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As more L&D teams start implementing skills-based learning within their organizations, they encounter two significant challenges that require the development of a skills ontology.
First, how can learning and development start closing the skills gaps within their organization?
Second, how can they map out career paths with upskilling initiatives that align with the needs of employees and the organization?
This article defines a skills ontology and explains how implementing this framework empowers you to identify skills gaps, map out career paths, and align your upskilling initiatives with your employees' needs.
We also outline the six best practices for creating a skill ontology and explore two AI-powered solutions to automate the busywork of getting your ontology up and running.
For L&D teams looking to close the skills gaps in their organizations, a skills ontology is the critical first step to implementing skills-based learning to its maximum effect.
A skills ontology is a structured framework that organizes and categorizes skills, their interconnections, and their relationship to various roles within an organization. Your skills ontology will be like a neural network that adapts as the skills within your organization and externally evolve.
Here are the key features that make up a skills ontology:
A skills ontology and a skills taxonomy are critical frameworks for skills-based learning. Although they are distinctly different, they can significantly complement each other.
Your skills ontology is a dynamic, living employee skills ecosystem that details the relationships between skill sets and their relevance to the job roles within your organization. In comparison, a skills taxonomy is a static framework that organizes skills into a structured hierarchy to identify the skills levels within your organization.
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A skills ontology will provide a complete picture of your organization’s workforce skills and their interconnections, empowering your organization to make more strategic talent management decisions.
Skills ontologies support talent development through continuous learning and development, providing a roadmap for employee growth. A skills ontology will also facilitate internal mobility and upskilling or reskilling the existing workforce, and help you impact your organization's retention rates.
Furthermore, these data-driven insights into existing skills and shortages will enhance talent acquisition by identifying and defining the right skills needed for new hires, making the recruitment process more efficient and targeted toward business needs.
To get started, you need to clearly define the purpose for creating your skills ontology and the specific goals you wish to achieve.
For example, many organizations today are leveraging a skills ontology to improve talent decisions, identify skills gaps, or enhance employee development.
Next, your stakeholders understand the specific skills required in the workflow that are critical to your organization's business needs.
Engage with line managers, team leaders, and employees to help you build the skills data you need and how those skills are interconnected in the context of your organization. You can also use existing data from job descriptions, performance reviews, resumes, and employee surveys to build up the datasets for your ontology.
Creating a skills ontology can be overwhelming, so you should consider leveraging the right AI-powered tool to help you create and manage your skills ontology.
AI-powered tools will automate the process of identifying and defining the skills to include in your initial ontology. The tool will then automatically update your skills ontology, making management quick and easy in real-time to keep your ontology current and relevant.
Your skills ontology will need to map out the relationships of individual skills with different skills, job roles, and departments.
Within the context of your organization, you will need to build up the following skills interconnections:
Next, you need to define each skill's proficiency level, relevant experience, and required certifications.
Skill proficiency is critical to a skills-based learning approach. Take the time to ensure that the skills and proficiency levels are defined in natural and plain language so employees, stakeholders, and your team can quickly and easily understand them.
You will also need a system to track and assess these proficiencies. For example, a solution with a real-time skills dashboard will empower you to keep a pulse on your organization’s skills.
You should consistently iterate and update your skills ontology to include new skills as they arise and change skill-to-skill, skill-to-role, and role-to-role relationships.
Engage employees, managers, and other key stakeholders regularly to update your skills ontology through real-world applications and changing business needs.
Developing and deploying your skills ontology doesn’t have to be daunting if you leverage the right solution. We’ve got you covered with the following two solutions to help you get your skills ontology up and running and future-proof your skills-based strategy today.
With Skills by 360Learning, AI does all the busywork when mapping your skills data and deploying your upskilling campaigns at scale.
Automation makes identifying skills gaps timely and seamless. Our solution gives you and your team visibility on skills per employee or team through an AI-powered skills ontology, assessments, and a Skills dashboard. You can guide employees with the right, personalized upskilling campaign from here.
With collaborative learning that leverages subject-matter expert knowledge to train employees on proprietary skills, you and your team can ensure the right upskilling initiatives are recommended to the right learners.
Skills by 360Learning also empowers you to upload your existing ontology, or you can leverage SkillsGPT to generate a draft ontology for your organization quickly.
Get started on your skills-based learning strategy with Skills by 360Learning
We developed SkillsGPT for anyone looking to start or streamline their skills-based learning approach.
SkillsGPT can help you draft a skills ontology by:
SkillsGPT will also speed up your skills ontology startup process. Instead of months or years, you can generate your draft ontology in minutes so you can head into your stakeholder conversations with 80% of the dull work done. From here, you can tweak the final 20% to match your organization’s context and upskilling needs.
1. What is the difference between skills taxonomy and skills ontology?
2. How can a skills ontology help in workforce planning?
3. What role does artificial intelligence play in skills management?
A 30-minute overview of the platform
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