As a human resources professional, you are responsible for managing the most valuable asset of any organization: its people. One of the critical aspects of your role is understanding the skills that your employees possess and how they can be best utilized for organizational success. This is where skill ontologies come into play. In this blog post, we will take you on a journey through the history of skill ontologies, their development, and their importance in the HR landscape today. With this historical perspective, you'll be better equipped to understand and leverage skill ontologies for your organization's talent management needs.
Skill ontologies are structured representations of skills and competencies that exist within a particular domain. They provide a common language and framework for describing and understanding the skills required for various jobs, roles, and tasks (Abel, 2017). In the context of HR, skill ontologies can be used for a variety of purposes, including:
1. Talent acquisition: Identifying the essential skills and qualifications for specific job openings.
2. Employee development: Identifying areas where employees can improve or acquire new skills to advance their careers.
3. Succession planning: Identifying potential candidates for leadership roles based on their skillsets and experience.
4. Workforce planning: Understanding the organization's current skill landscape and identifying potential gaps or areas for growth.
The concept of skill ontologies can be traced back to the 1960s and 1970s, with the development of the first job analysis techniques (Fine & Cronshaw, 1999). These early methods aimed to identify and describe the essential components of various jobs systematically. The U.S. Department of Labor's Dictionary of Occupational Titles (DOT) was one of the first attempts to create a comprehensive taxonomy of job skills and requirements (U.S. Department of Labor, 1991).
In the 1980s and 1990s, the focus shifted towards competency modeling, which aimed to identify the underlying knowledge, skills, and abilities (KSAs) required for successful job performance (McClelland, 1973; Shippmann et al., 2000). Competency models provided a more holistic and integrated view of employee capabilities and were widely adopted by organizations for talent management purposes.
The late 1990s and early 2000s saw the emergence of the first skill ontologies, driven by advances in information technology and the growing importance of the internet in the world of work (Abel, 2017). These early skill ontologies were primarily used for educational and training purposes, as well as for the development of online job boards and career guidance tools.
One of the most influential skill ontologies developed during this period was the European Skills, Competences, Qualifications and Occupations (ESCO) framework, which was first launched in 2008 (European Commission, 2021). ESCO aimed to provide a standardized language and structure for describing skills and qualifications across different industries and countries. It has since evolved into a comprehensive, multilingual, and widely used skill ontology that covers a broad range of sectors and occupations.
Another notable skill ontology is the Occupational Information Network (O*NET), which was launched in the United States in 1998 as a replacement for the DOT (U.S. Department of Labor, 2021). O*NET provides a rich and detailed database of occupational information, including the specific KSAs required for various jobs, as well as information on job tasks, work activities, and work context. O*NET has been widely adopted by organizations, researchers, and policymakers for various purposes, including HR management, workforce planning, and labor market analysis.
The Present and Future of Skill Ontologies in HR
In recent years, skill ontologies have become increasingly important in the world of HR, as organizations grapple with the challenges of rapid technological change, globalization, and an increasingly diverse and mobile workforce (Cappelli, 2015). Skill ontologies provide a powerful and flexible tool for analyzing and managing talent in this complex and dynamic environment.
One of the key trends driving the adoption of skill ontologies in HR is the growing importance of data-driven decision-making and the rise of HR analytics (Marler & Boudreau, 2017). By providing a structured and consistent framework for describing and analyzing skills, skill ontologies can help organizations make better-informed decisions about talent acquisition, employee development, and workforce planning.
Another important trend is the increasing use of artificial intelligence (AI) and machine learning in HR management (Brynjolfsson & McAfee, 2014). Skill ontologies can be used to train AI algorithms and support the development of intelligent systems for tasks such as resume screening, job matching, and skills gap analysis. This can help HR professionals save time, improve decision-making, and ultimately deliver better outcomes for their organizations.
Finally, skill ontologies are likely to play a crucial role in the future of work, as organizations increasingly adopt more flexible and agile ways of working (World Economic Forum, 2020). By providing a common language for describing and understanding skills, skill ontologies can help organizations adapt to new business models, identify emerging skill requirements, and build more resilient and adaptable workforces.
The history of skill ontologies is a story of continuous evolution and adaptation, driven by the changing needs of the world of work and the relentless march of technological progress. As an HR professional, understanding this history and the current landscape of skill ontologies is essential for unlocking their full potential for your organization.
By leveraging skill ontologies, you can not only make better-informed decisions about talent management but also help your organization navigate the challenges of the future of work. So, dive into the world of skill ontologies, and unlock the power of structured skill data to transform your HR practice and drive organizational success.
Abel, A. (2017). Skill Ontologies. In Encyclopedia of Terminology for Educational Communications and Technology (pp. 95-98). Springer.
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. WW Norton & Company.
Cappelli, P. (2015). Skill Gaps, Skill Shortages, and Skill Mismatches: Evidence and Arguments for the United States. ILR Review, 68(2), 251-290.
European Commission. (2021). ESCO: European Skills, Competences, Qualifications and Occupations. Retrieved from https://ec.europa.eu/esco/portal/home
Fine, S. A., & Cronshaw, S. F. (1999). Functional Job Analysis: A Foundation for Human Resources Management. Psychology Press.
Marler, J. H., & Boudreau, J. W. (2017). An Evidence-Based Review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3-26.
McClelland, D. C. (1973). Testing for Competence Rather Than for "Intelligence". American Psychologist, 28(1), 1-14.
Shippmann, J. S., Ash, R. A., Carr, L., Hesketh, B., Pearlman, K., Battista, M., Eyde, L. D., Kehoe, J., Prien, E. P., & Sanchez, J. I. (2000). The Practice of Competency Modeling. Personnel Psychology, 53(3), 703-740.
U.S. Department of Labor. (1991). Dictionary of Occupational Titles (4th ed.). U.S. Government Printing Office.
U.S. Department of Labor. (2021). O*NET OnLine. Retrieved from https://www.onetonline.org/
World Economic Forum. (2020). The Future of Jobs Report 2020. Retrieved from https://www.weforum.org/reports/the-future-of-jobs-report-2020
