Value of data from online job vacancies (OJV)
Big Data analytics offer new possibilities to improve LMI and deliver real-time and fine-grained skills analysis and insights for users. Big Data is characterised by volume, variety, velocity and eventually - value.
Machine learning and artificial intelligence algorithms, combined with immense computing power of anytime and anywhere allow data science to exploit certain Big Data sources, which have large potential to supplement and enrich conventional LMI: it is the case of online job vacancies (OJVs) managed by a large variety of online job portals and boards.
Value in Big Data analysis for LMI
Creating knowledge out of large volumes of Data, available with high velocity and variety is the major goal of Big Data analysis. It is about value.
Analysis of thousands and millions of job vacancies can tell much about the skills employers want, in almost real time and in fine-grained detail.
Screening and ranking of online job vacancy (OJV) portals - the first step of the methodology - can tell us much about the overall panorama of the online / digital labour market in countries and regions, the features of the individual job portals, the volume of posted OJVs, the sectorial and occupational coverage of OJV.
Most importantly, analysis of OJV reveal specifics of how employers describe the jobs / tasks, the mix of skills they seek, the importance they attribute to credentials / qualifications and the conditions they offer, and their recruitment practices and priorities.
Big Data analytics allow more refined (granularity), space-related insights in real time, as well as predictive analysis. Eventually the use of Big Data can contribute to formulate and refine labour market policies in at least the following ways:
- Labour market monitoring and analysis;
- Assessing demand for workforce skills;
- Observing job-search behaviour;
- Identify localised and detailed expressions of skills mismatch, and contribute to better skills matching;
- Predictive analysis of skills demand;
- Experimental studies.
Please log in or sign up to comment.