Authored by: Education Design Lab
Published: February 2026
At the Education Design Lab, we believe a workforce-aligned future depends on data that allow community colleges to adapt in real time. The Lab envisions a future in which community colleges are deeply aligned with local workforce needs and able to pivot as labor markets change. In that future, data is not peripheral, retrospective, or a compliance exercise. Data is the infrastructure that enables institutions, states, and systems to make informed decisions in a rapidly changing economy.
The Data Collaborative for a Skills-Based Economy (Data Collab) was launched in 2023 as a national innovation to address a critical gap in the education and workforce ecosystem: the absence of consistent, learner-level data on short-term, non-degree programs offered by community colleges. While these programs were growing rapidly in response to employer demand and learner needs, they largely existed outside traditional higher education data systems and accountability structures.
The Data Collab was intentionally designed as a multi-institution, cross-sector collaborative, bringing together community colleges, state agencies, national research partners, and data intermediaries to pilot new approaches to collecting, standardizing, and analyzing non-degree learner data. Rather than operating as a compliance system, the Data Collab functioned as a shared learning infrastructure — supporting colleges to experiment with definitions, surface structural barriers in existing systems, and test whether learner-level records from non-degree programs could be responsibly aggregated and, where possible, linked to administrative wage and labor market data. At the time of its launch, no scalable national model existed to support this work.
This brief draws on three years of experience from the Data Collab’s Data Coaches, three of whom are authors of this piece. Over that time, the team coached dozens of community colleges as they worked to collect, report, and make sense of data on short-term, non-degree programs. What follows reflects not a single implementation, but recurring patterns observed repeatedly across institutions as they tried to build data practices in a space that was never designed to be measured.
Reliable data on short-term programs enables colleges to move from intuition to evidence.
Without this data, colleges are currently forced to make high-stakes decisions based on partial or anecdotal evidence, which limits their ability to align programs with workforce demand, demonstrate value to employers, or identify gaps across learner populations.
Five years ago, short-term, non-degree programs sat largely at the margins of higher education. External research at the time described the field as fragmented and experimental, with limited shared definitions, inconsistent quality standards, and little integration into institutional data, advising, or funding systems¹. Most non-degree programs operated outside traditional academic pathways, and there was minimal evidence on learner outcomes, particularly employment and wage outcomes. While interest in short-term credentials was growing among policymakers, funders, and employers, there was little reliable data to assess their effectiveness or justify sustained public investment.
Only in recent months has more rigorous evidence begun to emerge. For example, a study by the Strada Institute for the Future of Work found that learners who completed short-term, non-credit community college workforce training programs experienced an average increase in annual earnings of approximately $2,000 within two years of completion compared to similar peers who did not enroll, with larger gains observed for certain subgroups and program types².
It was in this context that the Lab launched the Data Collab, which was designed to support community colleges in collecting outcomes and demographic data on learners enrolled in non-degree programs, data that colleges were largely not collecting. The initiative also sought to test whether learner-level records from non-degree programs could be aggregated across institutions and linked to administrative wage and labor records. Despite being included in a groundbreaking pilot with Georgetown University’s Massive Data Institute to match learner records to IRS wage data, several delays occurred, and the timeline at this point remains unclear. There was some success with matching a small batch of learner records from Ivy Tech Community College to Indiana’s state unemployment insurance (UI) wage data via a partnership with the Coleridge Initiative’s Administrative Data Research Facility (ADRF), but this approach was not easily replicable across other colleges or states and came at a high cost. At the time, few state or federal systems were equipped to capture non-degree enrollment, completion, or wage outcomes, and the Data Collab was intended as a proof-of-concept infrastructure in a field where such data largely did not exist.
As state policy and data capacity have evolved, the role of the Lab in supporting data capacity is shifting.The Data Collab as a standalone program is now sunsetting — not because the need for non-degree data has diminished (in fact, the opposite is true) — but because many state partners are moving, unevenly and at different stages of readiness, toward encouraging community colleges to ingest short-term, non-degree data directly into their internal student information systems.
This shift reflects a growing recognition that durable data infrastructure must ultimately live within institutional and state systems in order to support accountability, stackability, and emerging Workforce Pell requirements. Workforce Pell, in particular, raises the bar for what non-degree data must exist, requiring verifiable enrollment and completion, alignment with existing accountability systems, and the capacity for employment or wage linkage. Whether and how states and institutions will fully meet these expectations remains to be seen.
The insights that follow reflect patterns that emerged while colleges navigated unclear ownership, fragmented systems, and evolving expectations for non-degree data—realities that continue to shape the field today.
It is often assumed that non-degree data would be easier to collect because programs are shorter and sometimes simpler than degree programs. The opposite is true.
Credit programs evolved inside a regulated ecosystem with federally standardized definitions, established enrollment and completion rules, registrar ownership, and student information systems built for compliance reporting. Enrollment in credit programs requires formal registration and documentation, and credit-based systems often enforce holds that prevent registration without transcripts or other required records, ensuring data completeness even as they create barriers to access.
Non-degree programs evolved outside that ecosystem and were intentionally designed to remove those barriers. They often waive registration documentation, allow third-party payment, operate outside term-based calendars, and start and stop on rolling timelines. These features support speed, flexibility, and employer responsiveness, but they also introduce variability that standard student information systems were not designed to handle, leaving gaps in consistent enrollment, participation, and outcome data.
Non-degree programs also serve a broader mix of constituents, including workforce-focused learners and enrichment participants who should not be subject to the same depth of compliance reporting as credit students. As a result, institutional ownership of non-degree data is often fragmented or unclear, and there is still no federal standard for defining non-degree completion. The very design choices that made non-degree programs effective in the short term now complicate data governance and reporting as accountability expectations continue to grow.
Operationally, many non-degree programs rely on registration systems that function more like online shopping carts, such as Eventbrite, than student information systems. Learners select a course, pay, and enroll, sometimes without providing basic demographic or identifying information.
In these environments, colleges may know little more than a learner’s name and course selection. Without intentional design changes at the point of registration, institutions lack the information needed to understand who they are serving, how outcomes vary across populations, or whether programs are advancing opportunities for subpopulations of learners. Without completion rates, these programs will also not qualify for emerging funding opportunities such as Workforce Pell, though administrators and policymakers must understand the challenges associated with changing the administratively flexible nature of these programs.
Within the current community college data ecosystem, non-degree data rarely sits within institutional research offices in the way degree data does. Instead, it often lives at the intersection of workforce development, continuing education, registrars, employer partners, grant management, and external systems.
Responsibility for outcomes reporting is therefore dispersed, informal, or dependent on individual staff members. When those individuals leave, data processes are frequently disrupted or lost altogether. These challenges are compounded by long-standing divides between credit and non-degree operations. Systems, access, and incentives are rarely aligned, and collaboration across these boundaries remains the exception rather than the norm. As a result, non-degree data continues to sit at the margins of institutional data practice, even as short-term credentials become increasingly central to workforce and talent strategies.
Unlike traditional academic programs, some non-degree programs operate with rolling admissions rather than defined cohorts. Learners enter and exit at different times, progress at different speeds, and complete asynchronously. This design is intentionally flexible for New Majority Learner-Earners, yet it complicates how colleges define completion and calculate completion rates.
Traditional retention metrics are poorly suited to programs intentionally designed with on- and off-ramps and variable pacing. When learners pursue non-degree programs for targeted skill attainment rather than time-bound completion, retention may not be the most meaningful outcome measure. Without shared definitions, even institutions committed to data-informed practice struggle to produce consistent and comparable metrics across programs.
The gold standard for non-degree data is full integration into existing student information systems. When non-degree programs are ingested into the same systems as credit programs, ownership becomes clearer, access is broader, and reporting becomes part of routine operations rather than an added burden. This approach also allows for deeper understanding of how stackability works in practice, a key requirement for both non-degree design quality and Workforce Pell.
At the same time, this work underscores that hybrid approaches are often a necessary transition. Expecting immediate, full SIS integration without corresponding investment, policy alignment, technical support, and clear leadership from state systems risks setting unrealistic expectations and slowing progress.
Technological challenges and varying levels of technical skill pose significant barriers to sustained data collection. When systems introduce friction — through changing requirements, opaque errors, or manual workarounds — they undermine already-limited institutional capacity and slow the pace at which insights can be generated and acted upon.
For non-degree data efforts to succeed at scale, technology must be intentionally designed for real-world use, supporting not only data submission but also streamlined, timely analysis for educators, employers, and institutional leaders. Technology should also make information more accessible to learners as consumers, enabling them to understand program options, outcomes, and value. Ease of use is not a convenience; it is a prerequisite for adoption, analysis, and impact.
Across institutions, technology and data infrastructure alone are insufficient to drive meaningful change. Even where tools exist and reporting processes are in place, the use of data for decision-making is often stalled.
The most difficult work involves changing how people use data in their everyday roles and embracing data as part of everyone’s job. Shifting from viewing data as a compliance requirement managed by a few people to using it as a shared resource for learning and improvement requires changes in mindset, incentives, and norms.
Variation in data literacy across roles further complicates this work. Institutional research teams may be comfortable interpreting outcomes data, while program leaders, workforce staff, and student success teams often lack time or support to translate data into action. Colleges need to invest in a data strategy, which includes data literacy and culture around data use, alongside data collection processes and technology. When data is shared without context or facilitation, it can feel abstract or punitive rather than useful.
States, systems, and colleges should explicitly acknowledge that non-degree programs were built outside regulated data structures and invest in shared definitions, governance, and ownership models suited to short-term programs. Federal and state agencies should prioritize the development of clear, flexible standards for non-degree enrollment and completion.
Colleges should audit non-degree registration processes and intentionally determine which learner data elements are essential for supporting learners, accountability, and funding eligibility.
Colleges should formally assign responsibility for non-degree outcomes data within institutional research, with defined data steward roles spanning workforce, continuing education, and academic affairs.
Policymakers, accreditors, and institutions should recognize flexible pacing, partial attainment, and skill-based outcomes as legitimate measures of progress.
Data platforms should be evaluated based on ease of use, reliability, and alignment with institutional workflows.
Technical investments should be paired with leadership practices, shared sensemaking, and data literacy supports that enable data to inform decisions over time.
The insights outlined in this brief highlight structural realities about how short-term, non-degree programs were designed, governed, and valued within higher education — and why those origins now matter. Non-degree programs are no longer peripheral experiments; they are increasingly central to how community colleges respond to labor market change, serve New Majority Learner-Earners, and partner with employers. Yet the data systems, policies, and practices needed to support these programs have not evolved at the same pace, creating a persistent gap between the importance of short-term credentials and the field’s ability to understand and improve them.
As public investment in short-term credentials increases and accountability expectations sharpen, these insights point to a critical inflection point. The challenges documented here are not the result of poor execution by individual colleges, but of misalignment across technology, governance, policy, and data culture. Addressing these issues is essential if non-degree programs are to be scaled responsibly.
AI was used for editing purposes (ChatGPT). All use of AI has been reviewed to align with Education Design Lab’s approach to human-centered design and responsible innovation, and its standards for accuracy, equity, and ethical use.
Van Noy, M., Jacobs, J., Korey, S., Bailey, T., & Hughes, K. L. (2008). Noncredit enrollment in workforce education: State policies and community college practices. Washington, DC: American Association of Community Colleges and Community College Research Center. https://files.eric.ed.gov/fulltext/ED503447.pdf
Bahr, P. R., & Columbus, R. (2025). Labor market returns to community college noncredit occupational education. Educational Evaluation and Policy Analysis. https://doi.org/10.3102/01623737251360029 (Research supported by the Strada Institute for the Future of Work.)
Education Design Lab (the Lab) is a national nonprofit and intermediary with a mission to co-design an inclusive, skills-based learn+work system that facilitates upward economic mobility and closes opportunity gaps for the New Majority Learner-Earner. Our facilitated design process helps employer and education stakeholder groups co-design and launch scalable, skills-based education-to-work pathways that align talent supply and demand.
The Impact by Design publication series is part of an ongoing effort to share Education Design Lab’s impact across the learn+work ecosystem.
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