Tapping Data for Frontline Talent Development – Digital Promise

Tapping Data for Frontline Talent Development

Current Ecosystem to Ideal Ecosytem

Why It Matters

Who Are Frontline Workers?

Frontline workers are the workers at the frontlines, who deal directly with customers and deliver services.

< $30K Majority make less than $30K per year

< 35 years 30% are under 35

Low education levels Most have less than an associate's degree

60-70% have job-limiting foundational (e.g., literacy, numeracy, problem-solving) skills

40% work in retail, healthcare, food service and hospitality

frontline workers

Upskilling Opportunities Advance a Frontline Worker's Prospects for the Future

Employers offer opportunities

89% of employers who hire frontline workers offer education/training opportunities

73% of those employers don't know how many employees take advantage of opportunities

60% do not view frontline worker upskilling as a high priority

frontline workers upskilling

Some workers pursue opportunities

1 in 10 workers participated in an adult education class in the previous year

27% pursued a formal degree or certification in the last year

39% pursued other types of educational opportunities

69% pursued educational opportunities because of a job

Completion and relevance are lacking

1 in 3 say logistical barriers keep them from pursuing or completing education

50% drop out of their adult education class within 12 hours of starting

51% say that the education they've had in the last year was only somewhat useful or not useful

Frontline workers may not be getting the education and training they need. They lack the data regarding their own skills and learning, and they don't know what skills employers need in quality workers. Access to this data would help workers make decisions about how to advance their skills.

How Data Can Help the Frontline Worker

Having the right data will equip frontline workers with the agency and knowledge to make informed decisions about their future, and will equip stakeholders with information needed to deliver high-quality services.

a worker

Frontline Workers can access their data to make informed decisions

Gears with dollar signs on them

Funders can make informed, strategic investments

A small shop

Employers can maintain an effective workforce and support their frontline workers

A school

Programs / Providers can deliver relevant and high-quality services

How do we get from here to there?

Data Sharing and Interoperability

The Current Ecosystem

The current learning ecosystem that serves frontline workers is complex, siloed, and not set up to enable workers to direct their own pathways. Despite the amount of data collected, the processes and systems in the ecosystem do not support the flow of data between stakeholders or frontline workers.

Many different types of data are collected across the learning ecosystem.

an italic i on a blue circle = click or tap for more info

  • Personal Data
  • Assessments
  • Case Management
  • Employment Records
  • Financial Records
  • Labor Market Data
  • Learning Records
  • Pathways
  • Training Records

Different stakeholders collect the same data about the same frontline worker. Even within a single organization, staff often are collecting duplicative data.

Select a Stakeholder

  • Instructional Technology
  • Employer Training Providers
  • Educational Providers
  • Federal & State Government
  • Job Centers
  • Employers
Data being shared with stakeholders The worker's data is being shared with numerous stakeholders. There is a lot of overlap in what is being collected.


  1. Data collection and data entry is a very manual process
  2. Data are typically collected because of funding requirements
  3. Data collected are largely output data rather than outcome data
  4. A plethora of data exists, but with insufficient analysis and understanding

"There's a lot of data that's being collected that needs to be used more strategically. I think that the workforce system tends to collect data for reporting purposes, which oftentimes limits what you can learn from it."


"This data collection, measurement, and analysis has influenced us to serve fewer people but serve them more deeply and more holistically. We used to be very output-driven. My annual goal was to increase placements by 5% and to increase the number of people we serve by 5%. We really saw the average wage dropping, we saw the employment retention numbers dropping. Moving forward, we are measuring our success in self-sufficiency and long term sustainable living wage employment."


Data in the learning ecosystem flows from the worker to providers, and then to government and private funder organizations.

an italic i on a blue circle = click or tap for more info

The Current Data Ecosystem All data goes out from a worker and to various stakeholders.


  1. Frontline workers are often served by many different training and educational organizations
  2. Workers interact directly with one or more of these organizations, sometimes simultaneously
  3. Data are rarely shared between providers. Data rarely flows back to providers from state or federal government agencies
  4. Data are not shared back with the frontline workers

"I think one of the barriers learners face when trying to access services is knowledge of the system."


"There is a lot of fragmentation, but I think it's time to take all of that fragmentation and bring it together into some generally accepted, harmonized model aligned to industries' needs."


Overall, technology products, workforce development systems, and policies and practices that define, collect, store, and report data do not make data sharing easy.

The Challenges

The Legal and Regulatory Challenge

Privacy and security regulations can be a barrier to sharing due to a general lack of understanding among stakeholders.


  1. Understanding which data are needed for what purposes should be the first step
  2. Regulations themselves do not always prevent data sharing
  3. Stakeholders, especially providers, worry about violating data privacy regulations; they don't want to get in trouble
  4. Employers are hesitant to share proprietary and performance data, but willing to share data concerning competencies required to succeed in a job
  5. Creating policies and procedures for sharing data across stakeholders is key

"I think that there's fear in big data. I think the biggest concern is, Who will have access to the information? Is it EOC compliant [or] OFCCT compliant? Who's going to have access to this information?"


"We have audits regularly on our data and how we're protecting it. You have to work through the legalities of sharing it—who has it, when should they have it, who shouldn't have it—[with] multiple, ongoing conversations. Some specific regulations make it difficult to collect and access data."

Government Agent

The Incentive Challenge

The policies and practices surrounding funding and data governance discourage collaboration and data sharing, rather than incentivize it.

The Incentive Challenge Incentive structures, lack of awareness and understanding, funding models, and data governance rules can lead to a lack of collaboration.
The Incentive Challenge Incentive structures, lack of awareness and understanding, funding models, and data governance rules can lead to a lack of collaboration.


  1. Current funding models generally keep stakeholders siloed
  2. Funds are lacking to invest in sophisticated technology systems needed for data collection, analysis, and sharing
  3. Practices and policies around data governance vary greatly across states and stakeholders
  4. Policies do not yet give preference to ownership of data by the individual
  5. No obvious incentives for employers to share data outside of their organizations

"A lot of it is getting everyone involved on the same page about what we are really trying to do, who gets to use which data and for what purpose, and how are we going to make sure we keep each other in the loop so it still gets executed like a collaborative program as opposed to everybody going off in their own direction."

Government Agent

"There are so many different types of data. Some people think we should share data, but the question of which data and for what purpose is rarely answered coherently. Everyone kind of conceptually agrees that we should all share our data. That's the easy part. The harder part is, to what end?"

Workforce Board

The Technical Challenge

The technology products and systems used by stakeholders to collect and report data do not “talk” to each other, making the seamless transition and sharing of data between those systems difficult.

an italic i on a blue circle = click or tap for more info

Employer Systems an italic i on a blue circle

Employee performance systems and employer training systems are not communicating.

Government Systems an italic i on a blue circle

Federal reporting systems and state reporting systems are not communicating.

Provider Systems an italic i on a blue circle

Assessment systems, proprietary systems, spreadsheets, instructional tech products, case management systems, learning management systems are not communicating.


  1. Separate, siloed systems with manual and duplicate data entry
  2. Data exists in various formats
  3. No standard data definitions exist across systems and stakeholders
  4. No technical APIs exist to enable seamless data sharing
  5. Funding for building and maintaining robust systems is inconsistent and short-term

"Most of the data collection systems are unique for each of our services. We do not have one for all of the services our nonprofit offers."


"Even on individual data, though there is some standardization, there is a lot of variability in definitions as they are applied for each variable. You know, what does ‘enrollment' mean, what does ‘completion mean'? There's a lot of variability."


No Common Language and Technical Standards

While there are existing efforts to develop data standards around education and learning (IMS Global, Ed Fi Alliance, CEDS), to date few have focused on developing a set of standards and APIs that allow data to be seamlessly imported and exported between products/systems in the workforce development learning ecosystem.

However, one new effort by the T3 Innovation Network seeks to promote collaboration, interoperability, and harmonization among standardization initiatives related to the credential ecosystem by cataloging, sharing, and mapping data models, standards, and schema. The work will produce a common set of rules for the way data is described and recorded so that it can be more easily shared, exchanged, and understood by computers and online applications.

The Benefits

Stakeholders know that there is power in data-driven decision making and there are benefits to sharing data.

Funders / Government

A skyscraper and the Capitol building

Improved investment decisions


A small shop

Improved talent pipeline


A school

Yields improved programs and services

Worker / Learner

A groupd of frontline workers

Increased agency and informed decision making


  1. Providers want data to make informed decisions about programs and services
  2. Data will help stakeholders better understand and improve program impact
  3. Data sharing and collaboration will lead to more precise and personalized interventions and services
  4. Employers believe data sharing and collaboration will result in powerful talent pipelines

"I think there's a lot of energy around the use of workforce data. I am interested in seeing how we can all work together versus all of us having our siloed approaches to it. Ultimately it comes down to facilitating or brokering relationships and data sharing."


"Now we have six organizations that we are building sustainable, ongoing, engaged partnerships with. In those partnerships, we are sharing data about career progression, retention rates, completion rates and anything that we're able to share with respect to the employee or new hire experience to make informed decisions that better serve our clients."


The Emerging Systems

Data sharing systems and collaborations are beginning to emerge.

A worker using data sharing systems

Data Sharing Systems

  • Match identified, individual data from State, Federal, Provider, and sometimes Employer systems
  • APIs for data import / export
Workers discussing how to share data

Data Sharing Collaborations

  • Localities collaborate to achieve shared vision
  • Requires development of data sharing processes and MOUs/MOAs
  • Share individual data across platforms when applicable

Case Studies

a house


Read how the South Lake Tahoe, CA community is sharing data to empower their frontline workers.

Two hands shaking


Check out the cross-sector, data-driven process Rhode Island developed to achieve its statewide workforce development goal.

a small shop


Learn from the strategies deployed by healthcare industry employers to improve their talent pipeline.

Getting from Here to There

Data is flowing out from the worker to stakeholders.

Here: Disconnected Learning Ecosystem

  • Silos across organizations, and technology systems
  • Plethora of data, insufficient analysis and understanding.
  • Data not in the hands of the worker/learner
  • Lack of understanding around policy and security regulations
  • Lack of common language and technical standards around data
  • Inconsistent funding
Data is flowing both in and out from the worker to types of data instead of stakeholders.

There: Worker-Centered Ecosystem

  • Workers/Learners can access their data to inform their decisions
  • Program/Providers can deliver more effective, personalized services
  • Employers can improve their talent pipeline
  • More understanding around policy and security regulations
  • Funders/Government can make informed investment decisions

Next Steps

To move toward a more data-driven, collaborative, worker-centered learning ecosystem we have six key recommendations:

1. Create awareness and demand among stakeholders

While most stakeholders are encountering many of the challenges outlined above, there is limited awareness around the benefits of data sharing and little demand for systems and policies that enable data interoperability.


We Recommend: Building on the work of Project Unicorn, create an awareness campaign directed at providers, funders, and employers that promotes the benefits of using and sharing data, provides resources that deepen understanding, and actively calls on stakeholders to demand systems and policies that enable data interoperability.

2. Ensure equity and inclusion for workers/learners through access and awareness

Key to the empowerment of the worker/learner is ensuring that they have access to their own data, know how to interpret that data, and know how to use it to make decisions about their own learning pathways.

3 arms reaching in, their hands are on top of each other

We Recommend: Develop a pilot project, similar to the work happening in South Lake Tahoe, CA, in which workers/learners are purposefully granted access to their own information and data, are coached on how to access, interpret and use that data to achieve their own goals, and are provided opportunities to make decisions and pursue self-driven pathways. Broad communications of the findings from this pilot would inform strategies to provide access and to educate workers about data use, and provide insights on the outcomes and results of self-driven pathways.

3. Create data sharing resources

Stakeholders beginning their collaboration and data sharing efforts would benefit from access to resources to help manage through the process, in particular around data privacy, security and data governance.

two speech balloons

We Recommend: Create a repository of resources, similar to the forthcoming WorkforceEdTech tools repository, including those related to privacy and security (MOUs, data governance documents), vendor and system evaluation rubrics, and case studies of successful examples of data sharing and collaboration among stakeholders.

4. Advocate for data standards

Many stakeholders point to the need for common definitions and language used to describe the many types of data being collected and reported as well as a way to seamlessly import and export data between systems to reduce the need for duplicate, manual entry. Data standards and APIs for this learning ecosystem are non-existent or incomplete.

a column chart inside of a donut chart

We Recommend: Join with standards groups including IMS Global, EdFi Alliance, and T3 Innovation Network to include key data types, data standards and APIs used in the adult and workforce learning ecosystem. Advocate for the use of those standards and APIs in the systems used by states, communities, and providers to enable data import and export by developing RFP language and incentives for participation.

5. Advocate for policies and incentives

Current policies and funding models do not promote or incentivize data sharing across stakeholders. In fact, many policies seem to propagate a siloed approach. The learning ecosystem needs to understand what kinds of policies and incentives might encourage a more open data sharing environment.

The Capitol building

We Recommend: Building on the work initiated in the state of Rhode Island, develop a pilot project at the state level to find ways to incentivize cross-sector stakeholders to collaborate and share data to build effective career pathways. The project would look to understand effective incentives for each stakeholder and how those might translate into formal policy.

6. Spur the creation of technology systems that enable data sharing/interoperability

The landscape analysis revealed a shortage of technology systems that enable any kind of data sharing, even as simple as data import/export. The future of data interoperability relies on creating the systems that can help stakeholders collect, report, and use data to make decisions and create more effective learning pathways for frontline workers.

Two computers with lines connecting them

We Recommend: In addition to including vendors as targets for the awareness campaign, work with edtech and workforce tech accelerators, incubators, and investors to encourage the development of technology that not only helps collect data, but also enables data sharing across stakeholders and systems.

One more recommendation: Create/Spur a national conversation

A connected learning ecosystem will require identification of and collaboration between a diverse set of stakeholders, breaking down the silos that promote redundant and disconnected workforce development programs.

We Recommend: Create a consortium of national leaders and advocacy groups focused on workforce development, data sharing, data interoperability, and data security and privacy to establish guiding principles, best practices, resources for improving understanding, and to make progress on the six recommendations above. A key mandate for the consortium should be supporting equity and inclusion across the ecosystem. In addition, the consortium will also multiply impact through communication among each organization's constituents.

Get Involved

a column chart inside of a donut chart

If you are interested in Data Standards, check out the work the T3 Innovation Network is doing.

The Capitol building

If you are interested in Data Governance and Privacy, check out this guide from the National Neighborhood Indicators Partnership.


If you are interested in updates about the national consortium, .

Thank you to our advisory board members for their guidance and support throughout the project.

  • Kimberly Admire, Human Resources Consultant
  • Josh Copus, Entrepreneur in Residence, JFFLabs
  • Brenda Dann-Messier, Commissioner of Postsecondary Education, Rhode Island
  • Matt Gee, Senior Research Scientist, Center for Data Science and Public Policy, University of Chicago; CEO, BrightHive
  • Sonali Kothari, COO, JFFLabs
  • Mark Leuba, Vice President of Product Management, IMS Global Learning Consortium
  • Darin McCloy, Cyber Security and Information Technology Consultant
  • David Miller, CEO, LiteracyPro
  • Stephen Reder, Professor Emeritus, Portland State University
  • Jen Vanek, Director of Digital Learning and Research, World Education
  • Gwenn Weaver, Independent Consultant
  • Stephen Yadzinski, Managing Director, Acceleration, JFF

Thank you to the Walmart Foundation for their generous support.

The research included in this report was made possible through funding by the Walmart Foundation. The findings, conclusions, and recommendations presented in this report are those of Digital Promise alone, and do not necessarily reflect the opinions of the Walmart Foundation.

Sign Up For Updates! Email icon

Sign up for updates!