Understanding Data Equity

Data equity work begins with the creation of a core team called the Data Review Committee, which is comprised of school leaders, educators, and community partners, including students and families. This core group will serve as the leadership team for this work and be charged with ensuring that equity, diversity, inclusion, belonging, and cultural responsiveness are centered and prioritized. The composition of the core team should be reflective of the overall student population. The team should meet at least once per month; ideally, they will meet bi-weekly. (Note: Some members of the Data Review Committee will also serve on the Data Governance Committee referenced in Project Governance: Committee Model, Roles, and Responsibilities in Module 2.)

What interoperability standards are used to ensure the strategies will be able to validate results and share data across appropriate systems? 

Schools and community partners should work jointly to critically examine existing data sources and data collection processes. Data equity requires both qualitative and quantitative data. Students’ and families’ stories can’t simply be told through numbers and performance measures; they must also be understood through their own narratives and lived experiences. 

When gathering data, schools and community partners should ask why the school is collecting the data, what types of data are being gathered and valued, who the work will benefit, and how the process and product might cause harm to specific members of the community. They should consider sample selection, research design, collector behavior, scope and scale, cultural translation, and collection mediums among other factors. Through this process, schools and community partners can identify which data match the purpose for which they are intended, which do not, and where gaps exist in data collection.

Once they’ve collected data, schools and their community partners need to make important decisions about how the data will be analyzed and the implications for equity. The statistical methodology that schools use, the variables they choose to include or exclude from their models, and the way they choose to classify each data point impact the results. 

Data disaggregation is critical to fully understanding what’s happening within distinct communities. However, it is important not to disaggregate data in such a way that intersectionality is overlooked. Applying an equity lens during data analysis involves incorporating individual, community, political, and historical contexts to inform analysis, conclusions, and recommendations.

How we interpret the results of data analysis is heavily influenced by our experiences, our opinions, and our biases, many of which may be implicit. False assumptions about causality, correlation, expectations, and relevant factors can lead to flawed conclusions. For example, when looking at data indicating higher rates of disciplinary sanctions against students of color, it would be a mistake to automatically assume that students of color have a higher incidence of disruptive behavior. Instead, a careful analysis should include consideration of enforcement data to determine whether there are discrepancies.

Data equity requires schools and community partners to consider who will be involved in data interpretation and how viewing data from a variety of perspectives can reduce the potential for one-sided interpretation. Being transparent about what data is used to inform data interpretation and clearly documenting the process can help to build community members’ trust, capacity, and consensus.

Data equity also involves careful attention to how data interpretation is communicated and to whom. Schools can build trust with their communities by being transparent and inclusive in sharing data. Sharing and communicating about data is an important mechanism in telling the story of a school—what is valued and how education will be conducted—so sharing should be closely tied to the previously established purposes of data and data collection. 

The team must be conscious of the language, framing, and presentation style they use and work with community partners to consider how different approaches can make the data accessible to a wide variety of audiences and stakeholders. Framing is critical, because findings that may appear neutral can be easily misconstrued. For example, sharing that a school district’s standardized test scores have decreased without providing context, such as the fact that the district switched to a new testing provider or curriculum, could fuel a narrative around failing schools and diminish the community’s confidence.

Finally, data equity involves consideration of who is involved when decisions are being made based on data. Schools and community partners should ensure that representatives of all stakeholder groups have a place at the table, especially those who have traditionally been underrepresented. They should ask how they can make the process inclusive and transparent to build trust and shift power dynamics that privilege some members of the community while disadvantaging others.