methodology.

“Learned skills” are sub-skills that represent capabilities acquired through education and experience, and “inherent skills” are sub-skills that represent characteristics that are core to the individual outside of experience. This data is not additive (individuals have different skill compositions, that might often crossover) and, contrary to skill cluster data, does not show the total availability of talent, but rather accessibility of competencies.

Skill clusters defined as “white-collar” refer to those that are traditionally office-based and non-manual, whereas those defined “skilled trades” refer to those that are traditionally not office-based and are manual. Distinctions are made in this research for clarification purposes, as the JVRs of skilled trades are contextualized differently. 

While one of our in-demand skill clusters focuses on AI and automation skills specifically, this data refers to roles in AI, e.g., building AI tools. The Global In-demand Skills research also offers “impact of AI” filters on many of the charts to provide a view of how adding AI skills or experience impacts talent supply, demand, talent mobility and hiring complexity for all skill clusters, e.g., a marketing professional with AI experience.  

Trends indicate that companies are increasingly seeking more senior-level talent for today’s high-demand roles. At the same time, the hiring complexity for these roles is also increasing. To obtain the skills they’re seeking, companies will need to consider when to invest in emerging talent to ensure they continue building a pipeline of that senior talent they so eagerly seek. 

In many cases, this year’s research allows you to filter by “senior JVR.” For the purposes of this research, senior-level talent is defined as having more than 10 years of experience in the identified skill cluster. 

This section provides a breakdown of the inherent skills, learned skills and motivations/aspirations for each skill cluster. Inherent skills and motivations/aspirations combined are commonly defined as “soft skills” in the market. The importance for each individual inherent skill and aspiration is based on the human potential model. 

Through natural language analysis, we identify the frequency of occurrence for non-hard skills across different regions, providing insight into the general potential of each market. You can use this data to understand the complexity of hiring based on how often skills are reflected in the general population for each individual market. Additional dimensions include an analysis of job advertisements in the context of core skills required by employers.

Skills supply data indicates the total number of individuals who have the noted skills required for each of the in-demand skill clusters in the 24 markets we researched. These figures are based on a combination of verified, normalized labor market data by market and granular, skill-based data sourced from professional social media networks, job boards, and career sites. 

The in-demand skills dashboard represents the number of individuals who, based on predefined skills, claim to have such a skill in their profile/CV/social page. You can use these insights to understand the availability of skills in each of the 24 markets researched (“supply map”), availability of sub-skills (“skill type”), talent with recent job search activity (“active talent”), as well as the share of talent who prefer permanent or contract work (“preferred employment type”).

Skills demand data indicates job postings that require noted skills for each of the in-demand skill clusters in the 24 markets we researched. These figures are based on a combination of verified, normalized labor market data by market and granular, skill-based data sourced from professional social media networks, job boards, and career sites. 

The in-demand skills dashboard represents the total number of job adverts related to the skill clusters, and allows you to see demand for each skill cluster by market. You can also explore demand for sub-skills within each cluster, and the job vacancy ratio (JVR) — defined as hiring complexity (with a base hiring complexity of 1% for each category) — to understand market competitiveness for these skills. The higher the JVR percentage, the more complex and competitive the market is.

The data included in this graph shows the average salary brackets indexed relative to U.S. compensation for the in-demand skill clusters in the 24 markets examined. Compensation data is mapped and analyzed from combined sources providing current pay data. Given that each skill cluster contains various seniority levels, multiple job roles, and a wide variety of skills, it serves as an indicator of the relative expense of each skill cluster across multiple geographic markets.

The in-demand skills dashboard allows you to explore cost relations between different markets to help you stay competitive and within budget when recruiting for high-demand skills. 

Remote working data shows the percentage of job postings that offer candidates remote or hybrid work (noted as “demand”), as well as talent working preferences (noted as “supply”) for each of the in-demand skill clusters in the 24 markets researched. 

These findings are inclusive of job descriptions that use relevant remote/hybrid working keywords and are sourced from job advertisement databases, professional networking platforms and job boards. It is estimated that the actual share of remote/hybrid working opportunities is higher than advertised online. You can view the data by both skill cluster and individual skills.