Big data and AI still have a hard time today in gaining traction in the field of HR and employment services due to the poor quality and lack of explanatory power in the data. As JANZZ explains in a recent ILO report, any predictive analysis based on big data and determined by a large number of variables is rather inaccurate. The longer the time horizon and more variables included, the less likely such prediction is going to be completely or even partially close to reality.
Hence, any recommendations for market participants such as forecasts of the future employability and required skills of job seekers will generate little or no significant results if based on approaches that simply compile and evaluate all available job advertisements from all available sources in a market over a period of years. Because the skills are often presented and processed without any relevant semantic context, for example, the typical forecasts of general “top skills” as published regularly by LinkedIn and the World Economic Forum. One will find the skills listed are too generic or general to be used in matching, indeed, they are barely relevant for many occupations.
From the very beginning, JANZZ.technology has determined to form big data into smart data using a structured and fully semantic ontological approach and over the years, it has repeatedly proven to be the only game-changer. To learn more, please find the full article in the ILO report: