How Data Science serves HR?

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 made, which are essential to the smooth running of a business. The question that remains is how can the “science of data” serve the HR professions?

The importance of Data Science in HR

There is no shortage of HR dataProcess 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. Data Science provides a method to process and organize unstructured data so that it can be utilized productively. This data serves HR departments in two ways: by helping immediately in decision-making and by providing predictive 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 employee acquisition costs. This information includes internal quantitative data and social climate indicators within the company: wages, employee experience, absenteeism rates, turnover, productivity, and more. Data Science makes it possible to exploit and cross reference 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 learning algorithms, contributes to strategic decisions.

Let’s take two examples :

  • Recruitment. A company receives up to 250 applications one same position. Data Science makes it possible to do a first sort 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 to search for the best candidates wherever they are, especially on the Internet (83% of job seekers use the Web), and to identify professionals who are actively searching.
  • Turnover. When an employee leaves, it is necessary to launch a recruitment campaign, and a replacement is selected and trained. This process consumes time, money and resources. A detailed analysis of HR data provides the tools to identify in great details the conditions under 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 campaigns), training (what training for which employees, at what point in their careers) and the management of current and future careers (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 interrogating 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 discipline of Data Science.

Despite the innumerable possibilities offered by Data Science to serve HR, we must not forget the “human” dimension. As powerful as Data Science is, it cannot replace people and intuition, which is why there will always be people behind the data.