Why Skills-Based Hiring Needs Shared Data Standards
This is part one of a series on Unicon’s ed-to-workforce leadership and initiatives.
The skills-to-workforce problem is under-organized.
For years, higher education, employers, and technology providers have been circling the same question: how do we connect what learners know with what employers need?
So far, the answer has mostly been fragmentation with fancy branding: a badge here, a learner record there, a credential wallet somewhere else. Plenty of promise but not enough shared infrastructure.
As AI reshapes the labor market, the gap between education and employment is getting harder to excuse. Learners need a clearer way to show what they can do. Employers need a better way to find the talent they need. Institutions need stronger signals about how skills translate into opportunity.
At Unicon, we believe the next step is shared infrastructure. Not another discrete platform or tool, but a layer that connects data from educational institutions and employers to match learners with career opportunities on a large scale.
The Missing Layer: Shared Governance Language
The primary obstacle is unifying data governance.
More specifically, the market lacks a shared data glossary and skills taxonomy. Without shared language, the market can’t articulate a graduate’s capabilities in a way employers can use. With definitions in place, we can map those skills from coursework or work experience to the job marketplace.
But shared language requires shared data, and shared data requires trust. For institutions and employers, that has historically been the hardest part.
For employers, sharing data can mean exposing where they have talent shortages, which roles they struggle to fill, or how their hiring requirements differ from the skills they actually use on the job. For institutions, it can mean opening academic programs to closer comparison against employer demand.
Both sides are being asked to take on near-term risk for long-term value. These are often bigger barriers than the technology challenges.
Building a New Standard
Standards adoption is most successful when there are regulatory requirements for data sharing. The widespread adoption of Ed-Fi in the K-12 space is an excellent model for large-scale data standardization.
Individual school districts once operated in data silos, making it nearly impossible to share information easily. States began adopting the Ed-Fi standard and requiring districts to conform to it. The catalyst for change was not a shift in technology, but a shift in policy.
Skills-based education and hiring need a similar push. An open standard for skill documentation would give institutions, employers, and technology providers a common way to describe, compare, and exchange skills data. It would also give learners a better chance of carrying their achievements across platforms, jobs, and economic changes.
The web offers a helpful comparison. HTML worked because it created a shared structure flexible enough to evolve over time. A skills standard needs that same balance: specific enough to be useful, flexible enough to last for decades.
This will not happen by waiting for perfect consensus. The field needs leaders willing to define the standards, test the model, and invite others to improve it. At Unicon, we’re prepared to step into that role.
Our Vision for Skills at Scale
Connecting talent with opportunity should not depend on proprietary platforms, opaque algorithms, or commercial gatekeepers.
Tools like LinkedIn, Indeed, and Glassdoor help employers and job seekers find each other. While these tools are useful, they also make visibility dependent on platform-controlled systems: algorithms, paid promotion, profile signals, and networks that do not always reflect what a candidate can actually do.
A skills-based hiring ecosystem should communicate more directly, creating a continuous feedback loop that better aligns education, skills development, and workforce demand.
A better system would make skills easier to verify and easier to find. A learner who completes coursework, earns a certification, finishes a project, or gains relevant work experience could carry that achievement in a trusted skills record. An employer looking for candidates with specific capabilities could search against verified skills rather than relying only on degrees, job titles, keywords, or referrals. An institution could see where its programs align with workforce demand and where gaps remain. Over time, that exchange would create a feedback loop among education providers, employers, and learners.
For more than 30 years, we have turned complex systemic challenges into durable infrastructure. Whether we are helping organizations like Axim Collaborative modernize competency-based education through scalable taxonomy governance, or partnering with the Gates Foundation on the Learner Information Framework (LIF) for portable skills data, our focus remains the same: we build the “connective tissue” between education and the workforce to bring skills at scale into reality.
The Market Won’t Fix Itself
The infrastructure for the future of work won’t just happen. It’s a choice we have to make. We must decide whether we want a job market that is increasingly chaotic, disconnected, and dispiriting for employers and job seekers alike, or one built on transparent, standardized, and portable data.
We can create the latter. The tools are available, the model exists, and the need is clear. It is time to build the infrastructure that allows every learner to be discovered and every employer to find the talent they need.
