Big Data in Organizations and the Role of Human Resource Management

A Complex Systems Theory-Based Conceptualization

by Tobias M. Scholz (Author)
Thesis XVII, 237 Pages


Big data are changing the way we work as companies face an increasing amount of data. Rather than replacing a human workforce or making decisions obsolete, big data are going to pose an immense innovating force to those employees capable of utilizing them. This book intends to first convey a theoretical understanding of big data. It then tackles the phenomenon of big data from the perspectives of varied organizational theories in order to highlight socio-technological interaction. Big data are bound to transform organizations which calls for a transformation of the human resource department. The HR department’s new role then enables organizations to utilize big data for their purpose. Employees, while remaining an organization’s major competitive advantage, have found a powerful ally in big data.

Table Of Contents

  • Cover
  • Title
  • Copyright
  • About the author
  • About the book
  • This eBook can be cited
  • Preface
  • Acknowledgement
  • Table of Contents
  • List of Figures
  • List of Tables
  • 1. Introduction
  • 1.1 Statement of the Problem
  • 1.2 State of Research
  • 1.3 Terminological Clarification
  • 1.4 Objective of the Thesis
  • 2. Theoretical Framework
  • 2.1 Big Data
  • 2.1.1 Etymological Origin
  • 2.1.2 Epistemological Conceptualization and Hermeneutical Observations
  • 2.1.3 Delimitation from Related Terms
  • Data Mining
  • Algorithms and Machine Learning
  • Artificial Intelligence
  • 2.1.4 Big Data Pitfalls
  • Big Data Change the Definition of Knowledge
  • Claims of Objectivity and Accuracy Are Misleading
  • Bigger Data Are Not Always Better Data
  • Taken out of Context, Big Data Lose Their Meaning
  • Accessibility Does Not Make Them Ethical
  • Limited Access to Big Data Creates New Digital Divides
  • 2.1.5 May Big Data Be with You
  • 2.2 Big Data at the Socio-Technological Level
  • 2.2.1 Technology and Society
  • 2.2.2 Technological Determinism
  • 2.2.3 Social Determinism
  • 2.2.4 Socio-Technological Concurrence
  • 2.3 Big Data at the Organizational Level
  • 2.3.1 Epistemological Framing
  • 2.3.2 Organizations as Open Systems
  • Big Data in Cybernetics
  • Big Data in Systems Theory
  • Big Data in Population Ecology Theory
  • Big Data in Complex Systems Theory
  • 2.4 Big Data at the Human (Resource) Level
  • 2.4.1 Current Status of Big Data in Human Resource Management
  • 2.4.2 Classification of Views
  • 2.4.3 Augmentation as an Alternative Path
  • 3. Research Framework
  • 3.1 Mental Model
  • 3.2 Methodology
  • 4. Analytical Implementation
  • 4.1 Core Assumptions of Big Data within Organizations
  • 4.1.1 Temporal Dimensionality
  • 4.1.2 Factual Dimensionality
  • 4.1.3 Social Dimensionality
  • 4.1.4 Cross-Sectional Dimensionality
  • 4.2 Homeodynamic Organization
  • 4.2.1 Characterizing Homeodynamic Organization
  • 4.2.2 New Roles of the Human Resource Department
  • Big Data Specific Roles
  • Big Data Watchdog as Cross-Sectional Role
  • 4.2.3 Human Resource Daemon
  • Data Farm
  • Fog of Big Data
  • Big Data Baloney Detection
  • Big Data Tinkering
  • Big Data Risk Governance
  • Big Data Immersion
  • Big Data Authorship
  • Big Data Curation
  • Big Data Literacy
  • 4.2.4 Human Resource Centaur
  • 4.2.5 Big Data Membrane
  • 4.3 Homeodynamic Goldilocks Zone
  • 5. Results
  • 5.1 Summary
  • 5.2 Limitations
  • 5.3 Implications for Human Resource Management
  • 5.4 Implications for Research
  • 5.5 Implications for Teaching
  • 5.6 Outlook
  • References

← XIV | XV →

List of Figures

← XVI | XVII →

List of Tables

← XVIII | 1 →

1.  Introduction

1.1  Statement of the Problem

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In today’s world we are surrounded by a vast amount of zeros and ones. Those numbers, called binaries and based on the Boolean logic, are the common way for computers to communicate with each other (Weyrich et al. 2014). Any digital information can be depicted in binary code. Machines and humans communicate in entirely different fashions since machines’ communication is rooted within a computational logic (Kowalski 2011). Much like different languages, the communication between human and machine requires translation. The lines of numbers opening this chapter, for example, translated into writing spell “hello world”, a familiar term for anyone studying a programming language. Both worlds (human world and machine world) seem to be separated with only few interconnections, but in recent years, the phenomenon of “big data” has led to an ongoing fusion of the two. Big data are here and big data are here to stay (Davenport 2014).

In an interplay of human and machine, both constantly generate new zeros and new ones. Be it me writing these words, a person talking on her/his phone, someone just walking the streets, be it a traffic light communicating with the control center, a smartphone logging into a Wi-Fi network, or a robot-arm in a factory. Every inhabitant of the modern world now inevitably leaves a digital trail, in addition, to her/his normal trail. The amount of data we generate on a daily basis is staggering, and increasing at an exponential rate. Everybody is participating in this digital world, contributing ever greater amounts of data. Most interestingly, nobody can hide from this digital trail. Even the decision of not participating at least generates the information of someone who does not wish to participate (Hartley & Chatterton 2001). In the future, the digital trail will become more precise, singular, and granular (Kucklick 2014) as there is an increase in the number of digital devices in the world as well as the frequency at which digital devices come to use (Economist 2014). Furthermore, the number of sensors that are capable of tracking people is constantly increasing and some sensors generate information about people as a by-product. These data only add to the already massive pile of existing data.

Although the information deluge (Hoenkamp 2012) is coupled with people leaving a more and more detailed digital footprint, the current development is predominantly driven by technology (Boyd & Crawford 2012). Big data pierce social life and society extensively. Society’s solutions, however, are far from being precise or adequate. There is, in fact, a lack of social and ethical solutions (Barabási 2013). The need for a dispute of the social and ethical impact of big data is currently underestimated (e.g. Booch 2014), due to an inherent and precarious misconception. Big data are not the philosopher’s stone (Crail 2015). Contrary to ← 1 | 2 → the opinion of some researchers (e.g. Anderson 2008), they are not ever going to reveal a certain and objective truth (Van Dijck 2014). Data are subjective, contextualized, heterogenic, and incomplete (Dalton & Thatcher 2014), while at the same time emitting an “aura of truth, objectivity, and accuracy” (Boyd & Crawford 2012: 664). Partly misled, humans overestimate the preciseness of big data due to the seeming objectiveness and become overconfident on the basis of data (Miller, C. C. 2015). However, shaped by such a narrative (Kosslyn 2015), big data narrow down the image of human behavior excessively and focus on standardized archetypes. Big data contribute to a “demystification of the world” (Weber 1919: 9). Interestingly, the technology behind big data, however, is currently being placed inside of a black box (Pasquale 2015), itself becoming something inscrutable (LaFrance 2015), something mystical (in analogy to Clarke 1977). At the very least, Drucker’s statement (1967) that the computer is the moron, is no longer valid (Dewhurst & Willmott 2014).

There are reasons for outsourcing work and decisions to big data. In a complex world like the one we are living in, decisions need to be made in real time and under the pressure of a fluctuating and volatile environment which, therefore, makes constant change the new “stable” condition (Farjoun 2010). No human is capable of handling such massive complexity without the support of other humans and/or technological augmentations (Anderson & Rainie 2012). Big data are seemingly a technological enabler. Big data are a mixed blessing, supposedly capable of solving nearly any problem, but also the source of a staggering amount of new problems. Consequently, the mere use of big data will not suffice.

Deficit 1: Big data are not researched from a human perspective (Ekbia et al. 2015) and without a focus on the human factor (Zuboff 2014).

It is stated (Chen et al. 2012) that the usage of big data makes people’s behavior more calculable and predictable. On the one hand, there is always the danger of employees believing that they are watched, much like a post-panopticon (Bauman 2000, Bakir 2015) or the electronic whip (West & Bowman 2014). This causes them to adapt their behavior. At first, big data may resemble Taylorism and could possibly lead to Taylorism 2.0 (deWinter et al. 2014), both with negative connotations, although at a second glance Taylorism has the benefit of being comprehensible. On the other hand, the algorithms behind big data are becoming increasingly unintelligible and potentially inaccurate (Kleinberg & Mullainathan 2015). From a technological perspective, we occupy a land of milk and honey where we can “gather data first, produce hypotheses later” (Servick 2015: 493). But, as Davis states, “Big data is pushing us to consider serious ethical issues including whether certain uses of big data violate fundamental civil, social, political, and legal rights” (2012: viii). This discourse is currently lagging behind the technological progress (Kitchin 2014a), despite the increasing significance of big data (Shaw 2014) and the “need of deeper critical engagement” (Crawford et al. 2014: 1664). Given the undeniable potential of big data to solve major problems, such discussion is of utmost importance. ← 2 | 3 →

Deficit 2: Big data are not purely technologically driven; they are a social phenomenon. However, the relation between big data and society is highly underresearched.

Big data are closely entangled with humans, as they only unfold their potential when utilized. Big data do not magically develop solutions and do not work independently from humans. It is, therefore, impossible to separate big data from human interaction. Big data may act as a black box. People may not understand the way big data work and may suspect they have a life of their own. Big data have a strong impact at the human level and will influence people drastically (Mayer-Schönberger & Cukier 2013). An interdisciplinary approach to this upcoming discussion is essential since the context of implications will vary. Situational environments determine the use of big data. Differences become obvious in the relationships between government and citizen (Kim et al. 2014), supplier and customer (Strong 2015), and employer and employee (Davenport 2014). Transferring big data strategy to another relationship without making contextual adjustments bears the danger of being inappropriate and even harmful. One field of human interaction is economic organization and, in particular, the usage of big data concerning employees. Employees as an integral part of an organization are neither enemies nor mere resources to be exploited, but rather an employers’ partner with shared interests. This makes finding a potential competitive advantage for the company by adapting big data appropriately a delicate process. It might burden the trusting relationship between employer and employee. Marketing methods applying the shotgun principle are promising as they could lead to an increase in sales (Mayer-Schönberger & Cukier 2013), but using such methods with employees may disrupt the employer-employee relationship and harm commitment, performance, and retention.

Deficit 3: Big data are researched in a general way and not from a contextual viewpoint. In particular, the effects of big data in economic organizations are underresearched.

Within an organization, and especially within corporations, every use of big data will influence human relations (Harvard Business Review 2013). Even big data use in apparently nonadjacent fields will have an effect. The use of big data in research and development, for example, will lead to the creation of new products, and new products will impose different requirements of knowledge and skills onto employees. Big data are, therefore, bound to change work within organizations. One point of intersection of big data and humans to be considered is the human resource (HR) department. As a result of electronic human resource management (HRM), HRM have a long history of collecting and applying data. Using data in the analysis of employee relations is not a new turn, but the vastness of available data will represent a challenge to HRM. There is already a lot of information about the employees available to use (Kull 2016). It seems logical that not every individual member of an organization will handle big data but big data require steering by ← 3 | 4 → some entity within. The interests of both employers and employees will be incorporated into the use of big data. Consequently, big data as a technology will be driven by the IT department, however, as a social and human phenomenon will be designed and implemented by the HR department. At the moment, this discussion is predominately driven by practitioners and focuses on operational implementation. Big data will be a transformative power, but they are shaped by the people in the organization. The HR department can use big data to transform the organization proactively and adapt a new role, or leave this emergent but critical field to other departments. HRM will need to reinvent itself in order to deal with big data and use them for their purposes.

Deficit 4: Big data will force HRM to change and assume a new role in the organization. However, it is unclear what this role will look like.

Biographical notes

Tobias M. Scholz (Author)

Tobias M. Scholz is currently holding a position as a Post-Doctoral Researcher at the University of Siegen. After graduating from universities in Germany and the U.S., he has worked as a Research and Teaching Assistant. His field of research is human resource management and organizational behavior.


Title: Big Data in Organizations and the Role of Human Resource Management