"Data is the new oil", in short, data would be the essential source of the ongoing fourth industrial revolution, which has led some commentators to assimilate too quickly the quantity of data to a source of wealth in itself, and consider the development of big data as an quasi direct cause of profit. Human resources management is not escaping this trend, and the accumulation of large amounts of data on employees is perceived by some entrepreneurs as a necessary and sufficient condition for the construction of predictive models of complex work behaviors such as absenteeism or job performance. In fact, the analogy is somewhat misleading: unlike oil, there are no major issues here concerning the production of data (whose flows are generated continuously and at low cost by various information systems), but rather their ''refining'', i.e. the operations necessary to transform this data into a useful product, namely into knowledge. This transformation is where the methodological challenges of data valuation lie, both for practitioners and for academic researchers. Considerations on the methods applicable to take advantage of the possibilities offered by these massive data are relatively recent, and often highlight the disruptive aspect of the current ''data deluge'' to point out that this evolution would be the source of a revival of empiricism in a ''fourth paradigm'' based on the intensive and ''agnostic'' exploitation of massive amounts of data in order to bring out new knowledge, following a purely inductive logic. Although we do not adopt this speculative point of view, it is clear that data-driven approaches are scarce in quantitative HRM studies. However, there are well-established methods, particularly in the field of data mining, which are based on inductive approaches. This area of quantitative analysis with an inductive aim is still relatively unexplored in HRM ( apart from typological analyses). The objective of this paper is first to give an overview of data driven methods that can be used for HRM research, before proposing an empirical illustration which consists in an exploratory research combining a latent profile analysis and an exploration by Gaussian graphical models.

翻译：“数据是新时代的石油”——简言之，数据将成为正在进行的第四次工业革命的核心资源。这一观点导致一些评论者过快地认为数据数量本身就等同于财富源泉，并将大数据的发展视为利润的直接成因。人力资源管理也未能幸免于这一趋势，大量员工数据的积累被一些企业家视为构建缺勤或工作绩效等复杂工作行为预测模型的充分必要条件。事实上，这种类比存在一定误导性：与石油不同，数据生产在此并不构成主要问题（各种信息系统以低成本持续生成数据流），真正关键的是数据的“精炼”，即将数据转化为有用产品（即知识）所需的各项操作。这一转化过程正是数据价值评估方法论挑战之所在，无论对实践者还是学术研究者而言均如此。关于如何利用这些海量数据所提供可能性的方法学考量相对较新，且常强调当前“数据洪流”的颠覆性特征，指出这种演变可能催生基于纯粹归纳逻辑的“第四范式”——即通过密集且“不可知论”式地利用海量数据来提炼新知识，从而复兴经验主义。尽管我们并不采纳这一推测性观点，但显而易见的是，在定量人力资源管理研究中，数据驱动方法仍十分稀缺。然而，确实存在一些成熟方法（尤其在数据挖掘领域），它们基于归纳路径。这一以归纳为目标的定量分析领域在人力资源管理研究中（除类型学分析外）仍相对未被充分探索。本文旨在首先概述可用于人力资源管理研究的数据驱动方法，继而通过一项探索性研究（结合潜在剖面分析与高斯图模型探索）提供实证示例。