In terms of employment, France raises a paradox: the country suffers from an unemployment rate of around 10% while hundreds of thousands of vacancies are not filled.
What are the reasons behind this gap and how can big data help resolve this paradox?
It has been about twenty years that applying online has become a general practice. From SMEs to large groups, companies recruit online, either through job boards or through directly posting vacancies on their websites. What facilitated online postings and visibility of job offers, however, turned against recruiters as well as candidates. HR managers now receive hundreds of applications for a vacancy but do not have the human resources to properly analyze all CVs. HRIS software with scoring tools do facilitate this task but they also have their own limitations.
Start-ups specializing in HR issues now offer to successfully refine scoring methods and find the perfect match for each position. Recruitment giants are also focusing on skills in data science and machine learning. This explains why the successful recruiting platform Smartrecruiters (B2B enterprise) acquired Berlin-based start-up Jobspotting and with this, added an efficient sourcing tool (3 million jobs filled through Jobspotting in three years) to its existing suite of recruiting software. Thanks to the data input by users and integrating their feedbacks on serviceability, these platforms improve performance autonomously.
In France, public employment data comes from Pôle Emploi. The organization followed the footsteps of Etalab’s open data mission in 2013. By 2015, Emploi Store was offering the first 6 datasets which were open to developers and data scientists. But the exchanges between start-ups and the mastodon of employment were slow. At the end of 2016, Paul Duan, founder of Bayes Impact (a non-profit NGO) and a new ‘herald’ of big data for common good, aroused renewed interest and media frenzy with his goal to reduce unemployment by 10%. With his income from the Silicon Valley, he created Bob Emploi, a data-driven companion for job seekers.
There is a link between our online activity and the different aspects of our personal lives. For example, In Finland, a study showed that Google searches on social security schemes increase just before increase in unemployment rate. Similarly, in United Kingdom, the shut-down of a factory was reflected in telephone records of residents of that city. In Spain, a subset of all tweets made over a time period was mapped over unemployment rates and geolocation information. Thus, big data can be used to predict changes in employment rates in different places. Will INSEE and traditional surveys soon become obsolete? Either way, the next challenge in the field will be real-time monitoring of this data.
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