As a result of this research, we learned that an individual leader can spark an idea or curate a dataset, but it takes a community of stakeholders — including workers — to generate a movement toward real, measurable impact for all involved. Case study findings were particularly important in illustrating what successful cross-sector collaboration and data sharing can look like.
Drawing on the original set of research questions, the following section explores more closely what it takes to build powerful, data-driven, worker-centered ecosystems, and makes clear why stakeholder participation, including workers, is so critical moving forward.
The first set of findings, stakeholder incentives, identifies the motivations for each stakeholder group, and articulates what would incentivize their involvement in a collective impact effort. The second set of findings identifies drivers of change, including workers who are directly impacted, and outlines their potential role in this effort. Lastly, the network-building strategies synthesizes a set of actionable steps to help communities get started, based on case study findings.
Explore these findings by category:
As a fundamental part of this study, we identified what would incentivize each of the following stakeholder groups to participate in a collaborative, data-driven, worker-centered ecosystem: frontline workers, employers, education and career providers, and government agencies. Explore stakeholder incentives to gain insight into their unique perspectives.
Nearly all of the frontline workers we spoke to identified income mobility—their ability to earn more money to provide for themselves and their families—as the number one factor driving their decision to upskill. Along these lines, workers shared that contextualized learning opportunities that incorporate pay—such as internships, management workshops, and hands-on practice—were the most valuable for competency and skill development. When asked about leveraging digital tools and platforms, such as Linkedin, digital badges or micro-credentials, or comprehensive learner records to demonstrate and share their skills and experiences, workers we interviewed showed interest in this potential, but overall felt unequipped for participation in a data-driven workforce. Notably, workers in healthcare settings indicated that a lack of digital skills often prevented them from conducting an extensive job search, creating detailed profiles for online applications, and uploading supporting transcripts, certifications, or credentials. Moreover, workers in the retail industry noted the acquisition of professional skills, such as interpersonal and management skills, that were not represented on currently available certifications and degrees and therefore difficult to demonstrate to potential employers. Most of the workers we spoke to felt disconnected from the systems, programs, and even resources like tuition reimbursement or state grant support, that are designed to support their advancement. Instead, they relied primarily on friends, colleagues, and family members to learn about education and training programs and to explore career growth opportunities.
Employers identified the need to rapidly adapt and grow their business models as technological advancements transform the way they operate and deliver services. Employer representatives in retail and restaurants pointed to automated customer services like online ordering, digital pickup and delivery requests, and processing returns as examples of digital transformation. Most pressing is the need to hire and train staff who can readily apply digital skills in tandem with core professional skills, like customer service, teamwork, and management. For this reason, employers demonstrated considerable incentive to evolve hiring practices, support skill acquisition, and increase retention through workplace training. Most employers expressed interest in developing better systems to track employee skills, competencies, and credentials and to match existing and new talent to horizontal and vertical career growth opportunities.
Education and career service providers that we spoke to were driven to meet learner needs and committed to preparing people for sustainable wage careers. Providers shared limitations in their capacity to collect and track longitudinal data outcomes for participants, including those who exited before completion, leaving them unable to evaluate learner needs and program impact. While meeting learner needs often pushed providers to establish informal partnerships with local industry employers, higher education institutions, and nonprofit organizations, external funding was often the critical factor to securing partnerships through MOUs and propelling data sharing practices. Most providers indicated the funding models dictated what kind of data were collected, reported, and shared, and that their data were rarely used to evaluate long-term impact.
City government leaders and publicly-funded adult education institutions from Madison, Philadelphia, rural Maine, and New York City, were incentivized to allocate resources more effectively and streamline service provision for greater community impact. Many indicated that interoperable data systems supported their capacity to align and coordinate services across agencies and departments. Some government representatives had taken steps to pull data from multiple sources to understand community needs and allocate funding or programming based on disparate datasets. Others looked forward to the benefit of a learner-facing platform or tool to empower individuals to make their own decisions about their future. Notably, we interviewed one representative from the U.S. Department of Education who demonstrated incentive to coordinate interoperability across employers and educational institutions to more effectively connect learning and earning.
Our research indicates that each stakeholder group plays a distinctly critical role in the collective effort to promote a data-driven, worker-centered ecosystem. In three of the four case studies, employers generated demand for community change, both directly, as in the case of local hospitals in Central Pennsylvania, and indirectly, as in the case of industry closures in Taos, New Mexico. But employers were not able to solve the challenge of finding and retaining talent without the support of direct service providers in their communities. Similarly, providers were not able to meet employer demands and provide relevant training or instruction without the support of public and/or private funding. Most importantly, many of the workers we interviewed were not able to access and navigate relevant training and growth opportunities without available information, mentorship, and public services.
This strategic model illustrates how stakeholder groups can work together to participate in a meaningful and mutually beneficial data-driven ecosystem. Providers serve as the connective tissue between employers and frontline workers, but the role of government agencies, private funders, researchers, and policymakers cannot be underestimated. These stakeholders have the potential to reconstruct funding models to support longitudinal data collection through interoperable data system development and management and to promote alignment between industry and worker needs.
Design systems that are worker-centric and coordinate services to ensure greater impact.
From small, rural towns to large metropolitans, communities across the nation demonstrated that shared commitment leads to powerful solutions. Through several case studies, we identified a set of actionable strategies that each community used to build networks and solve workforce needs in their region. To learn more about how each community implemented these strategies, see Regional Design Solutions.
Our case study communities thought about data regionally and cross-organizationally—not just at the institutional level—to enable workers to access resources and upskill in their careers, create personalized pathways, and evaluate and improve programs and services.
These communities invited local drivers of change, including employers, government representatives, education and workforce providers, funders, and workers, to review and interpret the data and make data-informed decisions as a group.
Co-develop and commit to a shared vision with individuals who are most directly impacted, including workers in your community. Develop collective strategies on individual projects to advance the work of the entire community.
Explore ways to align resources, including personnel and disparate funding streams, for greater community impact.
Create intermediate goals and short-term, measurable outcomes. In addition, track data longitudinally to understand the long-term impact on workers in the community, in terms of talent development and income mobility.
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