Infrastructure, tools, and methods that enable data collection, analysis, and data-driven decisions
What do equitable data collection, analysis, and decision-making look like? An agency that has strong equity practices with their data is being intentional about the process for collecting and using data and has involved a variety of stakeholders in creating that strategy. Data can support equity by uncovering inequities through disaggregation, demonstrating the relative success or failure of interventions for different groups, and providing background information for strategic decisions.
Why is this important? Data can be used to improve equity, maintain the status quo, or increase inequity. Agencies that do not ask questions that get to the root of inequities in the processes of data collection and analysis and use these questions to understand how they collect, analyze and use data to drive strategy, investment, and implementation decisions will likely perpetuate inequality.
How do we go from inequitable systems for data to a racially equitable system of using data? To address racial inequality through data, governments should start with three key questions:
- Do we have the data we need to talk about equity?
- How are we collecting and using that data?
- How can we use data to address equity related problems?
1. Do we have the data we need to talk about equity?
Governments need data to understand inequities in outcomes for the goals of their agency. Without high-quality data that can be disaggregated by race, income, gender, etc. agencies will not be aware of potential inequities. With that data, agencies then have a tool to understand how they are doing in addressing those inequities.
2. How are we collecting and using that data?
When agencies have access to the data they need or are in the process of determining how to collect that data, they should examine the equity implications of how they are collecting and using data and be intentional about doing so in an equitable way.
Tools that support equitable ways of collecting data include:
- Data Biographies: A Data Biography tells the story of who is collecting the data, who owns the data, how was it collected, how big was the sample, who was included or excluded by the sampling, when was it collected/updated, why was it collected, and what else is known about the data. This tool can help ensure that data is not being used to mask or perpetuate inequities. We All Count has developed two data biography tools, the simple excel template and a more comprehensive online tool (this is currently in its beta version).
- Participatory Data: Governments should also be intentional about involving community experts, states, contractors, grantees, and individuals in determining what data is collected and how it will be used. Data collection is an extractive process where the power is in the hands of those collecting the data. By involving more people and institutions in conversations about how their data is collected and used, agencies can craft a more equitable process and also improve the quality of the data they are collecting.
- Public Tools for Data Visualization: To benefit from different interpretations of data and new, crowdsourced ideas for improvement, agencies should take advantage of open data and public tools for data visualization. These tools make it easier for the public to understand data, find correlations between variables, and identify disparities in services and outcomes. When these tools are made available they need to be well advertised to the community and should be accompanied by simple data literacy courses and connections to public computer labs or libraries. Tableau’s Government Analytics is an example of a tool that can strengthen the ability of the public to draw insights from data and contribute to the conversation.
3. How can we use data to address equity-related problems?
Data is not just useful for identifying inequities in outcomes, it can also be actively used to address equity challenges. One way to do this is to consider what data might be able to be linked to provide more robust information about particular geography, program, or demographic. For example, how can more agencies at federal and state-level access and use each other's data to better understand outcomes for different populations and begin addressing inequities that this data uncovers? In the past, we have seen the Social Vulnerability Index, HUD Location and Affordability Index, WIOA performance measures, and other indices being successfully used on a case-by-case basis, but there is significant room for additional coordination and expansion of the use of these data sets.