Big data: lessons for employers and employees
| Pages | 248-261 |
| DOI | https://doi.org/10.1108/ER-06-2018-0159 |
| Date | 06 January 2020 |
| Published date | 06 January 2020 |
| Author | Debora Jeske,Thomas Calvard |
| Subject Matter | HR & organizational behaviour,Industrial/labour relations,Employment law |
Big data: lessons for employers
and employees
Debora Jeske
School of Applied Psychology,
University College Cork National University of Ireland, Cork, Ireland, and
Thomas Calvard
Business School, University of Edinburgh, Edinburgh, UK
Abstract
Purpose –The purpose of this paper is to critically reflect on the pros and cons of using employee
information in big data projects.
Design/methodology/approach –The authors reviewed papers in the area of big data that has immediate
repercussions for the experiences of employees and employers.
Findings –The review of papers to date suggests that big data lessons based on employee data are still a
relatively unknown area of employment literature. Particular attention is paid to discussion of employee
rights, ethics, expectations and the implications employer conduct has on employment relationships and
prospective benefits of big data analytics at work for work.
Originality/value –This viewpoint paper highlights the need for more discussion between employees and
employers about the collection, use, storage and ownership of data in the workplace. A number of
recommendations are put forward to support future data collection efforts in organisations.
Keywords Data analytics, Ethics, Employment, Big data, Consent
Paper type Viewpoint
Introduction
Computer and internet-based technology has steadily advanced in recent decades and
changed working lives, both positively (e.g. working flexibly, remotely and virtually) and
negatively (e.g. work intensification, 24/7 availability). These technological developments
are the source of a dramatic increase in the amount and availability of data in the world
(McAfee and Brynjolfsson, 2012). Larger sets of data can be captured more readily than ever
before, increasing the potential for developing analytical formulae and rules to solve
problems (via algorithms to process data), which in turn generate insights in the form of new
information processing or decision-making aids (Dormehl, 2014).
Big data has become a popular label for many data analytics efforts. Originally, the term
big data emerged to define the technological revolution that enabled immense data
collection ( Jacobs, 2009). Since then, the term has migrated into other domains and stands
for different analytical aspects, depending on the context within which big data is
mentioned. The term is now used to refer to both data processing capabilities and the
characteristics of data, encapsulating both technical but also commercial aspects of data
collection activities (Nunan and Di Domenico, 2017). Mayer-Schönberger and Cukier (2013)
consider big data as the emerging ability to crunch vast collections of information and
analyse it instantly (see also Kitchin, 2014). In a similar vein, Boyd and Crawford (2012,
p. 663) suggest that big data is not necessarily a statement describing the size of data but
instead a term that designates the “capacity to search, aggregate, and cross-reference large
data sets”.
The most important characteristic is the fact that big data analytics will go beyond
traditional data sources (Ducey et al., 2015). Specifically, big data may involve several
conjoined data sets from very different sources, and include data points generated from a
variety of multimedia sources, such as video and audio records, pictures, different types of
file formats captured presentations and texts, as well as sensors, frequencies and
Employee Relations: The
International Journal
Vol. 42 No. 1, 2020
pp. 248-261
© Emerald PublishingLimited
0142-5455
DOI 10.1108/ER-06-2018-0159
Received 7 June 2018
Revised 15 April 2019
29 July 2019
Accepted 30 July 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0142-5455.htm
248
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