Big Data and People Management: The Prospect of HR Managers

Big Data and People Management: The Prospect of HR Managers

Daria Sarti (University of Florence, Italy) and Teresina Torre (University of Genoa, Italy)
Copyright: © 2019 |Pages: 27
DOI: 10.4018/978-1-5225-7077-6.ch006

Abstract

This chapter investigates the role of big data (BD) in human resource management (HRM). The interest is related to the strategic relevance of human resources (HR) and to the increasing importance of BD in every dimension of a company's life. The analysis focuses on the perception of the HR managers on the impact that BD and BD analytics may have on the HRM and the possible problems the HR departments may encounter when implementing human resources analytics (HRA). The authors' opinion is that attention to the perceptions shown by the HR managers is the more important element conditioning their attitude towards BD and it is the first feature influencing the possibility that BD can become a positive challenge. After the presentation of the topic and of the state of the art, the study is introduced. The main findings are discussed and commented to offer suggestion for HR managers and to underline some key points for future research in this field.
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Introduction

Working environments -and organizations in general- nowadays are continuously experiencing massive and rapid increases in those that have been identified as ‘big data’ (BD). Defined by Sivarajah and colleagues (2017: 263) as an ‘overwhelming amount of complex and heterogeneous data pouring from any-where, any-time and any-device’, BD is recognized as one of the most intriguing topic among scholars and practitioners.

Even if of uncertain origins, as Diebold (2018) remarks, the phenomenon has been investigated in its different dimensions, often in partnership with companies (Angrave et al., 2016) and taken into consideration from many perspectives. In particular, a significant and increasing interest has been shown from 2011 as Gandomi and Haider (2017) put in evidence.

The current debate on BD benefits from interdisciplinary contributions coming from diverse research domains - such as: engineering, information and communication technology, economics and management - and seeks to understand the newness and originality that BD is introducing in every field. Also, a vast contribution to the overall discussion is given by the massive number of reports and researches carried out by consultancy firms and by firms operating in the field of big data analytics (BDA) (namely Gartner, n.a.; McKinsey & Company, 2016).

A lively discussion is ongoing regarding the correct definition of the concept and subsequently on its delimitation (Sheng et al. 2017). Indeed the lack of consensus of the scientific community on the concept of BD (De Mauro et al., 2016) might represent itself a clue of the poor development of the discipline (Ronda-Pupo and Guerras-Martin, 2012) and of course, ‘not’ irrelevant is the fact that the debate on BD is relatively recent.

A number of researchers describe BD using a mere quantitative approach, thus considering the volume of data, which is an inherent property. For example, Manyika and colleagues (2011: 1) suggest that BD ‘… will range from a few dozen terabytes to multiple perabytes’, even if they do not ignore that the size can increase, as technology advances. Other scholars also include further intrinsic features, which contribute to qualify the topic (Angrave et al., 2016; Gandomi and Haider, 2017). In this sense, the most diffused and cited definition has been given by Gartner Company, which describes BD as ‘high volume, high velocity, and/or high variety of information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization’ (Gartner IT Glossary, n.a.: 1).

Coherently, Laney’s vision introduces the 3Vs model of BD, focusing on three dimensions representing its key elements: Volume (that is the magnitude of data available to the organization), Velocity (that means the speed of data creation, streaming, and aggregation), and Variety (which refers to the richness of data representation and to their structural heterogeneity) (Laney, 2001).

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