By now, HR leaders, recruiters, and hiring managers alike are familiar with the benefits of a skills-based hiring strategy. It promises improved candidate-job fit, higher retention rates, and a workforce geared for growth. More generally, organizations that leverage a skills-based approach are 63% more likely to achieve results than those that have not adopted skills-based practices.
But before your team can reap those benefits, you’ll need an in-depth understanding of skills within your organization. Enter the skills taxonomy, an adaptable framework that helps organizations articulate and identify the competencies they seek. It’s a structured system that categorizes and defines the range of skills needed within an organization.
But how exactly do you create a skills taxonomy that encompasses hundreds (or even thousands) of skills? And how can you ensure that your taxonomy remains relevant in the fast evolving tech industry? Let’s find out.
Why You Need A Skills Taxonomy
The traditional methods of hiring — relying heavily on job titles and generic descriptors — can often fall short. A role-based approach might not capture the unique blend of skills and competencies a candidate brings to the table or that a job truly demands.
Let’s think about the title “Software Developer.” It’s broad. A developer could be well-versed in Java but a novice in Python. They might excel at back-end infrastructure but struggle with front-end user interfaces. One developer might be an expert in relational, SQL database systems, and another might be a master of nonrelational, NoSQL database systems.
Without detailed skills requirements, it would be hard to find a candidate with the right competencies for this role.
This is where a skills taxonomy can help. It’s a critical component in the toolkit of modern HR leaders and recruiters, ensuring that your skills-based hiring strategy is not only effective but also efficient. Key reasons that companies implement a skills taxonomy include:
- Standardizing Criteria For Roles: By clearly defining and categorizing skills, a taxonomy ensures that everyone — from HR to hiring managers to candidates — is on the same page about what it will take to succeed in each role. This not only simplifies the hiring process but also sets clear expectations for all parties involved.
- Improving Job Descriptions And Recruitment Ads: With a well-defined skills taxonomy in place, you can craft more targeted and precise job listings. This means attracting candidates who align better with your specific needs, thereby increasing the chances of a successful hire.
- Identifying Skills Gaps And Future Training Needs: A taxonomy isn’t just a recruitment tool. It’s a strategic asset that can help identify areas where your current workforce might need upskilling. By tracking which skills are abundant and which are scarce within your teams, you can proactively address potential future challenges.
Steps to Build a Skills Taxonomy
Building a skills taxonomy isn’t just about creating a list of skills. It involves understanding their interrelationships, hierarchies, and required competency levels.
Constructing this taxonomy requires methodical planning, diverse insights, and a commitment to periodic refinement. By following these steps, you’ll be on your way to crafting a dynamic taxonomy that can streamline your hiring, enrich training modules, and more holistically capture the skills landscape of your organization.
Building an insightful skills taxonomy is not a solitary endeavor; it’s a collaborative project that thrives on varied perspectives. As you begin this process, pull together a diverse group from across your organization. These stakeholders should include:
- Tech Leads: Their hands-on experience will provide insight into the fundamental skills and emerging technologies required to work in your tech organization.
- Hiring Managers: They understand where skills gaps often arise in the hiring process, and their inclusion ensures that the taxonomy is anchored in the product and engineering requirements of the organization.
- HR and L&D Teams: Their broader organizational vision will ensure the taxonomy aligns with long-term growth strategies, upcoming training modules, and potential areas of expansion.
Collect Skills Data
To create a comprehensive skills taxonomy, delve into multiple data sources, both internal and external. Start by identifying any pre-existing internal databases or tools that log skills and competencies. Seek active input from your teams through discussions or detailed skill surveys. Complement this internal data with a broad view of the industry, ensuring your taxonomy is both current and forward-looking.
Some of the most valuable sources of skills data include:
- Employee Surveys: Directly sourcing information from your teams will spotlight both common and specialized skills.
- Industry Reports: These offer an overview of the broader tech industry, highlighting evolving skills and trends.
- People Analytics Platforms: These tools can sift through data to unveil prevalent skills and potential competency gaps.
- Performance Reviews: Analyzing past evaluations will underscore skills historically tied to high performance or areas that often present challenges.
Differentiate Between Skills and Competencies
It’s easy to conflate skills with competencies, but for a robust taxonomy, this distinction is crucial. Skills refer to the specific knowledge and abilities a person has. Competencies, on the other hand, encompass the broader application of those skills in various contexts, often tied to behaviors and attitudes. Since a single competency may require a combination of hard and soft skills, it can serve as a useful framework for grouping complementary skills together
Categorize and Group Skills
Once you’ve gathered your data and differentiated between skills and competencies, it’s time to structure all this information. Begin by creating broad categories to capture the nature of skills, be it technical prowess or interpersonal talents. Within these categories, build clusters or groups, focusing on relationships between skills. This clustering will not only make the taxonomy navigable but also offer insights into potential career paths, transferable skills, and interrelated competencies.
Technical Skills: Diving deeper, you might have groups like “Programming Languages,” “Infrastructure Management,” and “Software Lifecycle.”
Soft Skills: Create clusters that might encapsulate areas like “Communication,” “Team Dynamics,” and “Problem-solving.”
Define Skill Levels
Defining skill levels involves recognizing the required depth of proficiency and how it aligns with specific roles. This granularity allows for more nuanced hiring decisions and tailored training plans.
For instance, let’s consider the skill “Database Management”:
- Basic: A software engineering intern might need a basic understanding of databases to land and succeed in their first tech role. But as early career professionals, they might only have a foundational understanding of query structures and operations.
- Intermediate: A software developer building applications might need an intermediate level of proficiency. They should be able to design relational databases, optimize queries, and ensure data integrity, even if they’re not focused solely on database tasks.
- Expert: A database administrator (DBA), on the other hand, would need expert-level competency. Their role revolves around designing complex databases, ensuring optimal performance, handling backups and migrations, and securing data. Their deep expertise is essential for the smooth operation of data-heavy applications.
By delineating these levels, you not only pinpoint the skill but also the depth of expertise required, ensuring a better match between roles and capabilities.
Create a Skills Hierarchy
With your skills categorized and clustered, it’s vital to recognize that not all skills carry equal weight or relevance for every role. Build a hierarchical structure within your taxonomy. This aids in decision-making, focusing on primary skills for specific roles while also acknowledging supplementary or desirable skills. Your skills hierarchy should encompass the following:
- Primary Skills: Core competencies that are indispensable for a role.
- Secondary Skills: Beneficial skills that enhance performance but aren’t strictly necessary.
- Tertiary Skills: Additional skills that might be good to have, adding versatility but not directly impacting core job functions.
Continuously Review and Update
A skills taxonomy is not a static document; it’s a dynamic tool. Your taxonomy needs regular revisits and refinements. The rapid pace of change in the tech industry mandates that any taxonomy undergoes regular assessments to ensure its relevance and accuracy. This means not waiting for gaps to emerge, but proactively setting up periodic review checkpoints. Whether annually or bi-annually, these revisions should be calendared events, almost akin to a software update.
Moreover, fostering an environment where team members feel empowered to suggest modifications is pivotal. By creating open channels for feedback, you enable the taxonomy to benefit from on-the-ground insights, ensuring it remains a true reflection of both the industry at large and the specific nuances of your organization.
Leverage an Existing Skills Taxonomy
Make no mistake: building a comprehensive skills taxonomy is an involved process. Fortunately, there’s no need to start from scratch or go it alone.
Many companies leverage the expertise of tech platforms or consultants to set up their taxonomy. And companies like HackerRank have ready-to-use taxonomies that are built on real-world skills data and powered by machine learning. This provides a strong foundation to get you up and running fast and keeps your taxonomy current.
For companies with unique requirements, it’s also possible to craft custom taxonomies that resonate specifically with your organization’s goals and nuances. Utilizing these external resources can streamline the process, ensuring a blend of industry expertise and tailored customization.
Best Practices For Building A Skills Taxonomy
Building a skills taxonomy is a foundational step, but ensuring its effectiveness and longevity requires adherence to certain best practices. A well-crafted taxonomy is not just about the initial setup, but also its maintainability, adaptability, and relevance.
- Start Small: While it’s tempting to overhaul your entire skills framework in one go, starting with a single department or a high-priority team can be more manageable. This allows for a more focused approach. From there, you can fine-tune the process and then scale it across the organization.
- Ensure Consistency: While different teams might use varied terminologies or have unique skill nuances, the taxonomy should have a consistent structure and format. This ensures ease of use and prevents confusion during cross-departmental interactions or hiring processes.
- Engage with External Communities: Tech communities, forums, workshops, and conferences can be goldmines of information. They provide insights into emerging trends, fading practices, and the evolving needs of the tech ecosystem.
- Prioritize Flexibility: A rigid taxonomy can quickly become outdated. Build yours with flexibility in mind, allowing for easy additions or modifications. This ensures that as new skills emerge or old ones fade, your taxonomy remains current.
- Seek Regular Feedback: Beyond scheduled reviews, actively seek feedback from users of the taxonomy. This could be hiring managers, HR professionals, or even candidates. Their insights can highlight areas for improvement, ensuring the taxonomy remains user-centric.
Implementing these best practices can enhance the durability and efficacy of your skills taxonomy, making it a dynamic tool that evolves in tandem with your organization’s needs and the broader tech industry’s shifts.
Crafting a dynamic skills taxonomy can feel like a daunting project at first. But making the switch to a skills–centric approach is an investment in the strategic growth and adaptability of a company. A strong taxonomy empowers HR leaders, hiring managers, and tech recruiters to make informed decisions, nurture talent effectively, and build the workforce of the future.
This article was written with the help of AI. Can you tell which parts?