Guide to Skills Ontology - Transform Your Talent Strategy

Introduction

With 45% of CEOs worried about their companies’ future viability, within the next decade, transformation has become a crucial business necessity. However, many executives face a common challenge: uncertainty about their workforce’s current skills and the knowledge they need to acquire. Instead of seeking quick fixes for organizational change, the most effective transformation initiatives originate from within the company. They are based on a clear understanding of evolving skill requirements and how talent can be realigned to address emerging challenges.

As both in-demand skills and internal talent pools are constantly changing, leaders need more than traditional approaches and static technologies. They must leverage advanced AI-powered tools that adapt to evolving skill needs in real-time, which is where skills ontology frameworks become essential.

What is a Skills Ontology?

A skills ontology is a structured framework that organizes and categorizes skills, highlighting their interconnections and relevance to various roles within an organization.

Think of your skills ontology as a dynamic neural network that evolves alongside the changing skill landscape both within and outside your organization.

For learning and development (L&D) teams aiming to bridge skill gaps within their organizations, establishing a skills ontology is essential for maximizing the impact of skills-based learning.

Importance of Skills Ontology

A skills ontology offers a comprehensive view of your organization’s workforce skills and their interconnections, enabling more strategic talent management decisions.

By supporting continuous L&D initiatives, skills ontologies create a roadmap for employee growth. They also facilitate internal mobility, upskilling, and reskilling, positively impacting retention rates.

Moreover, the data-driven insights provided by a skills ontology help enhance talent acquisition by identifying and defining the necessary skills for new hires, making the recruitment process more efficient and aligned with business needs.

Benefits of Implementing a Skills Ontology

Here are some benefits of implementing a skills ontology

Improved talent acquisition

• By clearly specifying the skills needed for each role, organizations can attract candidates who are better suited to those positions.

Practical skills development

• Ontologies can inform training programs by identifying the specific skills required for various roles.

Enhanced career development

• Individuals can use ontologies to pinpoint skill gaps and map out their career paths.

Better workforce planning

• By analyzing skills data through ontologies, organizations can anticipate future skill requirements and make well-informed talent decisions.

Difference between Skills Taxonomy Vs Skills Ontology

Both skill taxonomies and ontologies are methods for organizing and classifying skills, but they differ in detail and flexibility.

A skills taxonomy categorizes skills in a hierarchical structure, starting with broad categories at the top and breaking down into more specific skills below. In contrast, a skill ontology defines connections between skills, moving beyond a simple hierarchical structure.

Skills ontologies enhance our understanding by revealing how different skills relate to one another, helping to identify patterns and relationships that might not be immediately apparent.

Despite their similarities, skills taxonomies and skills ontologies have important differences, as outlined below

How to Create a Skills Ontology

Creating a skills ontology involves leveraging AI-powered software to develop a comprehensive skills strategy.

Here’s how it can make a significant impact:

Benchmark and assess

Generate and report on AI-driven skills profiles, using self and peer assessments to capture skill proficiency, interest, and enjoyment.

Close skill gaps

Systematically deliver learning and training to address organizational skill gaps and meet immediate performance expectations.

Empower development and growth

Provide a personalized, social, and self-directed learning experience to support long-term career paths, interests, and goals.

Enable strategic deployment

Equip HR leaders and managers with the tools to effectively utilize the right skills by considering goals, roles, projects, and gigs, while offering personalized learning and development opportunities.

Challenges and Solutions

Although skills ontologies provide a valuable tool for understanding and managing workforce capabilities, they also have some limitations and solutions, which are outlined below.

Complexity

Creating and maintaining a skills ontology can be complex. It involves meticulously considering the specific skills relevant to your organization and understanding their interconnections. This process can be time-consuming and requires continuous effort, particularly as the skills landscape evolves.

Solution

Adopt a modular approach for your skills ontology by dividing it into smaller, manageable components. This allows for easier updates and adjustments as new skills emerge, making maintenance more efficient and less time-consuming.

Nuance

There is a risk of developing a rigid classification system that only addresses a limited range and depth of skills. For example, the ontology might categorize "data analysis" and "machine learning" as separate entities, overlooking the crucial role that data analysis plays as a foundation for machine learning.

Solution

Incorporate skill relationship mapping to detail how skills are interrelated and support each other. This ensures that foundational skills, like data analysis, are connected to advanced skills, such as machine learning, providing a comprehensive and nuanced understanding of skill connections.

Subjectivity

Defining and classifying skills can involve subjective interpretations, which may lead to inconsistencies and make it challenging to compare skills across different departments or roles.

Solution

Develop and use standardized criteria for defining and classifying skills to reduce subjectivity, ensure uniformity, and simplify comparison across departments and roles.

Maintenance

The world of work is continuously evolving, with new skills emerging regularly. To keep up, a skills ontology must be updated frequently to reflect these changes, which demands ongoing effort and resources.

Solution

Use AI-powered skill discovery tools to automatically identify and integrate new skills by scanning trends and data in real-time. This minimizes manual maintenance and keeps the ontology up-to-date with minimal effort.

No Standardization

There is no universal standard for skills ontologies, which can complicate the sharing of information and collaboration with other organizations that use different systems.

Solution

Adopt interoperable standards like SFIA or ESCO for your skills ontology to ease data exchange and collaboration with other organizations, ensuring compatibility and effective information sharing.

Adaptive Skill Management

Auto-evolving role-skill frameworks provide a flexible, data-driven solution for skill management within a company. These frameworks automatically adjust to changes in skills and roles, offering a more efficient method to ensure that employees possess the necessary skills.

Solution

Integrate dynamic skill gap analysis tools into your auto-evolving frameworks to continuously identify and address skill gaps between current skills and role requirements. This ensures your skill management system stays aligned with organizational needs and supports timely skill development.

Implementing Skills Ontology in Your Organization

1. Define the purpose of your skills ontology

To begin, you should clearly outline the purpose of creating your skills ontology and the specific objectives you aim to achieve. For instance, many organizations use a skills ontology to enhance talent management, identify skills gaps, or advance employee development.

2. Gather skills and insights from stakeholders

Next, ensure your stakeholders comprehend the specific skills essential for your organization's workflow and business needs. Collaborate with line managers, team leaders, and employees to gather the necessary skills data and understand how these skills are interconnected within your organization. Additionally, leverage existing data from job descriptions, performance reviews, resumes, and employee surveys to develop a comprehensive dataset for your ontology.

3. Leverage AI-powered tools

Creating a skills ontology can be daunting, so it’s wise to use an AI-powered tool to assist in its development and management. These AI-driven tools can automate the identification and definition of the skills to include in your initial ontology. They will also continuously update the ontology, ensuring it remains current and relevant while simplifying real-time management.

4. Map skills relationships

Your skills ontology should outline the relationships between individual skills, job roles, and departments.

In the context of your organization, you will need to establish the following interconnections:

• Skill-to-Skill: How each skill relates to other skills.

• Skill-to-Role: How each skill connects to various roles.

• Role-to-Role: How different roles are related to one another.

Define skills and proficiency levels: Next, define the proficiency levels, relevant experience, and required certifications for each skill. Skill proficiency is crucial for a skills-based learning approach. Ensure that the definitions and proficiency levels are described in clear, straightforward language so that employees, stakeholders, and your team can easily understand them.

Additionally, a system should be implemented to track and assess these proficiencies. For instance, a solution with a real-time skills dashboard will enable you to monitor your organization’s skills effectively.

5. Continuous maintenance and updates

Regularly iterate and update your skills ontology to incorporate new skills and adjust skill-to-skill, skill-to-role, and role-to-role relationships as they evolve. Engage employees, managers, and other key stakeholders frequently to ensure your skills ontology reflects real-world applications and evolving business needs.

Conclusion

Implementing a skills ontology is crucial for maximizing talent management, enhancing employee development, and aligning skills with organizational goals. By clearly defining and mapping out skills, organizations can identify gaps and strategically plan for future needs. Disprz's platform offers robust solutions for creating and maintaining an effective skills ontology, ensuring real-time updates and seamless integration.

Discover how Disprz can transform your skills management strategy. Book a demo today to see our platform in action!