Skill is the key to reading all HR elements. The difficulty lies in the fact that there are thousands of them, translated differently from one company to another.
Why should the HR function and HR solution providers should leverage an ontology?
Learning and Development
To proactively identify training needs and recommendations, improve the learning experience by increasing the match between training and learner needs, assess upskilling and reskilling needs…
Strategic workforce planning
In order to design a process within the company to anticipate current and future staffing needs by analyzing the current employee skills and highlighting the resource gaps to reach the company’s objectives.
With the goal of improving recruiting by better understanding the skill gaps that exist between employees’ skills and the company’s needs, areas of tension and the skills most in demand on the market.
Why not develop your own ontology in-house
To answer this question, it is essential to understand what is involved in building a job and skill ontology.
Steps to build a jobs and skills ontology
Minimum time to develop
Building a comprehensive jobs and skills database
Building an enhanced and curated dataset on top of existing database (ESCO, O*net, others)
Leveraging NLP, multivariate algorithms and AI to exploit that database
Job and skill title matching, career path projection, text-to-skills and text-to-jobs matching, artificial neural networks
Enrich that database with insights
Soft skills to job matching, remote work index, automation index, senior employment, industry benchmark…
Solve a broad array of data challenges to fit that brick into your technology
Scope and define the client’s needs, design solutions, and APIs to meet this need, test and iterate, deliver final solution
→ Thus, even with some of these four steps done in parallel, it takes a minimum of 3 years for the ontology to be operational.
Choosing your job and skill ontology
Customer testing and data completeness are two important criteria to consider when choosing an ontology.
Having data is good, making it talk is even better. Data is of little value without a layer of algorithms to match jobs to each other, job titles, technical skills, soft skills and training.
Exhaustiveness of data: The various job and skill repositories
The repositories, sources of job and skill data of various kinds, can be classified into three major groups: generalist, specialized / branch and company- specific repositories.
Get access to our jobs and skills ontology.
Our customers can access our data and algorithms through a SaaS platform or APIs, allowing them to access our information without having to modify their existing applications.
Deployment time is short: typically less than two months from project launch to availability.
Would you like to learn more about Boostrs’ job and skill ontology? Contact our team 📩 and let’s discuss your needs together.
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