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Will Big Data transform recruitment methods or processes?

SHIFTING FROM TRADITIONAL RECRUITMENT TO PREDICTIVE RECRUITMENT

Wondering what Big Data is? Translating it into French won't help you understand. In this article, I will explain in detail what it entails: its advantages as well as its drawbacks!

The term Big Data encompasses a set of technologies, methods, and specific practices for storing and quickly analyzing massive amounts of data to draw insights. It's a means of studying vast quantities of data to establish original models that provide a finer view of reality and enable more relevant decision-making. This technology is now being utilized in various sectors and is also making its way into recruitment and training.

Advantages of Big Data:

  • It encompasses a family of tools that address a triple challenge known as the "3V rule". These include processing a considerable volume of data, handling a great variety of information from diverse and unstructured sources, and achieving a certain level of velocity - the frequency of data creation, collection, and sharing.

  • The cost of data collection has plummeted, connected systems are exponentially growing, and uncertainty demands deeper, faster, and more frequent analysis.

Recruiters have adopted a new method of recruitment - simple, rather intriguing, and relevant, called "predictive recruitment." This method employs straightforward yet effective strategies while also harnessing Big Data as a source. Why is it necessary to use this technology in recruitment rather than sticking to traditional methods?

The traditional process of recruiting new talent relies on different elements: academic background, skills, and professional experience. These factors form the basis on which recruiters form opinions about a candidate's journey and motivations. However, this method is no longer cutting-edge as it's considered subjective.

Today, recruiters are turning to this technology to benefit from its advantages and reduce the time spent on searching and selecting CVs of candidates who won't stay long in their company. Moreover, it adds an entirely new dimension to the job market, where human factors and equal opportunities are now part of the process.

According to Harvard Business Review (HBR), predictive recruitment allows companies to precisely target candidates for specific positions through an algorithm. To achieve this, the company must determine the key performance factors for the position. This involves analyzing various parameters, including personality, skills, experience, motivation, satisfaction factors, job descriptions, etc. In addition to identifying personality traits and useful skills, the idea is to find candidates who will thrive in their roles and won't leave too soon.

In practical terms, this predictive recruitment method requires the company to define its needs and gather internal data. Data collection can involve those occupying the relevant positions, collecting performance-related data from management, etc. The goal is to define the top-performing profiles internally and attempt to identify similar talents from a pool of candidates.

Data collection then allows the creation of a model. Companies must subsequently define the criteria that make a candidate sufficiently qualified for the position. They are then free to interact with the individuals selected by the algorithm. Take the example of platforms that provide advantages for recruiters and candidates in their processes: APEC, Indeed, Jobteaser, LinkedIn, etc.

In summary, it's advantageous to use both recruitment methods, even though predictive recruitment is more effective than traditional recruitment. However, there are drawbacks such as potentially erroneous data and security issues (Data Security). To avoid data-related problems, it's advisable to involve a Data Scientist (data analyst or data manager).


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