Fostering Data and Technology Convergence in the Talent Marketplace

April 5, 2018

Takeaways

We know that emerging tech has the power to transform the talent marketplace and drive future innovation.
This work officially launches the T3 Innovation Network work group.

On March 7th over 60 individuals representing 43 organizations convened in a suburb of San Francisco at Workday’s offices to spend the day discussing data, emerging technologies, as well as some of the challenges faced in today’s talent marketplace.

This meeting, the first in a series led as a joint effort between the U.S. Chamber of Commerce Foundation and Lumina Foundation (read about the joint effort), kicked off an eight-month sprint of work. All of the organizations involved, now known as the T3 Innovation Network, are experts on today’s emerging technologies, such as semantic web standards (e.g., linked data), distributed ledger technologies (e.g., blockchain), artificial intelligence, and machine learning. We know that these technologies have the power, if leveraged properly, to transform the talent marketplace and drive future innovation. 

In this first meeting, individuals were broken up into work groups that will convene over these next eight months to develop a series of use cases and pilot projects. The responsibility of these working groups is to design how these emerging technologies can converge and form a new public-private data infrastructure to power the talent marketplace.

A Little Background

Both the Chamber Foundation and Lumina Foundation have made significant investments in supporting employers and learners in the talent marketplace (such as the Talent Pipeline Management initiative, job registry pilot, and Lumina’s investments in Credential Engine and digital learner records), and are dedicated to taking those efforts to the next level.  

In this first meeting, we shared a new framework—one based on interoperability, trust, transparency, and analytics—that aims to break down the technology and data silos on both the demand-side and supply-side that have slowed or hindered responsible innovation to date.

Figure 1: Slide Shared Describing Silos

Figure 1: Current Silos
Figure 1: Current Silos

Figure 2: A New Framework

Figure 2: A New Framework
Figure 2: A New Framework

The Work Groups

As mentioned before, the key to this initiative is the convening of four working groups who will define the most promising use cases of emerging technologies and how they can be bundled together to set up a series of pilot projects over the coming years. The four work groups include:

  • Work Group 1: Stakeholder use cases for achieving breakthrough innovations – This group will identify the most high-impact use cases for end-use customers that improve (1) employer signaling and talent sourcing processes, (2) documentation and transfer of learning, and (3) credential attainment, portability, and connections to employment. The use cases developed by this group will provide the foundation for further development by the more technical work groups that follow.
  • Work Group 2: Exploring sustainable data standards convergence – This group will build on use cases developed by Work Group 1 and identify opportunities for additional data standardization and harmonization that (1) enables greater movement of data across systems and enhances interoperability, and (2) enables improved use and convergence of leading technologies including semantic web, distributing ledger, and machine learning technologies.
  • Work Group 3: Developing and analyzing competencies – Continuing to build on the work of Work Group 1, this group will identify opportunities for improving how competencies are developed, utilized, and analyzed using semantic web standards, machine learning, and translation services.
  • Work Group 4: New architectures and uses of linked individual-level data – Also building on Work Group 1, this group will identify opportunities to deploy new data verification architectures and protocols (e.g., blockchain) that enable (1) learner-centered and learner-empowered records, and (2) improve verification of learning, credentialing, income, and employment history. 

Figure 4: Slide Shared Describing the Convergence of Structured Data and the Use of Machine Translation

Figure 4: Convergence of Structured Data and the Use of Machine Translation
Figure 4: Convergence of Structured Data & the Use of Machine Translation

This is no small task. These work groups will convene virtually and in-person over the next eight months to identify the most promising use cases and report out recommendations for pilot projects. We are thankful to have such great partners in this exploration and we encourage you to get involved. As these working groups meet and move each of their initiatives forward, more information will become available to outline their progress. In the meantime, find some additional background information in our background paper or contact us if you have more questions about this exciting project.