"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.

数据驱动方法在人力资源管理中的应用：本文首先概述了用于人力资源管理研究的数据驱动方法，然后提出了一个探索性研究的实证例子，结合了潜在分类分析和高斯图形模型的探索方式。