of identities relate to power structures within your organisation or community. This helps identify where interventions might be most needed and most effective. It's about being honest about who has voice and influence, and who doesn't.
What Data Should You Actually Be Collecting?
Moving beyond basic demographics means expanding your data collection to capture the intersections that matter most in your context. But let's be practical about this, you don't need to track everything at once. Start with what's most relevant to your organisation and build from there.
Workplace Experience Data
Rather than just asking about overall job satisfaction, examine how experiences differ across intersections. How do promotion rates vary for women of different ethnic backgrounds? What are the retention patterns for LGBTQ+ employees at different career stages? How do flexible working arrangements impact parents from different socioeconomic backgrounds? These questions help you understand not just who's leaving, but why different groups might be having vastly different experiences.
Access and Barriers Information
Collect data on what actually prevents people from fully participating. This might include physical accessibility needs, childcare requirements, language preferences, financial constraints, or cultural considerations that affect everything from recruitment to professional development opportunities. Sometimes the barriers aren't obvious until you ask.
Leadership and Decision-Making Participation
Track not just who holds formal leadership positions, but who participates in informal networks, speaks up in meetings, gets assigned to high-visibility projects, or influences key decisions. These patterns often reveal intersectional dynamics that formal hierarchies miss. You might be surprised by who's actually being heard and whose voices are getting lost.
Professional Development and Growth
Examine how different groups access mentoring, sponsorship, training opportunities, and stretch assignments. Look at who gets invited to networking events, who receives feedback, and whose ideas get credited and implemented. Career progression isn't just about formal qualifications,it's about access to opportunities that often happen informally.
Well-being and Belonging Indicators
Measure psychological safety, sense of belonging, and stress levels across different intersections. How do work-life balance challenges vary for single parents versus childless employees? How do microaggressions affect different groups' sense of inclusion? These softer metrics often predict who'll thrive and who'll burn out.
The key is moving from "How many women do we have?". It's about shifting from counting to understanding.
Implementation Strategies
Making this shift requires concrete actions, but don't feel like you need to do everything at once:
Start by auditing your current data collection practices. What categories do you track? How do you analyse them? Where might important intersections be invisible in your current approach? This isn't about judging what you've done before; it's about understanding where you are so you can plan where to go next.
Redesign surveys and forms to capture relevant intersections while respecting privacy and self-determination. Allow for multiple selections, write-in options, and "prefer not to answer" choices. Remember, the goal is to understand people better, not to force them into uncomfortable categories.
Train your team in intersectional analysis techniques. Help them understand both the theoretical frameworks and practical tools needed to interpret complex data meaningfully. This is as much about changing how people think as it is about changing what data you collect.
Create feedback loops with affected communities to ensure your data collection and analysis actually serve their needs rather than extracting from them. Regular check-ins can help you course-correct and ensure you're asking the right questions in the right way.
Build accountability measures that track not just whether you're collecting intersectional data, but whether you're acting on the insights it provides. Data without action is just expensive reporting—make sure you're using what you learn to create real change.
The Path Forward
Adopting intersectional approaches to data collection isn't just about being more inclusive: it's about being more accurate, more effective, and more innovative. When we see people in their full complexity, we can create solutions that actually work for the real world rather than simplified versions of it.
The frameworks we build today will shape tomorrow's decisions about everything from healthcare delivery to urban planning to educational policy. By embracing intersectionality now, we ensure that these frameworks support human flourishing rather than perpetuating the limitations of our current systems.
The data we collect and the ways we analyse it are never neutral. They either reinforce existing power structures or help us imagine and build more equitable alternatives. When we choose intersectional approaches, we choose to see clearly, act precisely, and create systems that truly serve everyone.
In a world where data increasingly drives decisions that affect people's lives, we have both an opportunity and an obligation to get this right. The tools exist. The frameworks are available. What remains is the commitment to do the harder but more transformative work of seeing people as they actually are: complex, multifaceted, and deserving of systems that support their full humanity.
Taking the First Step
Understanding the importance of intersectional data collection is one thing; knowing where to begin in your own organisation is another. Many inclusion practitioners find themselves caught between recognising the need for more sophisticated approaches and feeling uncertain about how to assess their current practices or identify the most impactful next steps.
If you're ready to move beyond basic demographic tracking toward more meaningful, intersectional data practices, consider starting with an honest assessment of where you are now. What data are you currently collecting? How are you analysing it? Where might important voices and experiences be missing from your current approach? Most importantly, how well are your current frameworks actually serving the people they're meant to support?
This kind of organisational reflection can be challenging to do alone. Sometimes an external perspective can help identify hidden areas and opportunities that are difficult to see from within. Whether through self-assessment tools, community feedback, or diagnostic processes that help map your current state against best practices, taking that first step toward intersectional data approaches can transform not just your numbers, but your impact.
The goal isn't perfection; it's progress toward systems that see and serve everyone. And that journey begins with understanding exactly where you stand today.
Visit Powered by Diversity to find out how we can support your organisation in building more inclusive, intersectional approaches to data and beyond.