DATA OVER INTUITION' - HOW BIG DATA ANALYTICS REVOLUTIONISES THE STRATEGIC DECISION-MAKING PROCESSES IN ENTERPRISES - DIVA

 
DATA OVER INTUITION' - HOW BIG DATA ANALYTICS REVOLUTIONISES THE STRATEGIC DECISION-MAKING PROCESSES IN ENTERPRISES - DIVA
‘Data over intuition’ –
How big data analytics revolutionises the
strategic decision-making processes in enterprises
A single case study of IKEA

                              MASTER THESIS WITHIN: Business Administration
                              NUMBER OF CREDITS: 30 ECTS
                              PROGRAMME OF STUDY: Digital Business
                              AUTHOR: Finn Brand, Filip Höcker
                              TUTOR: Matthias Waldkirch
                              JÖNKÖPING May 2020
DATA OVER INTUITION' - HOW BIG DATA ANALYTICS REVOLUTIONISES THE STRATEGIC DECISION-MAKING PROCESSES IN ENTERPRISES - DIVA
Master Thesis in Business Administration
Title:          ‘Data over intuition’ – How big data analytics revolutionises the strategic decision-
                making processes in enterprises
Authors:        Finn Brand and Filip Höcker
Tutor:          Matthias Waldkirch
Date:           2020-05-18
Subject terms: Big data; Big data analytics; Strategic decision-making; Strategy-as-practice, Data-
driven decision-making

Abstract

Background: Digital technologies are increasingly transforming traditional businesses, and their
pervasive impact is leading to a radical restructuring of entire industries. While the significance of
generating competitive advantages for businesses utilizing big data analytics is recognized, there is
still a lack of consensus of big data analytics influencing strategic decision-making in organisations.
As big data and big data analytics become increasingly common, understanding the factors
influencing decision-making quality becomes of paramount importance for businesses.

Purpose: This thesis investigates how big data and big data analytics affect the operational strategic
decision-making processes in enterprises through the theoretical lens of the strategy-as-practice
framework.

Method: The study follows an abductive research approach by testing a theory (i.e., strategy-as-
practice) through the use of a qualitative research design. A single case study of IKEA was
conducted to generate the primary data for this thesis. Sampling is carried out internally at IKEA
by first identifying the heads of the different departments within the data analysis and from there
applying the snowball sampling technique, to increase the number of interviewees and to ensure
the collection of enough data for coding.

Findings: The findings show that big data analytics has a decisive influence on practitioners. At
IKEA, data analysts have become an integral part of the operational strategic decision-making
processes and discussions are driven by data and rigor rather than by gut and intuition. In terms of
practices, it became apparent that big data analytics has led to a more performance-oriented use of
strategic tools and enabling IKEA to make strategic decisions in real-time, which not only increases
agility but also mitigates the risk of wrong decisions.

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DATA OVER INTUITION' - HOW BIG DATA ANALYTICS REVOLUTIONISES THE STRATEGIC DECISION-MAKING PROCESSES IN ENTERPRISES - DIVA
Acknowledgements

This Master Thesis and the research process would not have been possible to conduct without
certain people who deserves the utmost appreciation.

Firstly, an especial thank you goes out to IKEA, and the employees which has provided valuable
information by voluntarily participate in the interviews performed in the case study.

Secondly, we would like to express our special appreciation and deep gratitude to their supervisor
Matthias Waldkirch for his extensive and valuable support and constructive criticism of this work.

Finally, we do not want to miss the opportunity to thank all the members of their seminar group
(Ouafaa Cherradi & Cansu Tetik, Stephanie Muth & Marius Rauscher, and Sandra Henkel & Gesa
Köhrbrück) who gave valuable and constructive feedback in each of the seminars.

________________________                                           ________________________
Finn Brand                                                          Filip Höcker

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Table of contents

1   Introduction .................................................................................................................. 1
            Background .................................................................................................................................. 1
            Problem ........................................................................................................................................ 2
            Purpose ......................................................................................................................................... 3
            Research question ....................................................................................................................... 4
            Delimitations ............................................................................................................................... 5
2   Literature review and theoretical framework................................................................ 6
            Frame of references .................................................................................................................... 6
            Literature collection .................................................................................................................... 6
            Literature review ......................................................................................................................... 7
    2.3.1       Big data .................................................................................................................................... 7
       2.3.1.1           The V’s of big data........................................................................................................ 7
       2.3.1.2           Definitional perspectives ............................................................................................. 8
    2.3.2       Big data in enterprises............................................................................................................ 9
       2.3.2.1           Big data value creation ................................................................................................. 9
    2.3.3       Big data analytics capabilities .............................................................................................. 10
       2.3.3.1           Tangible resources ...................................................................................................... 11
       2.3.3.2           Human resources ........................................................................................................ 11
       2.3.3.3           Intangible resources .................................................................................................... 11
    2.3.4       The role of big data in decision-making ........................................................................... 12
       2.3.4.1           Knowledge creation through big data...................................................................... 13
       2.3.4.2           Locus of data-driven decisions ................................................................................. 14
    2.3.5       The role of big data analytics in strategic processes ....................................................... 14
       2.3.5.1           The changing context of strategy ............................................................................. 15
            2.3.5.1.1 Growing number of relevant data sources ......................................................... 15
            2.3.5.1.2 Top-down vs. ad hoc information flow .............................................................. 17
       2.3.5.2           Beneficiaries of data-driven strategies...................................................................... 17
       2.3.5.3           Strategic decision-making and big data .................................................................... 18

            Theoretical framework ........................................................................................ 20
    2.4.1       Motivation for the choice of theory .................................................................................. 20
    2.4.2       Strategy-as-practice .............................................................................................................. 20
       2.4.2.1           Practitioners ................................................................................................................. 21

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2.4.2.2          Practices ........................................................................................................................ 21
       2.4.2.3          Praxis ............................................................................................................................. 22
       2.4.2.4          Strategy-as-practice and big data............................................................................... 22

3   Methodology ............................................................................................................... 23
            Research philosophy................................................................................................................. 23
            Research purpose ...................................................................................................................... 24
            Research approach .................................................................................................................... 24
            Research strategy....................................................................................................................... 26
            Research context ....................................................................................................................... 26
    3.5.1       Empirical investigation ........................................................................................................ 26
    3.5.2       Purposeful sampling ............................................................................................................ 27
    3.5.3       Interview process ................................................................................................................. 28

            Data analysis .............................................................................................................................. 29
            Credibility, transferability, dependability, and confirmability of research........................ 30
            Ethical considerations .............................................................................................................. 31
4   Empirical findings ...................................................................................................... 33
            Strategic decision-making at IKEA........................................................................................ 33
    4.1.1       Practitioners .......................................................................................................................... 35
    4.1.2       Practices ................................................................................................................................. 38
    4.1.3       Praxis ...................................................................................................................................... 40

5   Discussion ................................................................................................................... 43
            The changing context of strategic decision-making ............................................................ 43
    5.1.1       Altered role of practitioners ............................................................................................... 46
    5.1.2       Shifting premises of practices............................................................................................. 48

6   Conclusion .................................................................................................................. 51
            Research question ..................................................................................................................... 51
            Theoretical implications ........................................................................................................... 51
            Managerial implications ........................................................................................................... 52
            Limitations ................................................................................................................................. 53
            Future research .......................................................................................................................... 54
7   References ................................................................................................................... 55

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Figures

Figure 1 - “Big Data chain” - Source: Janssen et al. (2017) .................................................................... 9
Figure 2 - “Paradigm of Big Data processing” - Source: Wang et al. (2016) ..................................... 10
Figure 3 - “The framework of Big data decision making”- Source: Wang et al. (2016) .................. 13
Figure 4 - “Increased of processed data in enterprise information systems” - Source: Schmidt &
         Möhring (2013)............................................................................................................................ 16
Figure 5 - “The decision-data quadrants” - Source: Intezari & Gressel (2017) ................................ 18
Figure 6 - “How big data revolutionises strategic decision-making in enterprises” - Own
         representation .............................................................................................................................. 45

Tables

Table 1 - Interviewees ................................................................................................................................ 28
Table 2 - 2nd order themes and aggregate dimensions ......................................................................... 35

Appendix

Appendix A - Search syntax on online databases .................................................................................. 62
Appendix B - Topic Guide ........................................................................................................................ 63
Appendix C - Data structure ..................................................................................................................... 65
Appendix D - Secondary data ................................................................................................................... 66

Abbreviation List
 BD                                           Big data
 BDA                                          Big data analytics
 ERP                                          Enterprise resource planning
 INGKA                                        The largest franchisee taker of the IKEA franchise
 MNC                                          Multinational corporation
 SD-SD                                        Structured decisions based on structured data
 SD-UD                                        Structured decisions based on unstructured data
 UD-SD                                        Unstructured decisions based on structured data
 UD-UD                                        Unstructured decisions based on unstructured data

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1    Introduction
__________________________________________________________________________________________

Research problem, research purpose, and research question are presented and explained to create an understanding
of the concepts discussed later.

       Background

The rapid pace the business environment is transforming in is not merely causing ambiguity when
looking at future trends, but also, significant challenges are put in front of organisations (Kotter,
2014). By implementing big data analytics within an organisation, possibilities arise to disrupt the
structure within senior management along with changing how decision are being made (Gupta &
George, 2016; Merendino, Dibb, Meadows, Quinn, Wilson, Simkin, & Canhoto, 2018; Vidgen,
Shaw, & Grant, 2017; Wang, Xu, Fujita, & Liu, 2016). Janssen, van der Voort, & Wahyudi (2017)
are highlighting the disruptive effect that the usage of big data analytics has had on current ways
of working for both directors as well as decision-makers. A trend is identified where there is a
knowledge shift, and decision-making mandates are shifted in organisations (Bumblauskas, Nold,
Bumblauskas & Igou, 2017). Instead of having experience and intuition as grounds for a decision,
data is being used to understand how to make the correct decision (Gupta & George, 2016;
McAfee & Brynjolfsson, 2012). Furthermore, Merendino et al. (2018) argues that the rapid
emergence of big data analytics is enabling decision-makers to make decisions based on higher
quality data, and by doing so, being able to make quicker and most importantly, make better
decisions. The tool big data has quickly become among the biggest concepts for companies to
apply for optimizing business operations during the 21st century (Grover, Chiang, Liang & Zhang,
2018; Sivarajah, Kamal, Irani & Weerakkody, 2017). As the usage of big data analytics is becoming
standard practice, companies are reporting increases in productivity, as well as improving areas
within decision-making (McAfee & Brynjolfsson, 2012; Sheng, Amankwah-Amoah & Wang,
2017). Researchers argue that big data analytics has changed and transformed business models as
well as enabling decision-makers to act on structural changes promptly (Rehman, Chang, Batool,
& Wah, 2016; Sheng et al., 2017). The rapid increase in interest and usage for big data has emerged
as e-commerce firms apply big data analytics, and in the process transforming into a data-driven
company (Grewal, Roggeveen, & Nordfält, 2017). By applying big data analytics in the decision-
making process, possibilities are enabled to easier make the correct decisions and developing
strategies that are well-grounded in organisational data (Intezari & Gressel, 2017; Pauleen & Wang,
2017). The net result is being able to construct strategies that have a higher certainty of success

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compared to previous methods (Bumblauskas et al., 2017). With the emergence of big data,
companies are enabled to make decisions grounded in information that previously has been
difficult to understand and analyse (Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016). By
analysing and applying big data within a company, possibilities to identify patterns and factors that
are impacting the ways customers navigate and make purchasing decisions (Grewal et al., 2017).
However, in the process a company needs to invest in creating digital capabilities, i.e. becoming a
data-driven company (Bharadwaj, El Sawy, Pavlou, & Venkatraman, 2013). By creating and
developing digital capabilities decision-makers are enabled to understand what they need to do to
predict patterns to improve their decision-making abilities (Janssen et al., 2017). However, research
also states that there is a gap at the individual level of directors, stating that shortages are found in
the “cognitive capabilities” (Merendino et al., 2018, p. 74) which are necessary to be able to grasp
the full potential of big data, i.e. shortcomings in digital capabilities (Merendino et al., 2018).
Furthermore, research also states that implementing big data analytics in the decision-making
process creates a potential risk of “board cohesion” (Merendino et al., 2018, p. 74) to be disrupted
which instead of improving decision-making processes, has negative consequences such as
creating uncertainty and inertia (Merendino et al., 2018). In the rapidly changing business markets
of today, companies cannot afford to disrupt the chain of decision-making, but rather needs to
make it more efficient and lean to increase the competitive gap a company might have towards
their competition (Grewal et al., 2017). To keep relevance within a market, companies can evolve,
develop digital capabilities, and becoming data-driven (Bharadwaj et al., 2013). By becoming a
data-driven company, further analysis is made of the data collected from customers and operations
to effectively increase the capabilities of managing a company and making quick and agile decisions
(McKenzie, van Winkelen, & Grewal, 2011).

      Problem

It is imperative for organisations to come closer to its customers and adapt to the changing
business environment (Bharadwaj et al., 2013; Sheng et al., 2017). Decisions need to be made faster
and with higher accuracy than ever before (Pauleen & Wang, 2017). Furthermore, data has been
collected in firms for many years, however, the knowledge of how to interpret it has historically
been flawed (Bradlow, Gangwar, Kopalle & Voleti, 2017). Another issue has been the management
of the data, whereas the data has not been used to correctly achieve a result, and instead, decisions
are being based on intuition or experience alone (McAfee & Brynjolfsson, 2012). This means that
organisations that are failing to adapt big data analytics are risking of falling behind by not analysing
their operational performance in the same way as the competition (Ghasemaghaei, Hassanein, &

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Turel, 2017; Merendino et al., 2018). The issue many companies currently face is partly the
organisational transformation that needs to be done to start capitalizing on the new technology,
but also to identify in what way and how an organisation can capitalize to decrease the steps and
involvement in decision-making (Akter et al., 2016; Bharadwaj et al., 2013). The risk of making a
miscalculated decision can be high by not being able to analyse the collected data in a correct way
(Zhao, Fan & Hu, 2014). This can ultimately lead to a company making inappropriate decisions
and as a result lose business and fail to capitalize and increase a competitive advantage
(Ghasemaghaei et al., 2017).

      Purpose

The purpose of this research is to investigate how the operational strategic decision-making
process is changing within a firm as big data analytics has been implemented. The aim of this study
is to investigate one of the largest retailers of the world, IKEA, to create an understanding of how
a major retailer can shift and apply big data analytics to become more efficient when making
strategic decisions. It is of high interest to understand how the process of decision-making is
changing and how big data analytics impacts the daily practice of decision-makers and strategists.
As an internationally operating company in the retail industry, IKEA offers an excellent empirical
context for the given thesis, as the multinational group possesses a plethora of transaction-related
customer data, which is mainly generated from furniture sales in its worldwide branches and
increasingly from online sales through its website and its complex supply chain that generates vast
amounts of internal operational data (Ringstrom & Clarke, 2019). In addition, IKEA is monitoring
all parts of the value chain from listening to the customers, developing furniture, manufacturing,
packaging, and distributing to ultimately create the best service for the customers (The IKEA value
chain, n.d.). Thus, the organisation truly processes big data at its disposal, which is systematically
monitored and has a decisive impact on the strategic orientation of the company. The retailer
operates in a competitive industry that is increasingly dominated by big players that also provide
additional value-added services besides furniture. Positioning the company as a mere furniture
retailer is no longer enough to stay ahead of the competition; creative ideas and innovative
products and services for customers are required. IKEA’s vision “to offer a wide range of well-
designed, functional home furnishing products at prices so low that as many people as possible
will be able to afford them” clearly focuses on customer centricity (The IKEA vision and business
idea, n.d.). The retailer is investing in making all future decisions based on a combination of
properly managed data and experience from the co-workers (Dahlin & Lindstan, n.d.)

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With regard to the aforementioned problem, the current market situation within the retailing
industry, company management needs to be rapid when it comes to decision-making to strengthen
a company’s position (Bradlow et al., 2017). As research states, big data analytics are enabling tools
that management can use to make well-grounded decisions based on data that has been difficult
to analyse previously (Merendino et al., 2018). By utilizing this, companies possess the capabilities
of becoming more agile, and in the long run increase their market position (Akter et al., 2016;
Ghasemaghaei et al., 2017). However, as mentioned, implementing big data analytics within a firm
can create a disruptive environment within the decision-making chain, and thereby disorder a
firm’s ability to make agile and quick decisions. Should this happen, companies pose the risk of
losing their competitive advantage quickly (Merendino et al., 2018). The purpose of this study is
to, by support from theory, understand how big data analytics has impacted the practitioners,
practices, and praxis at IKEA making strategic decisions on an operational level, and by doing so,
creating a generality of how companies can capitalize from big data analytics for their strategic
decision-making processes. By investigating how IKEA has acted, this research will understand
how companies can shift dynamically and improving their strategic decision-making process by
utilizing big data analytics. This research is of importance, not only because the phenomenon of
big data analytics is a recent topic, but also to investigate how the tool is simplifying the strategic
decision-making process on an operational level. Furthermore, recent research is focused on the
overall impact BD has had within firms that apply the technology and generally touch upon
organisational performance (e.g. Akter et al., 2016; Côrte-Real, Oliveira, & Ruivo, 2017; Fosso
Wamba, Akter, Edwards, Chopin & Gnanzou, 2015; Gupta & George, 2016). Their research
conducted in the field is of great value, however, there is a gap in how big data analytics is affecting
parts of an organisation in detail, seen from a strategic decision-making point.

       Research question

It is commonly known that big data improves the information flow in organisations, but it is
unclear how it affects strategic decision-making. Thus, the research question that has driven this
thesis focuses on:

RQ: How does the use of big data analytics affect practitioners, practices, and praxis in strategic decision-making
on an operational level?

To answer this research question, the following chapters review the literature on big data and big
data analytics in business, management, and decision-making research to provide a thorough
understanding of the phenomenon and its role in strategic decision-making. The theoretical lens
of the given thesis is grounded in the strategy-as-practice (s-as-p) theory as the research attempts

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to identify the influence of big data analytics on humanized management and organisation
practices. This thesis attempts to contribute to the research problem by answering the given
research question. More specifically, the goals of this thesis are:

•   To identify how big data analytics affects the strategic decision-making processes in an
    organisation on an operational level.
•   To analyse the impact of big data analytics on practitioners, practices, and praxis.
•   To provide practical implications for data-driven strategic decision-making.

This thesis is organised into four parts. First, a literature review on BD and BDA and how it is
affecting strategic decision-making processes in enterprises. Continuing with the theoretical
framework that has driven the empirical investigation of this study. Thereafter, a chapter touching
upon the methodology of the empirical research, followed by findings of the empirical study.
Finally, practical implications are discussed, and conclusions are drawn.

      Delimitations

Although the need for implementing big data analytics can have a big impact in many industries
and organisations, a delimitation of this thesis has been to concentrate on one multinational
corporation (MNC) that for many years has collected a lot of data, but never had a deep analysis
of it. Firstly, this decision was made partly in with the reasoning of narrowing down the scope of
this thesis, as a type of this research by nature can become were extensive. Secondly, this research
was delimited to one MNC due to the lack of agility that can be compared with smaller
organisations. Also, it is acknowledged that many perspectives could be taken into consideration,
as this master’s thesis has been conducted within the field of digital business, it has adopted a
combined view of a digital point of view and a managerial point of view. Considering this aspect,
the interviewees encountered has been chosen accordingly. Furthermore, due to the scope of the
thesis, the study has been delimited to collect data in the form of semi-structured in-depth
interviews from a single company. As a result of the delimitations mentioned, difficulties arise to
develop general conclusions across industries or companies. However, the aim of this research is
to deliver generalities of how big data analytics could affect the strategic decision-making process
within a company, and by doing so, generate guidelines for future studies.

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2    Literature review and theoretical framework
__________________________________________________________________________________________
The purpose of this chapter is to provide the theoretical background for this thesis. First, a literature review
examines what is understood by the term big data and big data analytics and how it is affecting strategic decision-
making processes in an enterprise. Thereafter, the theoretical framework is presented.

       Frame of references

Contemporary research on business and management offers evidence that big data and big data
analytics are directly linked to strategic decision-making processes within organisations (Grover et
al., 2018; Intezari & Gressel, 2017; Janssen et al., 2017; Pauleen & Wang, 2017; Sheng et al., 2017;
Wang et al., 2016). The interdisciplinarity regarding the distinct academic domains (i.e. big data
analytics, decision-making, and strategy) demand separate disciplinary investigations in the
following literature review. Recently published business and management studies investigated the
effects of big data analytics on strategy- and decision-making processes (Gupta, Kar, Baabdullah,
& Al-Khowaiter, 2018; Intezari & Gressel, 2017; Janssen et al., 2017; Merendino et al., 2018).
Further, by complementing the business and management disciplines with research in decision
science, the influence of big data analytics on human decision-making processes can be elucidated
in-depth (Intezari & Gressel, 2017; Pauleen & Wang, 2017). Research from the aforementioned
disciplines frame the scope of references and are presented in the following paragraphs.

       Literature collection

To make sure only to have relevant and true literature, the databases JU Primo, Web of Science,
and Google Scholar have been used. In addition, when selecting the relevant literature, care was
taken to ensure that the academic articles were taken from high-quality journals that are respected
in the relevant disciplines. To determine the current state of literature concerning the
phenomenon, search syntaxes such as “big data” and “big data analytics” were used in the
databases. Furthermore, to specify the search and narrow down the topic, keywords such as
“decision-making” and “strategic decision-making” were entered, separately and combined with
“big data” and “big data analytics”. To ensure that the correct domain-specific keywords were
used, alternative terms and phrases such as “BD”, “BDA”, “data”, “data analytics”, “strategy
making”, “operational decision-making”, “operational strategies” were entered. In addition to the
keyword search, as soon as relevant literature was found, further searches within the found
literature were carried out to expand the collection of sources. An overview of the search syntaxes
and the corresponding databases can be found in Appendix A.

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Literature review

2.3.1     Big data

There exists no universal definition of the term “big data” and scholars suggested that big data can
be considered as a “moving definition”, which varies in its meaning depending on the ever-
increasing nature of this phenomenon (Gupta et al., 2018; Sheng et al., 2017). Due to its ubiquity,
the term is used in various disciplines and thus has a wide range of definitions and interpretations
(Mikalef, Pappas, Krogstie & Giannakos, 2018). Moreover, there is no fixed technical threshold
for measurement of what size and type of data can be treated as big data, but there appears to be a
consensus in the literature about its unique characteristics that distinguish big data from
conventional data, namely the V’s of big data (Akter et al., 2016; Grover et al., 2018; Gupta &
George, 2016; Janssen et al., 2017; Sheng et al., 2017). Although big data has various definitions,
for the further elaboration of this thesis the authors refer to a definition widely used in the literature
to define big data as:

          “[…] extremely large amount of structured, semi-structured or unstructured data
          continuously generated from diversified sources, which inundates business operations in
          real-time and impacts on decision-making through mining insightful information from
          rambling data.” (Sheng et al., 2017, p. 98)

2.3.1.1    The V’s of big data

This differentiation from conventional data helps in understanding and classifying the concept of
big data. According to Gupta & George (2016), the term big data was initially coined to reflect the
“bigness” or voluminous size of data generated as a result of using new forms of technology. A
recently published study by Mikalef et al. (2018) coincide with these assumptions and state that in
addition to the volume property two other characteristics, namely velocity and variety met the unique
characteristics of big data. Their study reviewed several publications and consolidated various
definitions of big data and its associated attributes. The majority of the reviewed publications
described its uniqueness with three Vs, namely volume, which “[...] refers to the sheer size of the
data set due to the aggregation of a large number of variables and an even larger set of observations
for each variable.” (Mikalef et al., 2018, p. 554), velocity, which “[...] reflects the speed at which
these data collected, updated, and analysed, as well as the rate at which their value becomes
obsolete.” (Mikalef et al., 2018, p. 554) and variety, which “[...] refers to the plurality of structured
and unstructured data sources, which, amongst others, include text, audio, images, video,
networks, and graphics.” (Mikalef et al., 2018, p. 554). However, due to the ever-changing

                                                    7
character, big data is constantly redefined, and several scholars have supplemented these three
attributes with other Vs. A commonly acknowledged aspect is its veracity, which “[...] refers to
the degree to which big data is trusted, authentic, and protected from unauthorized access and
modification.” (Mikalef et al., 2018, p. 555). And further, some scholars refer to its value, which
“[...] refers to the value it’s creating for organisations.” (Mikalef et al., 2018, p. 555) as the fifth
attribute (Grover et al., 2018; Gupta et al., 2018; Janssen et al., 2017; Mikalef et al., 2018; Rehman
et al., 2016; Sheng et al., 2017; Wang et al., 2016).

Studies that primarily investigate the influence of big data on strategic decision-making processes
within organisations, which is also the focus of this thesis, are mainly concerned with the velocity
attribute (Intezari & Gressel, 2017; Merendino et al., 2018). Velocity plays a decisive role in
strategic decisions, as the development of technological data-driven infrastructures enables data to
be managed in “[…] continuous flows and processes.” (Davenport, Barth & Bean, 2012, p. 23),
allowing decisions to be made in real-time (Intezari & Gressel, 2017). The following paragraphs
provide a working definition of big data.

2.3.1.2 Definitional perspectives

Wang et al. (2016) distinguish four distinct perspectives for defining BD, such as “product-
oriented”, “process-oriented”, “cognition-oriented” and “social movement” perspective (Wang et
al., 2016, p. 749). All of which highlight and emphasize different attributes concerning what
constitutes BD. The product-oriented perspective highlights the attributes of data regarding their
sizes, speeds, and structures (Wang et al., 2016). Definitions that are classified as a process-oriented
perspective concentrate on the novelty of processes required and involved in storing, managing,
aggregating, and analysing BD (Wang et al., 2016). The cognition-based perspective focuses on the
challenges caused by big data concerning their cognitive capacities and limitations (Wang et al.,
2016). Finally, the social movement perspective draws attention to the gap between vision and reality,
especially the socioeconomic, cultural, and political shifts that underlie the presence of big data
(Wang et al., 2016).

Since this thesis attempts to broaden the scope of reviews in the field of business and management
by investigating the influence of big data analytics on the strategic decision-making processes in
organisations, only the first three perspectives, namely product-oriented, process-oriented, and
cognition-oriented, are taken into consideration as relevant definitional perspectives for the given
investigation.

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2.3.2   Big data in enterprises

           “Big data has rapidly moved to being a mainstream activity of organisations.”
                                 (Janssen et al., 2017, p. 338)

Over the past two decades, the field of big data in the business context has become increasingly
important (Gupta & George, 2016). While organisations in the past have primarily focused on
enterprise-specific structured data (i.e., data that can be stored in relational databases) to make
business decisions, today’s organisations tend to capture every bit of information regardless of the
size of data, the structure of data, and the speed at which data are created (Zhao et al., 2014).
Hence, structured data is no longer the only type in enterprises – rather more unstructured and
semi-structured data is processed to support strategic decision-making processes internally (Sheng
et al., 2017). Unstructured data occurs in various forms such as text (e.g. documents), web data
(e.g. web usage, web content), social media data (e.g. online platforms), multimedia data (e.g. image,
audio, video) and mobile data (e.g. sensor, geographical location, application) (Sheng et al., 2017).
However, the mere collection of data alone does not add any value to decision-makers, enterprises
require holistic data-driven ecosystems to grasp the full potential (Gupta & George, 2016).

2.3.2.1 Big data value creation

Well-cited academic papers investigated various data-driven enterprises and developed models,
frameworks, and so-called blueprints for businesses to turn data into relevant information for
decision-makers (Grover et al., 2018; Gupta & George, 2016; Janssen et al., 2017; Wang et al.,
2016). Although these frameworks are designed based on varying theoretical assumptions, they
provide a solid understanding of data-driven infrastructures and ecosystems that effect decision-
making. Moreover, while studying and analysing these models, patterns became apparent, which
will be highlighted in more detail in the following part.

These models, i.e. the “Big data chain” (Janssen et al., 2017, p. 340) and the “Paradigm of Big Data
processing” (Wang et al., 2016, p. 751), have similarities in their design. Both models illustrate how
big data can provide information for decision-making. According to Janssen et al. (2017), a big
data chain begins with collecting data and ends when decisions are made (see Figure 1).

                           Figure 1 - “Big Data chain” - Source: Janssen et al. (2017)

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Wang et al. (2016) coincide with these notions as their framework encompasses intelligent
decision-making based on data as an outcome of their process (see Figure 2). Both models are in
affect similar in their design. They start with data collection/data capture steps, which include the
gathering and collating of data. Followed by a preparation or curation phase, as the raw data
requires a processing measure before it gets analysed in a third step. Lastly, the result of the analysis
is interpreted by a decision-maker, who has the knowledge to utilize the results for appropriate
decisions (Janssen et al., 2017; Wang et al., 2016). These decisions are dependent on the strategic
goals and orientations of the enterprise (Janssen et al., 2017).

                       Figure 2 - “Paradigm of Big Data processing” - Source: Wang et al. (2016)

An integral part of these models is an analysis step that is indispensable to gain insights for data-
based decision making. The following chapter gives an outlook on the practice of big data analytics.

2.3.3   Big data analytics capabilities

The creation of big data analytical capabilities is indispensable for companies that want to translate
data into insights (Gupta & George, 2016). In essence, big data analytics encompasses not only
the data upon which analysis is performed, but also elements of tools, techniques, skills, and
capabilities (Mikalef et al., 2018). Gupta & George (2016) identify three key building blocks of big
data analytical capabilities: Tangible resources (i.e., data, technology, and basic resources), human
resources (i.e., managerial skills, and technical skills), and intangible resources (i.e., data-driven
culture and organisational learning). In the following subchapters, the three categories are
examined in more detail to understand the big data capabilities in their entirety.

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2.3.3.1 Tangible resources

Gupta & George (2016) argue, that firms need to invest in advanced technologies (e.g. database
management systems, cloud computing, data warehouses, etc.), software (e.g. Hadoop, Microsoft
Azure, Tableau, etc.), and programming languages (e.g. Python, R, SQL) to be able to store,
process and analyse diverse data sets. Thus, for example, highly decentralized multinational
corporations with several international branches, such as IKEA or other players in the online
retailing industry, can gain access to databases from multiple branches via cloud-based solutions
(Sivarajah et al., 2017). Although these systems and resources are readily available for all firms and
are unlikely to provide any competitive advantage on their own, yet they are required to create
capabilities (Gupta & George, 2016).

2.3.3.2 Human resources

Further, Gupta & George (2016) emphasize on two categories of human resources that are
necessary to build big data capabilities: Managerial and technical skills. Managerial skills demand
strong analytical acumen and the ability to put data analytical results into a strategic perspective to
make organisational-wide decisions (Gupta & George, 2016). Managers believing in big data
should emphasize finding the right human capital including technical, analytical, and governance
skills for data analysts (Gupta & George, 2016). Technical skills are highly sophisticated and some
of these skills include competencies in machine learning, data cleaning, statistical analysis, and
understanding of programming languages (Davenport, 2014). Centralized departments can assist
in consolidating capabilities and skills, thus leveraging accumulated know-how. Departments and
teams, namely Business Analytics (BA), Business Intelligence (BI), Centre of Excellence (CoE),
and Data Science teams process, analyse, and visualize data that build the foundation for decision-
makers in various business operations such as process management, market intelligence, sales
forecasting, price optimization, and inventory management (Grover et al., 2018; Gupta & George,
2016).

2.3.3.3 Intangible resources

Moreover, Gupta & George (2016) differ between two intangible resources that make data
analytics capabilities: Data-driven culture and intensity of organisational learning. To realize the
full potential, firms must develop a data-driven culture (Gupta & George, 2016). Given that
employees at all levels in an organisation are required to make decisions, it is pertinent to diffuse
the culture of data-driven decision-making to all levels such as that organisational members,
regardless of their job titles, can make decisions that are grounded on evidence as suggested from

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data (Ross et al., 2013). Once, data-driven infrastructures and big data capabilities are established,
managers might concentrate on how to retain it and even sense and seize novel opportunities
(Gupta & George, 2016). Firms that can learn and reconfigure their resources according to changes
in their external environment will likely have a sustained competitive advantage (Gupta & George,
2016).

As Gupta & George (2016) state, the purpose of processing big data is to exploit knowledge from
data to support intelligent decision-making, the following section analyses how big data creates
knowledge for decision-making.

2.3.4    The role of big data in decision-making

              “Big data’s power does not erase the need for vision or human insight.”
                           (McAfee & Brynjolfsson, 2012, p. 65)

As the previous paragraphs emphasize, has big data prompted decision-making processes in
organisations. Decision-making is central to what managers do (Hickson et al., 1989; Michel, 2007;
Stewart, 2006), and it is integrated into all kinds of management functions (Intezari & Gressel,
2017).

While the business and management discipline investigates data-driven decision-making in the
realm of enterprise-specific practices, processes, and structures (Grover et al., 2018; Janssen et al.,
2017; Merendino et al., 2018; Wang et al., 2016; Xu et al., 2016), little research has been conducted
from a psychological perspective to what extent data influences a person’s decision-making
(Intezari & Gressel, 2017; Mazzei & Noble, 2017; Pauleen & Wang, 2017). When investigating
studies in decision science, it becomes apparent that human knowledge and experience are solely
responsible for decisions, therefore negating the influence of knowledge when discussing the
impact of big data on decision-making is impossible (Pauleen & Wang, 2017). The knowledge
explosion that accompanies increasing access to BD has a major impact on how and what
information managers use to inform their decisions (Pauleen & Wang, 2017). Merendino et al.
(2018), investigated the influence of big data on board-level decision-making and argue that
understanding the transformational process of changing meaningless raw data into knowledge is
essential for decision-makers. Hence, before examining the structure and processes associated with
corporate decision-making, it is crucial to understand how data creates knowledge and how it is
incorporated into decision-making.

The definitional complexity and plurality debates around knowledge lead the following
examination to concentrate merely on how knowledge is achieved through big data analytical

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processes, and not on what knowledge is since it has different meanings in different contexts
(Pauleen & Wang, 2017).

2.3.4.1 Knowledge creation through big data

Knowledge is central to any discussion around big data – whether the data generated are used in
operational, tactical, or strategic business domains, knowledge will guide its use (Pauleen & Wang,
2017). Wang et al. (2016) developed the “The framework of Big data decision making” (Wang et al., 2016,
p. 751) that displays the relationships between both phenomena by providing an overview of the
inner-human process of decision-making and connecting it to various stages in an organisation
(see Figure 3). The authors explain that decisions are made by deriving information from data,
obtaining knowledge from information, and then achieving wisdom from knowledge (Wang et al.,
2016).

                 Figure 3 - “The framework of Big data decision making”- Source: Wang et al. (2016)

When looking at the model, two aspects, in particular, are striking. First, the decision-making
process follows a funnel-shaped approach, starting with a vast amount of raw data and ending
with a narrowed knowledge and wisdom peak. Secondly, decisions take place at varying levels
within the organisations (i.e., operational, tactical, strategic, systemic). By processing and analysing
data at the operational level, decisions at subsequent levels are increasingly based on facts and data-
related evidence (Wang et al., 2016). As mentioned, Wang et al. (2016) locate decisions on an
operational, tactical, strategic, and systemic level. To understand how big data analytics influences
decisions at these levels, the following chapter is dedicated to this subject.

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2.3.4.2 Locus of data-driven decisions

Decisions are made in varying organisational settings and are mostly initiated with particular
business objectives in the decision-makers’ minds (Pauleen & Wang, 2017). According to Pauleen
& Wang (2017), decisions only become meaningful when they are classified and brought into
relation by decision-makers, the underlying concept is called contextual knowledge. Combing it with
the three levels of decision-making proposed by Merendino et al. (2018), varying types of decisions
favoured by big data become clearer. The authors outline three levels for data-driven decision-
making within enterprises: The director/individual level, the board level, and the stakeholders’
level (Merendino et al., 2018). At the director/individual level, managers and professionals utilize
data-driven analytics to make operational decisions for day-to-day business matters (e.g., project-
based decisions, performance-related decisions, product- and service-related decisions, etc.)
(Merendino et al., 2018). At the board level, big data analytics can deliver insights to more complex
decisions (e.g., structural decisions, global supply chain decisions, etc.) (Merendino et al., 2018).
The increased number of Chief Data Officers (CDO), Chief Information Officers (CIO), and
Chief Analytics Officer (CAO) on the boards of directors show that decision-makers at board level
are placing greater emphasis on technical and data analytical skills in their decision-making (Côrte-
Real et al., 2017; Mazzei & Noble, 2017). At the stakeholder level, companies are expected to
respond to environmental changes by correctly anticipating the stakeholders’ needs (Merendino et
al., 2018). By providing new insights into environmental trends, big data can empower corporate
decision-makers to respond and adapt to current dynamic economic demands (e.g., market
decision, governmental restrictions, fiscal policies, etc.) (Erevelles et al., 2016; Merendino et al.,
2018).

The following chapter examines how big data is tied to strategic processes. Here, the links between
big data analytics and strategic processes are discussed in-depth.

2.3.5    The role of big data analytics in strategic processes

   “Big data in effect multiplies the potential of organisational data engagement and the shaping of
                                    enterprise strategy processes.”
                                      (Bhimani, 2015, p. 3)

The chapters in the previous sections indicate that data enables business decisions to be more far-
reaching and conclusive. At a strategic level, data can be utilized to target more effective
interventions in areas that so far have been dominated by gut and intuition rather than by data and
rigor (Bhimani, 2015; Constantiou & Kallinikos, 2015; McAfee & Brynjolfsson, 2012). Making

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effective strategic decisions is one of the critical abilities that managers are required to have and
develop to lead their organisations in the increasingly volatile and competitive business
environment (Intezari & Gressel, 2017). As Porter (1985) emphasizes, the success or failure of a
firm relies mainly on the managers’ competitive ability to make strategic decisions. While in the
past managers operated under conditions of information scarcity with decisions made with
incomplete and often unstructured binary data, big data create conditions of information
abundance due to the massive amount of detailed data made available, which enables strategic
decisions (Intezari & Gressel, 2017).

2.3.5.1 The changing context of strategy

Strategy and decision-making are closely intertwined, as decisions form the foundation for any
strategic initiative (Intezari & Gressel, 2017). Therefore, big data analytics not only facilitates
decision-making processes but also has a considerable effect on the domain of strategy (Intezari
& Gressel, 2017). The academic discourse provides various reasons for this perceptible impact. As
Constantiou & Kallinikos (2015) note, the process of strategy-making is directly linked to models
of data collection (e.g. market research instruments and statistical models of inference,
classification, or management accounting systems), which indicates a direct link between the two
phenomena. These models are loaded with structured (often quantitative) data to provide insights
in a persuasive and argumentative way that can be leveraged for strategic measures (Constantiou
& Kallinikos, 2015). The underlying data enables constant updateability and real-time responses
to organisational activities (Constantiou & Kallinikos, 2015). Real-time updates of operational data
sets provide valuable information and direct feedback on strategic implications. However, the
growing number of data sources driven by big data efforts poses a challenge for enterprises in that
the data arise from wider configurations of information pools (Bhimani, 2015). Big data is not only
produced by internal homogenous data sources, but the majority is also a hugely heterogeneous
base, actively through user-generated content, which unleashes a diversity of strategic re-
orientation possibilities (Constantiou & Kallinikos, 2015; Galbraith, 2014).

2.3.5.1.1 Growing number of relevant data sources

Modern enterprises collect a plethora of data from various sources that are all relevant for strategic
decisions. The literature postulates a distinct classification of organisational data sources. Rehman
et al. (2016) distinguish direct and indirect data sources. Direct data sources in enterprises generate
operational information relevant to supply chain management, production, behaviour analysis of
employees, etc. (Rehman et al., 2016). Indirect information includes data that is not generated

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within the boundaries of the enterprises, such as customer-specific data (Rehman et al., 2016). This
data can be acquired, for example, from websites by analysing customers’ feedbacks and online
product reviews (Rehman et al., 2016).

Likewise, Schmidt & Möhring (2013) followed a similar approach in the classification of data
sources. The authors developed a graph that illustrates different data sources, data types, and data
volume (see Figure 4). The data sources are grouped into four categories, namely Enterprise
Resource Planning (ERP), Customer interactions, Weblogs and Sensor Data, and Social Media
(Schmidt & Möhring, 2013).

     Figure 4 - “Increased of processed data in enterprise information systems” - Source: Schmidt & Möhring (2013)

What is striking, is that both the amount and structure of the data becomes more complex as the
data sources change from internal to external, or from ERP to Social Media. This indicates that
enterprises that increasingly include external data sources (i.e., Social Media) in their strategic
planning must process more complex data sets (Schmidt & Möhring, 2013). Sources of data that
were previously not relevant to strategy-making might become more meaningful, depending on
the strategic orientation of the enterprise (Schmidt & Möhring, 2013). The literature coincides with
this statement, as Constantiou & Kalliniko (2015) argue, that strategy-making through big data is
becoming a part of a wider context of social relationships that are shaped in ways that tend to
redefine organisational boundaries.

Not only the foundations for strategic processes have changed, but also the directions of strategic
processes internally. The following subsection will emphasize on the changed processes.

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