Finding your superpower: What is a skills ontology?
Skills has been THE buzzword in learning technologies for a number of years now, and almost every learning, talent, or HR platform has some form of skills management built into it. But skills aren’t exactly a new concept, so is there more to all this than meets the eye?
The Great Resignation around the Covid-19 pandemic and current shortages in the labor market has
made it hard for organizations to retain staff with the relevant skills or to buy in the skills they need with
an external recruitment drive, all of which means that skills management is now a key focus. But how do managers, leaders, and individuals know where the skills gaps are and where best to invest in learning experiences that bridge those gaps?
This is where AI-driven skills-ontology-management systems come in. They allow you to see the skills you have within your organization right now and know which skills you need today and in the future. These combine to show you a skills gap for individuals, teams, and the organization as a whole, meaning you in L&D can focus on creating or curating effective learning experiences to close that gap. And skills AI can even go one step further by auto-tagging your learning and your people, thus ensuring recommendations are personalized, relevant, and timely.
What is a skills ontology?
If you’ve only recently heard the word “ontology” being used in relation to skills and wondered what on earth it means, don’t worry—you’re definitely not alone! If you search for a definition of ontology you will find that it is a branch of metaphysics concerned with the nature and relations of being, but dig a bit deeper and the term has been used in scientific circles for a number of years to describe a way of organizing concepts or information. However, while skills ontologies have been the subject of research in academic circles since the beginning of the 21st century, it’s only recently that they have been front of mind for L&D departments and learning technologies vendors.
In contrast to a taxonomy or framework, which only forms a hierarchical subdivision of defined terms, an ontology represents a network of information with logical relationships. The strength of ontologies is that they can operate in the space between different classifications and data sources, allowing relationships to be formed between terms that might be slightly different but have the same or similar meanings.
Think about the ambitious employee who is looking to progress to a leadership role within their organization. They enter a bookstore to find a book on leadership and automatically head to the business section, where they find a range of relevant books and pick out a couple that look interesting. But by looking only in the business section they have really limited their choice of books, because lurking in less obvious sections, such as biography and sport, are other books that contain elements of leadership that could help them on their journey. An ontology would have spotted this link and recommended those books to them also in a way that more traditional hierarchical frameworks can’t.
One benefit of skills ontologies is that they give us a common language to compare the skills of our
people, teams, and organizations, even where their skills are described differently or used in a different
way. Skills taxonomies have always failed here, their rigid structures mean that they can’t recognize and
compare skills that are not part of the taxonomy.
What if different companies group skills differently? Is leadership a technical skill or a social skill? Who
decides in which group a skill belongs? Also, the skills needed today are unlikely to be the skills needed
tomorrow. According to the World Economic Forum, the half-life of a job skill is about five years—meaning that every five years that skill is about half as valuable as it was before. It’s a struggle for our people to keep up, but it’s even more of a struggle if our skills frameworks can’t, as we can’t even see the changes coming.
So how can we compare our skills with those in the wider world—and the wider world of the future at
that? By using an AI-based skills ontology.
Trying to analyze and keep up with the pace of change surrounding required skills in the jobs market
is a bit like trying to count the number of people in Times Square, with crowds constantly on the move
and flowing in and out. While a human could look to achieve this by taking a photograph, the data would
instantly be out of date—almost certainly changing before the counting even started.
But while it is hard for a human to track the changing organizational skills landscape, this type of data crunching is easy for a machine. Machines can continually absorb job-market data, including resumes and job descriptions from across the globe, and update the ontology, meaning you and your organization can monitor and compare. What is needed then are easy-to-read dashboards that show all your internal data mapped against that of external forces, allowing you to see at a glance where your organization and your people need to build skills both for today and for tomorrow.
But before we look at the full benefits that skills ontologies can bring, let’s look at the skills challenges
organizations are facing and the impact they are having on their businesses.
A highly competitive labor market
One of the key indicators of the state of our labor markets is the number of unemployed people per vacancy or job opening. Data from the Office for National Statistics shows us that in early 2020, just prior to the Covid-19 pandemic, there were 1.7 unemployed people for every vacancy in the U.K. This peaked at 4:1 in the early stages of the pandemic but has now dropped to 1:1.
In the U.S.A., the labor market saw shortages even prior to the pandemic. In fact, the number of unemployed people per job opening hasn’t risen above one person to one opening since February 2018 (data from the U.S. Bureau of Labor Statistics).
Considering that 63% of companies surveyed by Zenefits say retraining employees is actually harder than hiring them, this is a huge issue, and it’s also a costly one.
According to the Society for Human Resource Management, the average cost of hire in the U.S.A. at the end of 2021 was $4,683, rising steeply to $28,329 for executive hires. These costs include hard costs,
which are easy to measure, such as the administration of an employee’s departure, advertising, and recruitment to find their replacement, the time taken to write job ads, conduct shortlisting and interviews, and the onboarding of new hires.
But the Work Institute’s 2022 Retention Report shows that the actual figures are even higher when you take into account not only the hard costs but also the soft costs, which include the lower productivity of the departing employee, lower productivity of the supervisor or employee who covers the job until a replacement is found, and the time spent training the new hire.
Having the right skills in the right places in organizations has always been key to efficiency, profitability, and success. The problem for organizations across the globe is that a few years ago if we didn’t have the right skills, it was easy enough to buy them in, but with only one applicant per role, that gets increasingly tricky. The other issue with recruiting to close skills gaps is that new recruits will have a steeper learning curve.
The skills gap
When we are trying to fill roles, whether, through internal mobility or external recruitment, we are looking ideally for a skills match. That skills match allows people new in the role to hit the ground running more quickly and to be effective and impactful for the organization sooner after they start.
But the decline of certain roles and vast changes to those that remain make this a real challenge. According to predictions from the World Economic Forum’s The Future of Jobs Report 2020, employers expect that by 2025 roles that are becoming less in demand through automation will “decline from being 15.4% of the workforce to 9% (6.4% decline)” and roles that are becoming more in demand “will grow from 7.8% to 13.5% (5.7% growth),” which means it is likely that “85 million jobs may be displaced by a shift in the division of labor between humans and machines, while 97 million new roles may emerge that are more adapted to the new division of labor between humans.” So, it’s not that there will be fewer jobs for humans in the next five years, but rather that those jobs will require very different skills.
And even if they do know where to focus “40% of HR leaders say they can’t build skill development
solutions fast enough to meet evolving skill needs.”
In response to these challenges, an ontology-based skills management system can help. Take a look at our latest whitepaper, ‘Skills are your organization’s superpower‘, to find out how.
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