The ten T3 Network pilot projects are organized into four specific areas of focus. Each pilot project is identified below with an outline of that project team's specific work tasks.
Open Data Standards
Promote technical standard harmonization and interoperability to support competency data exchange, worker/learner records, and digital identities and its use by government entities.
PP1: Data Standards Harmonization
Lead: Jeanne Kitchens, Southern Illinois University
Stuart Sutton, Sutton & Associates Consulting
Support: Natalie Evans Harris, BrightHive
Work Tasks
1.1 Formalize and expand the Standards Mapping Group to address standards harmonization across the public-private talent marketplace (e.g. state and federal agencies, T3 Network, and participants in PP4) for both search and discovery and system to systems data transfer.
1.2 Develop a standards harmonization process and platform (TBD if platform needs to be built or provided as a service by an outside partner).
1.3 Develop two-year work plan starting with T3 Network mapping and harmonization priorities related to competency data (PP5); employment and earnings records (PP2); comprehensive learner records (PP3); and personal identifiers and related protocols for linking individual-level employment and learner records (PP9) as well as self-sovereign data management (PP10).
1.4 Conduct data standards mapping and harmonization work based on the work plan.
1.5 Develop a plan for long-term sustainability and how it will be governed and managed.
PP4: Public-Private Standards Development and Use by Government
Lead: Bob Sheets, The George Washington University
Natalie Evans Harris, BrightHive
Support: Andrew Reamer, The George Washington University
Work Tasks
4.1 Review current policies and practices for, and barriers to, developing and adopting consensus-driven public-private data sharing standards. Draft a set of guiding principles and processes. Identify potential pilot opportunities for testing them (PP1, PP2, PP3, PP9).
4.2 Conduct meetings with standards bodies and federal and state agencies to review and revise the draft guiding principles and processes. Explore how federal and state agencies can promote and/or participate in one or more pilot opportunities.
4.3 Conduct follow-up meetings to review pilot projects and their implications for revising guiding principles and processes.
4.4 Meet to finalize draft guiding principles and processes and discuss recommendations for federal and state implementation.
4.5 Prepare a report on guiding principles and processes as well as recommendations for implementation.
Comprehensive Learner & Worker Records
Identify gaps in and develop public-private standards for employment records, earnings records, and learner records. Explore and promote adoption of enhanced public-private standards by federal and state entities.
PP2: Employment & Earnings Record Standards
Lead: Bob Sheets, The George Washington University
Andrew Reamer, The George Washington University
Work Tasks
2.1 Develop public and private stakeholder use cases and standards for employment and earnings records. Use cases will include applications of an enhanced Unemployment Insurance (UI) wage record and applications requiring the linkage of an individual's employment and earnings records with learner records (PP3 and PP9).
2.2 Work with employers, HR technology vendors, and states to identify the benefits and cost of implementing enhanced UI wage records based on the public-private standards, including impact on state UI data systems (Note: This only focuses on UI wage record applications of public-private employment and earnings record standards).
2.3 Develop guidance and a plan for promoting the adoption and implementation of public-private standards for enhanced UI wage records.
2.4 Promote employer, state, and federal adoption and implementation of public-private standards for enhanced UI wage records.
2.5 Develop a plan for exploring additional public and private use cases and applications of a comprehensive employment and earnings record standard.
PP3: Learner Record Standards
Lead: Natalie Evans Harris, BrightHive
Jeanne Kitchens, Southern Illinois University
Support: Stuart Sutton, Sutton & Associates Consulting
Work Tasks
3.1 Develop public and private stakeholder use cases including search and discovery and system to system data sharing between employers; education, training, military, and credentialing organizations; federal and state agencies; and public-private data collaboratives.
3.2 Facilitate cross-collaborative work among data standards bodies (i.e. learner records, HR, and government) to harmonize and address gaps in data standards including individual identifiers needed to match individual records (PP9).
3.3 Harmonize and fill gaps in public-private standards for learner records necessary to address stakeholder use cases.
3.4 Pilot test the harmonized and enhanced standards with T3 Network partners with a focus on system to system data sharing between employers and their education, training, military, and credentialing partners.
3.5 Coordinate with PP4 to promote the use of harmonized and enhanced standards by federal and state data reporting systems and national and state data collaboratives to reduce the burden of inconsistent data requirements.
Open, Shared Competency Infrastructure
Develop guidance and tools to author, curate, translate, and distribute open and shared competency and skill statements across all stakeholders in the talent marketplace.
PP5: Competency Data Exchange
Lead: Tom Plagge, BrightHive
Stuart Sutton, Sutton & Associates Consulting
Support: Matt Gee, BrightHive
Jeanne Kitchens, Southern Illinois University
Work Tasks
5.1 Organize an open, shared competency data collaborative in cooperation with PP6, PP7, and PP8.
5.2 Develop guidelines and protocols for publishing competency data as open linked data and support search and retrieval between linked repositories.
5.3 Enhance and harmonize competency data standards needed to implement guidelines (coordinate with PP1).
5.4 Develop tools for publishing and maintaining open licensed competency frameworks that support systematic discovery and translation between alternate data formats.
5.5 Pilot test publishing tools (coordinate with PP6).
5.6 Promote widespread adoption and use of publishing tools.
PP6: Competency Analysis and Translation
Lead: Matt Gee, BrightHive
Tom Plagge, BrightHive
Support: Stuart Sutton, Sutton & Associates Consulting
Work Tasks
6.1 Organize an open, shared competency data collaborative in cooperation with PP5, PP7, and PP8.
6.2 Through the open, shared competency data collaborative, develop a plan for contributing and developing open AI tools to begin analyzing, translating, and comparing competencies.
6.3 Determine and provide access to the necessary competency and contextual data to support the development of AI algorithms.
6.4 Develop openly licensed AI algorithms best suited for interpreting, aligning, writing, and generating competencies along with defining necessary metrics for training given defined tasks and use cases.
6.5 Pilot test openly licensed AI algorithms (contingent on additional funding).
6.6 Work with user groups (PP8) to establish feedback and testing mechanisms.
PP7: Learning Outcomes Exchange
Lead: Matt Gee, BrightHive
Jeanne Kitchens, Southern Illinois University
Support: Tom Plagge, BrightHive
Work Tasks
7.1 Organize an open, shared competency data collaborative in cooperation with PP5, PP6, and PP8.
7.2 Identify learning outcome use cases through the data collaborative.
7.3 Develop a report and propose pilot applications for a learning outcomes exchange, including applications for curriculum development, articulation, and improving career and educational pathways.
PP8: Public-Private Use of Open Competency Data
Lead: Jeanne Kitchens, Southern Illinois University
Andrew Means, BrightHive
Work Tasks
8.1 Organize an open, shared competency data collaborative in cooperation with PP5, PP6, and PP7.
8.2 Through the open, shared competency data collaborative, establish a group to identify users, technical barriers, and use cases for open, linked competency data.
8.3 Promote competency data and IT tool adoption by government agencies and private sector vendors to generate new analytics.
8.4 Develop inventory of users and leading analytical applications.
Linked, Individual Level Data
Design protocols for empowering individual workers and learners with their data through new and emerging self-sovereignty technologies, such as distributed ledger and block chain.
PP9: Data Collaboratives for Individual-Level Data
Lead: Matt Gee, BrightHive
Tom Plagge, BrightHive
Support: Bob Sheets, The George Washington University
Work Tasks
9.1 Establish a work group composed of federal and state government agencies; public-private data collaboratives; employers; education, training, military, and credentialing organizations; and related standards organizations to develop protocols for accessing, matching, and using individual-level employment and learner records.
9.2 Develop protocols for using individual identifiers for matching and linking individual records and their implications for employment and earnings (PP2) and comprehensive learner records (PP3).
9.3 Develop protocols (e.g. secure multiparty computation) for accessing and using linked individual employment, earnings, and comprehensive learner records for policy research and evaluation, consumer information systems, and workforce data analytics.
9.4 Promote the use of these protocols in both the public and private sectors (PP4).
PP10: Empowering the American Student and Worker
Lead: Matt Gee, BrightHive
Support: Bob Sheets, The George Washington University
Stuart Sutton, Sutton & Associates Consulting
Work Tasks
10.1 Form a work group of stakeholders familiar with relevant data standards and individual-level data protocols to develop a plan for developing and testing protocols for self-sovereign data management.
10.2 Review and analyze related initiatives in other sectors (e.g. healthcare) aimed at individual record management and promoting the adoption and use of existing data exchange standards with a new consumer-driven use case.
10.3 Develop open, self-sovereign protocols and sandboxed data infrastructure. Then develop pilot projects with partners.
10.4 Conduct pilot projects (contingent on additional funding).