Building Equitable Pathways (BEP)

Data Enablers: Critical Conditions to Design, Deliver, and Evaluate Equitable Pathways

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Data are essential to prove the efficacy of an intervention, to demonstrate the need for a new approach, or to simply understand how our individual efforts add up to systems-level trends. In their work designing and advocating for more equitable pathways systems that span education to careers, intermediary organizations use data from multiple sources. Internally, they monitor and evaluate program performance. They also rely on data from across education and workforce systems to reveal trends and identify areas for greatest impact. Identifying cross-systems trends requires responsibly painting a picture by looking across K–12, postsecondary, and labor market data.  

Working with diverse data sets, intermediaries ask—and work to answer—questions such as:

  • Who advances and who gets left behind at key transition points between secondary, postsecondary, and workforce systems in our program?
  • What is the significance that X percent of Black students in our program graduate with a STEM associate’s degree? Are we doing well or poorly from an equity perspective? 
  • How do we compare local or programmatic data with state and national trends?
  • How do we track postsecondary pathways into the regional labor market when data sets follow different timelines and individuals?

This brief seeks to identify the necessary conditions within an intermediary and its ecosystem for enabling responsible data collection and analysis, empowering data-driven decision-making, and pursuing systemic and equitable change. 

About Building Equitable Pathways

Building Equitable Pathways is a community of practice with 14 innovative intermediary organizations, JFF, the Bill & Melinda Gates Foundation, Bloomberg Philanthropies, and the Walton Family Foundation. Together, we seek to increase our individual and collective capacity to change our education and workforce systems for the better. We will identify best practices, create tools, and develop a theory of action to support the efforts of high-quality intermediaries to transform our systems and scale and sustain equitable pathways. We aim to drive engagement across these systems, improve their sustainability, and ultimately, influence more equitable student outcomes in academics and careers.


Data Enablers

What is a data enabler? In brief, it is a person, thing, or condition that makes something possible. The term enabler is adopted from the policy world’s practice of identifying the necessary conditions to enable a new policy to take root. Enabling conditions for cross-system equity analysis include 1) access to priority data sources and types, 2) adequate data capacity of the intermediary’s staff, and 3) a culture appreciative of the evidence data provides. 


Each enabler allows for a different level of analysis and action, working together to inform what the data does—or doesn’t—make possible. For example:

  • Collecting more data won’t necessarily help us close equity gaps if it isn’t disaggregated (see here).
  • Having disaggregated data won’t necessarily help us close equity gaps if the staff capacity doesn’t exist to communicate findings with people empowered to act on those findings (see here).
  • Communicating findings to people with the authority to act won’t necessarily help us close equity gaps if definitions of success and accountability aren’t shared (see here).

Individually, these enablers may seem obvious, but together they create a road map toward a robust data ecosystem that centers equity. Each enabling condition is key to designing, building, and evaluating effective pathways work in terms of how it aids or inhibits the use of data for positive impact. Each requires partnership and collaboration, investments in technical tools and capacity, and strategic mapping of available data to address collective priorities. Intermediaries play an important role in advancing these enabling data conditions. But the end goal in using this data is to help youth, educators, employers, funders, and policymakers feel equipped and empowered to make changes—both individual and systemic—that lead to more equitable pathways.

1. Data

Intermediaries access, analyze, and share data—both program data and public data (see Appendix A)—to enhance their work with regional partners to guide equity analyses and inform pathways strategies. Program data are collected internally on a given program (e.g., a career and technical education (CTE) program or an apprenticeship program); public data are available from myriad sources, such as the U.S. Census, the Bureau of Labor Statistics, and state labor market information (LMI) agencies. Public data can supplement and contextualize the data intermediaries collect about their own work or that of partners. Intermediaries help determine what existing data, or new data, can best inform steps toward more actionable processes and outcomes. For more on specific pathways data sources and types and the differences between program and public data, see Appendix A.





Enable an equity analysis of pathways outcomes and identify high-leverage opportunities for change.


Reflection Questions:
  • What indicator(s) do you find important in determining if your pathways system is becoming more or less equitable?
  • Where do you find that data, and how often do you revisit it?
  • How does student voice show up in your pathways data, if at all? How do students inform your analysis of the data you gather?
  • What data source(s) need to be considered and potentially paired to make sure your indicators are accurate and actionable?


2. Capacity

To conduct the analyses described above, intermediaries need partners or their own staff to build the skills to collect, access, analyze, understand, and communicate information. Like any organization, intermediaries are limited in their capacity, namely by the extent of their access to the resources, tools, and staff capacity and skills necessary for this high-impact work. Data capacity encompasses the ability of intermediaries and partners to collect, analyze, and use data and the amount of time they have available for this work. The quality of the resources, tools, and abilities all contribute to the ultimate impact in the field. Intermediaries shouldn’t be responsible for doing all the pathways data work in their region, but they can be instrumental in coordinating the enabling conditions that ensure the data work happens. 





Obtain and process timely and accurate information that can guide equitable actions and decision-making.

Reflection Questions:
  • How does your organization decide which capacities to staff in house, what to contract for, and how to distribute data roles among partners?
  • How does your organization prioritize data requests, from both internal and external stakeholders? Based on that, how do you align staffing capacity to match those priorities? 
  • How do you understand your stakeholders’ goals? How do you use that understanding to influence your communication about data?
  • How do you know when your communication or presentation about data has been effectively received by partners? How much time or staff capacity is devoted to gathering and acting on that feedback?


3. Culture

Data culture encompasses both the culture of the intermediary organization internally as well as the culture across the organizations the intermediary interacts with in the pathways ecosystem. Intermediaries constantly navigate this ecosystem, forging relationships, championing policies, and implementing and analyzing practices that ultimately support equitable pathways. This culture can be explicit, codified through data-sharing agreements, or implicit, showing up in the learning mindsets of intermediaries and partners.  





Equity-centered expectations for pathways programming with clear roles for K–12, postsecondary, and workforce partners in attaining those goals.

Reflection Questions:
  • How do all the stakeholders in your pathways ecosystem define success? To what extent do these visions of success align?
  • How does your definition of success translate into what you measure, how often you measure it, and what actions come from those measurements? 
  • Whose data needs get prioritized as urgent in your organization, and how does that impact your work with partners? 


Data for Whom:
Key Customers for Equity Data

The enablers connected to data, data capacity, and data culture are foundational conditions that support equitable pathways design but are essential only in that they enable stakeholders across the education and workforce ecosystems to collaborate for change. Each stakeholder group understands data to varying extents and uses it for distinct purposes. Intermediary organizations use data to equip and empower these key stakeholders, detailed below, to build more equitable pathways through program design, advocacy, and individual decision-making. For examples of metrics used by the stakeholders below, see Appendix B. 

Understanding various stakeholder groups, and how they understand and use data, is an essential piece in mapping out your own data ecosystem.

Surveying Your Ecosystem

The above enabling conditions are illustrative of the experience of members of the Building Equitable Pathways community of practice and are not meant to be exhaustive. Each intermediary has their own data system needs and unique challenges.  

Use this tool to think through the way your organization currently uses data, in both an evaluative sense and to empower others. 


To sum up, this paper addresses an issue that is critical to our pursuit of equity but rarely named and subjected to analysis: what conditions need to be in place to access data and use it responsibly to drive more equitable outcomes?


Where the Field is at 

Currently, each locality and each organization is left to patch together the data they can readily access to conduct an equity assessment of pathways in a region. One organization might rely solely on federal census data while another might rely on their own programmatic data alongside data they gather from individual employers. 

Because we typically don’t have a unified consensus on what, if any, pathways data should be available, how often, and to whom, we have perpetuated a system that relies on the strength of individual trusted relationships or the know-how to navigate complex data-sharing agreements. The ability to then interpret that data in effective and timely ways hinges on funding—for staff, for access to paid services that shorten the time it takes to pull data together, and for platforms and mechanisms for communicating data findings in timely and actionable ways.

In part because we use different data sets—most of which are too different to be analyzed side by side—and our systems are siloed in various ways, we don’t have a unified picture of what is actually happening. A school superintendent’s understanding of equitable pathways can be very different from that of a college dean, which again, might differ drastically from that of the local chamber of commerce or an elected official. Even state longitudinal data systems differ in who has access to the data or related reports to inform what analysis should even take place. This in turn affects our ability to unite around shared definitions of equity solutions that address the root causes of inequality.  

In short, we have some general sense of agreement in the field about the importance of individual metrics for measuring equity in pathways (see examples in Appendices A and B), but the data we use to inform those metrics—and who has access to what data and when—vary widely within and across geographies. Even within the Building Equitable Pathways community of practice, no two organizations have the same experience with, or access to, the enablers outlined in this brief.

Where We Want to Be

Increasing the number and scope of regional partners experiencing most of the enabling conditions described here is something we can address through intentionality and by way of funding and policy decisions at the local, state, and federal level. For example, the states of Washington and Connecticut have made great strides in the ways in which they gather, process, and share wage record data. In Connecticut, employers are now required to report detailed information including employees’ gender identity, age, race, ethnicity, veteran status, disability status, and highest education completed—a huge step in enabling an equity analysis to even take place.

Looking to the future, intermediaries are well positioned to provide a clear-eyed assessment of where their state or region is currently at in each of these enabler categories. The goal is to bring stakeholders together around a united data vision and plan, sparking changes to how we determine what data are important, how we prioritize data staffing and skills, and how we can collaborate differently and hold one another accountable in service of more equitable pathways.  


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