Which Systems Should Own Skills Data: ATS, LMS, HCM, Or Something Else?
As more companies pursue a skills-based approach to workforce transformation, one question keeps surfacing: Where should skills data live?
Is it the ATS, where candidate profiles are built? The LMS, where upskilling happens? The HCM, where organizational structures are tracked? Or should it live somewhere else entirely?
Each system has a piece of the puzzle – but none are designed to own the complete picture. If you’re a CHRO or transformation leader trying to build a unified, intelligent view of workforce capability, understanding where skills data belongs is a critical first step.
How does each tool stack up? 🤔
1. ATS: Good for candidate skills, but not the whole story
Your Applicant Tracking System often contains the richest data on external talent. It captures candidate resumes, skills, and experiences, and any information pertaining to the job they applied to. But it’s often:
- Limited to applicants only
- Disconnected from internal employee data
- Focused on point-in-time assessments rather than dynamic capabilities
Verdict: Great for sourcing and external hiring; not built to track skills over time.
2. LMS: Good for upskilling, not for visibility
The Learning Management System tracks course completions and training paths. It knows what learning content an employee has consumed, but it usually lacks:
- Skills not gained through the LMS
- Context on why someone needs a skill
- Visibility into on-the-job application
- Integration with job architecture or performance data
Verdict: Valuable for development activity, but not the source of truth for actual skills.
3. HCM: The system of record (but not a system of intelligence)
Your Human Capital Management platform is the core system of record for employee data, including organizational structures, roles, and – sometimes – skills. But while it's essential for governance and compliance, it typically:
- Lacks the depth to model skills across roles and people dynamically
- Struggles to integrate real-time skills signals from recruiting and learning systems
- Can’t activate skills data for talent decisions like internal mobility or workforce planning
Verdict: A reliable system of record … but it needs a dedicated skills intelligence layer to turn data into action.
The problem: siloed systems, fragmented skills data ⛓️💥
Each of these systems stores valuable (but partial) skills signals. None of them offer a unified view of what skills your workforce has today, where the gaps are, or how to close them. And that fragmentation comes at a cost:
- Duplicate efforts in skills mapping
- Inaccurate matching in internal mobility or hiring
- Disconnected L&D strategies
- Slower, less confident workforce decisions
In a world where skills evolve faster than job titles, these silos become strategic blockers.
The solution: A skills data layer that sits across systems 💡
Rather than trying to force one system to become the “owner” of skills, forward-looking organizations are introducing a central skills intelligence layer – a platform that connects to the ATS, LMS, and HCM, and other HR technologies, and continuously ingests and standardizes skills data from all of them (and feeds the data back in).
This layer:
- Uses AI to extract and normalize skills from resumes, job descriptions, learning records, and performance feedback
- Maps those skills to a shared ontology or taxonomy
- Makes enriched, up-to-date skills data available to every HR system, in context
In other words, the skills layer doesn’t replace your existing tech stack: it enhances it.
In practice, this might look like:
- A recruiter opens a new requisition in the ATS. The platform instantly pulls in the standardized skills for that role and highlights internal and external candidates who are a close match.
- A people manager uses the HCM to view a team’s org chart: and can also see real-time skills insights for each employee.
- An L&D lead in the LMS recommends upskilling based on actual gaps tied to current and future job requirements.
All of this becomes possible when your systems are speaking the same “skills language” – powered by a centralized, AI-driven skills layer.
This helps unify data across systems, making every HR decision more strategic.
The connected skills intelligence layer is really a foundation for transformation. If you want to fill skills gaps – hire faster, reskill efficiently, and unlock internal mobility – you need reliable, real-time visibility into workforce capabilities.
The question isn’t really “Which system owns skills data?”
It’s “How do we make skills data work across all systems, so we can make better decisions everywhere?”