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Workforce Disruption Sharpens Focus on Improving Skills Taxonomies

A man in glasses is looking at a piece of paper while sitting at a desk.

​For HR managers seeking to track and match skilled workers to job opportunities, building a taxonomy that provides updated and accurate information on employees' skills and experience could be a solution.

Many HR professionals are hampered by an environment where millions of workers are resigning, skills shortages are commonplace, and the pandemic-induced migration of labor has scattered workers across the U.S. and the globe.

The hope is that a skills taxonomy will lead employers to qualified candidates, identify skills gaps and promote career growth by suggesting training opportunities as the demand for skills changes. 

These shifts in the workforce make it more difficult for HR managers to identify the right people to fill available jobs, said Ajay Awatramani, chief product officer at Cornerstone OnDemand, a Santa Monica, Calif.-based people development company that entered the skills taxonomy market in 2020 when it bought Clustree, a French technology company with an extensive artificial intelligence-powered skills ontology.

"It is a much more fluid workforce for which there needs to be a way to measure and understand not just skills, but adjacent skills and skills proficiencies," Awatramani said. "If I want to create a group of people to solve a particular problem, how do I identify who those people are? How do I predict that they can do the job and whether they will grow their skills?"

He added that a good skills taxonomy can sense the changes ahead by analyzing, for example, the skills of graduate students from prominent universities where cutting-edge research is being conducted. Being able to see the velocity of change in data over a period of time is a very important aspect of a good skills taxonomy.

"Mapping the evolution of skills is important," he said. "A computer scientist is now a specialist in AI or a specialist in robotics or a specialist in semiconductors. That specialization happens over time. Being able to capture in a taxonomy the relationships between different skills and the adjacencies of those skills is extremely important, and that is where you start to see the nuances in terms of how things start to evolve." 

Keeping Up with the Speed of Change

Martin Sundblad, research manager at International Data Corporation (IDC), said many new job roles have been introduced in recent years in the IT and communications technology sectors.

For example, the widespread adoption of cloud computing during the last decade has seen the rise of the cloud engineer, the cloud analyst and the cloud strategist. Each role has a definition of the job along with related skills and experience, which often change depending on who defines the role.

"The Great Resignation has, as one of its root causes, the reconsideration of the value of work and work-related decisions," he said. "This has forced employers to appreciate the need for a taxonomy. Employers need to look at what skills they have, what job roles they need and what are the company's human assets."

To help companies gauge the demand and supply of skills, IDC has spent the last four years developing a taxonomy of job classifications that it updates every year to help their clients provide forecasts for the development of human resources and skills. The taxonomy standardizes job descriptions and simplifies the assessment of skills within an organization, Sundblad said.

But the evolution of job roles and skills is rapid, which means taxonomies typically don't capture up-to-date shifts in new job role categories and IT professional skills requirements. Furthermore, employees don't always define job roles with consistency in their CVs, which means a taxonomy like IDC's requires continuous updates.

Another example is AI jobs, whose descriptions often change when employees update their resumes.

For example, in IDC's taxonomy, the definition of a data analyst is one who analyzes data and ensures that the information transferred from the data source to the data analysis tool is consistent and accurate. A data scientist, however, collects information about what data the business needs, what kind of decision support the company requires and translates the business problem into an analysis that the data analyst can understand. And then there are data engineers.

"A data scientist gets a higher salary than a data engineer, therefore a data engineer tends to call themselves a data scientist in his or her CV," Sundblad said. "An understanding of this difference between the two job roles is not widespread, which has resulted in a huge growth of data scientists in the world."

The Future of Skills Taxonomies

Looking ahead, Awatramani said the next frontier in skills taxonomy development will be exploring the softer side of employees' skill sets. To help this effort, Awatramani's company recently launched Cornerstone Xplor, a platform that executives say can track softer skills. The platform leverages the company's AI-based skills taxonomy to match people to training and job positions.

Awatramani said it's important to know who a qualified engineer or data analyst is, but the pandemic has highlighted other critical skills.

"Employers want to know if a worker can thrive under pressure, do they have the ability to make change head-on, either be a change agent themselves or be a participant to change, and are they an empathetic manager who can lead a team in a remote geography," Awatramani said. "Taxonomies are evolving to include these types of new skills."

Nicole Lewis is a freelance journalist based in Miami.


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