Abundant and inexhaustible, data is an unlimited fuel that companies can seize upon to drive their strategic decisions and improve their performance. As such, human resources is one of the most relevant application areas for Data Science: it is here that qualified data allows informed decisions to be taken, which are essential to the smooth running of the business. The question remains of how the “science of data” can serve the HR professions.

The importance of Data Science in the HR sector

There is no shortage of HR data. Process automation can collect huge volumes of data right through the human resource value chain. The problem is that HR departments are not always sure how to exploit this data. This is the aim of Data Science, which is to process diffuse and unstructured data so that it can be exploited. This data serves the HR departments in two ways: by helping in decision-making and by providing prediction tools.

Analysis of HR data to aid decision-making

For 64% of HR departments (source: IDC), dashboards and HR analysis tools are of paramount importance. They provide real HR data that the company can rely on to make the right decisions and reduce acquisition costs. This information includes internal quantitative data and social climate indicators within the company: wages, employee experience, absenteeism rate, turnover, productivity, etc. Data Science makes it possible to exploit and cross this data in order to learn from it, for example by observing the impact of absenteeism on overall productivity.
This HR data, exploited through machine learningalgorithms, contribute to strategic decisions.

Let’s take two examples.

  • In terms of recruitment. A company receives up to 250 applications for the same position. Data Science makes it possible to make a first sorting by identifying the candidates that best fit the post to be filled. It also helps to calculate the volume of candidates that must be seen in order to find the right candidate. Finally, it offers the opportunity search for the best candidates wherever they are, especially on the Internet (83% of job seekers use the Web after the Employment Center), and to identify professionals who are actively searching.

 

  • In terms of turnover. When an employee leaves, it is necessary to launch a recruitment campaign, and a replacement selected and trained. This process consumes time, money and resources. A detailed analysis of HR data provides the tools to identify the conditions in which employees choose to leave or stay.

HR data as a prediction tool

The other aspect of Data Science applied to HR is prediction. The analysis of HR data makes it possible to anticipate recruitment needs (the time it takes to find the ideal candidate for a given position versus the roadmap of upcoming missions), training (what training for which employees, at what point in their careers) and the management of current and future talent (create the conditions for employees to stay and for new talent to want to apply).

This predictive approach, based on machine learning , involves the HR departments facing the data. The data cannot speak for itself. It needs to be sorted, processed, analyzed, and properly exploited. Using data to drive HR means setting up a new corporate culture through the integration of Data Science.

Despite the innumerable possibilities offered by Data Science to serve HR, we must not forget the “human” dimension, with its host of unquantifiable data and unpredictability. That’s why there will always be men and women behind the data!