Data is powerful. It shows us patterns, reveals gaps in our understanding, and clarifies steps we need to take to solve problems. Except when it doesn’t. The fact is, there are serious limits to quantitative data, including the ways it can be manipulated and misinterpreted. When assessing matters of DEI, quantitative data is just one of many reference points needed to get the full picture and to start developing solutions.

Organizations often seek purely data-based solutions, but this strategy risks serious blindspots. DEI solutions come from surveying a variety of sources, triangulating, and coming to nuanced conclusions based on full context. Conventional data collection methods often do not adequately capture the experiences of marginalized groups, leading to underrepresentation or misrepresentation in datasets. Additionally, obstacles like underreporting, distrust of institutions, and fear of repercussions can lead to gaps in data and biases in sampling. To address these challenges, culturally sensitive approaches that engage authentically with diverse communities must be adopted in order to fully understand the issues at hand. 

So how can individuals and organizations improve the analyses they use to develop better solutions? When data is being collected for the purposes of DEI work, we need to ask ourselves a series of questions: 

• How are our analyses honoring the humanity in the data? 
• What are the categories we use to disaggregate in our analyses? 
• How are we triangulating qualitative with quantitative data? 
• Are we able to include quotes and/or storytelling as opportunities to humanize the analysis? 
• Who are the least-heard or the least-reached and what are we doing to make sure we are hearing and centering their perspectives? 
• How fully are we honoring the lived experience of our stakeholders through our data collection strategies and analysis?

These questions are front and center in our DEI practice. Our recent work with the Queens Public Library to improve outcomes for Black library staff and patrons highlights how the qualitative and quantitative go hand-in-hand. In Queens, we prioritized real world conversations with employees and patrons in informal settings, helping reveal patterns that numeric data could not. On-the-ground observation of library branches provided additional, nuanced context to guide our recommendations. This approach is especially essential when organizations lack existing substantive raw data. 

The benefits of a multifaceted approach go beyond assessment projects and into the realm of professional development and training. In Massachusetts, where we led a series of DEI training sessions for adult educators, quantitative data analysis was overlaid with personal anecdotes and storytelling, emphasizing points hard data alone simply couldn’t. By gathering students’ stories about the specific challenges they faced individually and collectively in adult education programs, we were able to craft learning sessions that more effectively reflected the experiences of adult education students and were ultimately more impactful. 

By embracing minority perspectives that typically get lost in aggregate data sets, engaging with diverse sources of information, and interrogating the complexities of identity and power, we move closer to achieving our shared vision of a more equitable and inclusive society. Context matters. Biases run rampant. With a more holistic approach to data collection and use, we can get closer to truly understanding our collective reality.