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As per a Gartner survey, 53% of respondents stated that one of the biggest roadblocks to building a digital workforce is the inability to identify crucial skills. Surprisingly, despite this evident need, only 10% of HR executives believe they possess the capability to define and organize the skills into a skills taxonomy or framework.

As companies navigate through the challenges posed by technological advancements, changing market dynamics, and shifting workforce expectations, having a clear and comprehensive classification of skills is crucial. 

By systematically categorizing and understanding the diverse workforce skill set, businesses can make informed decisions, foster targeted growth, and bridge skills gaps to build a skilled and adaptable workforce for the challenges of tomorrow. This categorization of skills is termed a skills taxonomy.

A skills taxonomy serves as a strategic roadmap, guiding organizations in their talent acquisition, employee development, and overall human resource management efforts. 

In this blog, let’s understand what exactly is skills taxonomy, what are the key elements of it, and how to implement it effectively to address skills challenges.

What is Skills Taxonomy?

A skills taxonomy is a structured framework that categorizes and organizes various skills into specific groups. 

This taxonomy enables clear communication about skills, aids in matching job requirements with candidate profiles, and facilitates targeted skill development initiatives by enhancing skill visibility.

An increase in skills visibility empowers you to enhance your workforce planning by recognizing emerging knowledge gaps. This insight also highlights the essential competencies that will play a pivotal role in upskilling or recruitment in the upcoming years. 

Elements of Skills Taxonomy

Skills Hierarchy: Skills hierarchy refer to the arrangement of skills in a structured order based on their complexity, specialization, or importance. Skills are organized in a way that reflects their relationships and dependencies. 

For instance, within the realm of programming, a skill hierarchy might start with basic coding skills and progress to more advanced concepts like algorithms and software architecture. This hierarchy helps employees and organizations understand the logical progression of skills and plan their learning or training accordingly.

Skills Categorization: Skills categorization involve grouping similar skills together based on their shared characteristics or functions. It helps in simplifying the understanding of skills by creating broader categories. 

For example, programming languages, database management, and web development could be categories within the broader technical skills domain. Categorization makes it easier to assess and manage skills, especially when dealing with a wide range of competencies.

Skill Descriptions: Skill descriptions provide detailed explanations of what each skill entails. They clarify the specific knowledge and abilities associated with a skill. 

A skill description for "Data Analysis" might include proficiency in statistical software, the ability to interpret data trends, and the skill to communicate insights effectively. These descriptions ensure that everyone has a clear understanding of what they need to be proficient with under a particular skill, promoting better communication and expectations.

Skills Mapping: Skill mapping involves connecting skills to relevant roles, tasks, or responsibilities within an organization. It helps you identify where specific skills are needed and provides insights into potential skills gaps. 

By mapping skills to job roles, businesses can make informed decisions about recruitment, training, and workforce planning. For example, a skill map might show that "Project Management" is crucial for roles like team leads or project managers.

These concepts work together to create a structured framework for understanding, organizing, and utilizing skills within individuals and organizations. 

A skill hierarchy guides skill development, categorization simplifies the process, skill descriptions provide clarity, and skill mapping aligns skills with organizational needs.

Why do organizations need to have skills taxonomy?

Having an effective and impactful skills taxonomy is crucial for organizations for the following reasons:

Streamlined Hiring Process: A skills taxonomy categorizes required skills for specific roles, helping recruiters identify suitable candidates efficiently. Aligning candidates' skills with job requirements can help you save time and resources during candidate screening and selection.

Assessing Skill Gaps for Improvement: A skills taxonomy helps identify gaps between current employee skills and those needed for optimal performance. This insight will guide you for targeted training and development initiatives, improving workforce performance and overall productivity.

Performance Evaluations: Skill-based assessments facilitated by a skills taxonomy provide objective criteria for evaluating employee performance. This data-driven skills-based approach will help you plan for fair appraisals and offer actionable insights for career growth and skill enhancement of your workforce. 

Let’s take an example where a company evaluates specific skills of the employees in its marketing team for performance review — a content creator's ability to generate engaging articles, a data analyst's capacity to interpret marketing metrics, or a social media manager's effectiveness in driving engagement.

By focusing on skills, the company reduces biases and subjectivity in evaluations, thereby shifting the emphasis from random traits to concrete capabilities, fostering a more equitable review process.

Better Talent Management: With a clear skills framework, organizations can strategically place employees where their skills are best utilized. This helps you with better resource allocation, and improved employee satisfaction, and ultimately contributes to the organization's success.

Common challenges in implementing skills taxonomy

Defining skill categories: Skills taxonomy requires a clear and consistent definition of skill categories that reflect the organization’s needs and goals. However, different stakeholders may have different perspectives and preferences on how to classify and label skills, which can lead to confusion and inconsistency. Moreover, skill categories may change over time as the organization evolves and adapts to the changing market conditions and customer demands.
Skill granularity: Establishing a balance between the level of detail and the level of abstraction of skills is an integral part of skills taxonomy. Too much detail can make the taxonomy too complex and cumbersome to use, while too little detail can make the taxonomy too superficial and irrelevant to the specific roles and tasks. Finding the optimal level of granularity can be challenging, as it depends on various factors, such as the size and diversity of the organization, the nature and scope of the work, and the availability and quality of data.

Integration with existing systems: A seamless integration with the existing systems and processes of the organization, such as learning management systems, talent management systems, performance management systems, and recruitment systems is also required. However, integrating skills taxonomy with these systems can be difficult, due to differences in data structures, formats, standards, and protocols. Moreover, integrating skills taxonomy with these systems may require significant changes in the workflows, policies, and practices of the organization.
User adoption: Skills taxonomy requires a high level of user adoption and engagement from both employees and managers. However, users may resist or ignore skills taxonomy if they do not understand its benefits, value, and relevance to their work. Moreover, users may lack the motivation, confidence, or ability to use skills taxonomy effectively, especially if they do not receive adequate training, support, or feedback.

How to implement skills taxonomy in your organization?

Assessment and Planning: Evaluate your organization's needs and goals for the taxonomy. Identify key skill areas and assess current skill gaps. An AI- powered platform like Disprz LXP can help you with skill identification, assessment, skill building and measuring the impact on ROI. 

Skills Categorization and Hierarchy: Create a structured taxonomy by categorizing skills into relevant groups (e.g., technical, soft skills). An advanced platform like Disprz AI - powered platform can help build a relevant skills taxonomy specific to your department and teams, segregate skills into proficiency levels and establish hierarchical structure to show relationships between different skill levels, aiding in clear progression.

Decide on Technology and Tools: Choose suitable technology and tools to build and manage the taxonomy. Consider platforms or software that facilitate easy navigation, updates, and integration with other systems. Platforms like Disprz LXP can help you with seamless integration with existing systems, ease of customization of skills with changing market standards.

Integrating with HR Processes: Seamlessly integrate the taxonomy into HR processes like recruitment, performance evaluations, and training plans. This enhances talent management and decision-making.

Communicate and Train: Communicate the taxonomy's importance, benefits, and usage to employees. Provide training on how to navigate and understand the skill levels, fostering a shared understanding.

Monitor and Adapt: Continuously monitor the taxonomy's effectiveness and relevance. Regularly update it to reflect evolving skill requirements and business needs.

Conclusion

Implementing a well-structured skills taxonomy is a crucial step in optimizing organizational growth and talent management. By categorizing skills, creating hierarchies, and integrating them into various HR processes, companies can strategically bridge skill gaps, enhance workforce capabilities, and drive innovation. Incorporating AI into skills taxonomy empowers companies to proactively address skills gaps, nurture employee growth, and stay ahead of industry transformations. 

By embracing AI-powered platforms like Disprz LXP, organizations gain a competitive edge, ensuring their workforce is equipped with the right skills for success in an ever-evolving business landscape.

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