/smash/get/diva2:641706/FULLTEXT01.pdf

/smash/get/diva2:641706/FULLTEXT01.pdf
Uppsala University
Department of Business Studies
How Big Data Analytics are perceived as a
driver for Competitive Advantage
A qualitative studies on food retailers
Alessandro Galletti & Dimitra Papadimitriou
Supervisor: Sabine Gebert-Persson
Spring 2013
Master Thesis – Spring 2013
Alessandro Galletti & Dimitra Papadimitriou
Abstract
The recent explosion of digital data has led the business world to a new era towards a more
evidence-based decision making. Companies nowadays collect, store and analyze huge amount of
data and the terms such Big Data Analytics are used to define those practices. This paper
investigates how Big Data Analytics (BDA) can be perceived and used as a driver for companies’
Competitive Advantage (CA). It thus contributes in the debate about the potential role of IT assets
as a source of CA, through a Resource-Based View approach, by introducing a new phenomenon
such as BDA in that traditional theoretical background. A conceptual model developed by Wade
and Nevo (2010) is used as guidance, where the concept of synergy developed between IT assets
and other organizational resources is seen as crucial in order to create such a CA. We focus our
attention on the Food Retail industry and specifically investigate two case studies, ICA Sverige AB
and Masoutis S.A. The evidence shows that, although this process is at an embryonic stage, the
companies perceive the implementation of BDA as a key driver for the creation of CA. Efforts are
put in place in order to develop successful implementation of BDA within the company as a
strategic tool for several departments, however, some hurdles have been spotted which might
impede that practice.
Keywords: Big Data, Big Data Analytics, Resource-Based View, Competitive Advantage, Retail
Industry, ICA AB, Sweden
Master Thesis – Spring 2013
Alessandro Galletti & Dimitra Papadimitriou
Definitions and Abbreviations
BDA= Big Data Analytics
CA= Competitive Advantage
IT=Information Technology
Organizational resources: “resources that include all assets, capabilities, organizational processes,
firm attributes, information, knowledge, etc. controlled by a firm that enable the firm to conceive of
and implement strategies that improve its efficiency and effectiveness” (Barney, 1991, p.101)
Competitive Advantage: “a process in which the firm is implementing a value creating strategy not
simultaneously being implemented by any current or potential competitors” (Barney, 1991, p.102)
IT resources: technological resources such as tangible assets (in the form of IT hardware and
software), or intangible assets such as technical and managerial IT knowledge (Melville et al.
2004)
IT tangible assets: widely available, off-the shelf or commodity-like information technologies that
are used to process, store, and disseminate information (Wade & Hulland 2004)
IT-enabled resources: relations between IT assets and organizational resources (Wade & Nevo,
2010)
Emergent capabilities: capabilities that neither component can possess by itself (Wade & Nevo,
2010)
Synergies: positive emergent capabilities, or emergent capabilities that result in positive outcomes
(Wade & Nevo, 2010)
Master Thesis – Spring 2013
Alessandro Galletti & Dimitra Papadimitriou
Table of Contents
1.
2.
Introduction............................................................................................................................................... 1
1.1.
Background information................................................................................................................... 1
1.2.
Problem Discussion ........................................................................................................................... 3
1.3.
Research question ............................................................................................................................. 4
1.4.
Thesis Disposition.............................................................................................................................. 4
Literature Review ...................................................................................................................................... 5
2.1.
2.1.1.
Resource Based view................................................................................................................. 5
2.1.2.
Information Technology resources and competitive advantage ............................................ 7
2.1.3.
Conceptual model ..................................................................................................................... 8
2.2.
3.
4.
Big Data Analytics as an IT asset ............................................................................................ 11
2.2.2.
Big Data as a Driver for Competitive Advantage................................................................... 12
Method .................................................................................................................................................... 12
3.1.
Research type .................................................................................................................................. 12
3.2.
Selection of cases ............................................................................................................................ 13
3.3.
Data Collection and Analysis .......................................................................................................... 13
3.4.
Reliability and Validity of research ................................................................................................ 18
Case studies ............................................................................................................................................. 19
ICA Case Study ................................................................................................................................. 19
4.1.1.
ICA case is a Big Data Analytics case...................................................................................... 19
4.1.2.
Big Data Analytics’ implementation within ICA .................................................................... 20
4.1.3.
Big Data Analytics as a driver for competitive advantage in ICA ......................................... 26
4.2.
6.
Big Data Analytics and Competitive Advantage............................................................................ 11
2.2.1.
4.1.
5.
Contribution of IT to Competitive Advantage .................................................................................. 5
Masoutis Case Study ....................................................................................................................... 29
4.2.1.
BDA implementation within Masoutis ................................................................................... 30
4.2.2.
BDA as a driver for CA in Masoutis......................................................................................... 31
Analysis ................................................................................................................................................... 33
5.1.
Potential Synergy and Enablers of Actual Synergy ........................................................................ 33
5.2.
The strategic potential of IT-enabled resources ............................................................................ 35
5.3.
IT Enabled Resources and Competitive Advantage ....................................................................... 37
5.4.
Discussion ........................................................................................................................................ 38
Conclusions and beyond ......................................................................................................................... 40
6.1.
Concluding Remarks ....................................................................................................................... 40
Master Thesis – Spring 2013
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Alessandro Galletti & Dimitra Papadimitriou
6.2.
Contribution .................................................................................................................................... 41
6.3.
Limitations of Study Results and Suggestions for Further Research ............................................. 41
References ............................................................................................................................................... 44
Appendix 1 ...................................................................................................................................................... 50
1. Introduction
1.1. Background information
We live in an era in which data are constantly proliferated and become more easily accessible. The
rise of the Internet has led to a highly interconnected world, which generates an exponential amount
of data that can take many forms, from meteorological calculations and health records, to social
media networks and online services. Within the business field, with millions of transactions being
made online and the rise of the smart-phone applications, data is everywhere, leading to an increase
of a data-driven decision making in business (Harris et al., 2010). Retailers, health care providers,
insurers, financial institutions and other organizations collect large amounts of data on every
transaction.
The retail industry in specific shows an increasing interest in the use of data, because of the high
volume and quality, stemming from Internet purchases and social-network conversations (Brown et
al., 2011). Retailers use customer data to get insight into customers' needs, by using integrated
customer, shopping and behavioral data and in that way they have visibility into user demographics,
buying behaviors and mobile application downloads (Savitz, 2012). They are also exploiting big
volumes of customer data in order to better target customers and gain loyalty, in order to
personalize campaigns, coupons and offers to individual customers. Some of them use techniques to
mine the huge streams of data now generated by consumers using various types of social media,
measure responses to new marketing campaigns in time much quicker than the one of traditional
feedback (Rogers, 2011). As a result, with the use of data, retailers can communicate directly with
their customers, sell smarter and increase margins (Savitz, 2012).
Since the amount of data generated is changing, new technologies are emerging that can analyze
data on a faster pace (McAfee & Brynjolfsson, 2012). That systematic analysis of data and statistics
in business contexts in order to obtain useful information is called analytics (Cooper, 2012). The
latest trend of Information Technology (IT) in the field of data analytics has been named as Big
Data Analytics (BDA) and meets a big interest in business circles. BDA describe how companies
examine large amounts of data of a variety of types in order to obtain useful information (McAfee
& Brynjolfsson, 2012).
Seeing BDA in relation to traditional Analytics, one might ask how the former differ. BDA has
been differently defined, something that shows the existing uncertainty towards that new concept,
Master Thesis – Spring 2013
Alessandro Galletti & Dimitra Papadimitriou
which is also in stage of experimentation as far as its use is concerned. Nowadays, Big Data and
BDA are viral terms that are used to define the data sets and analytical techniques in applications
that are so large and complex, that they require advanced and unique data storage, management and
analysis technologies (Hsinchum et al., 2012). The size of Big Data is a major differentiator from
traditional data, but “big” is not just a matter of size; it might include volume, velocity or variety
(Gobble, 2013). Volume is the increasing amount of business data—created by both humans and
machines. Variety is also about the increasing number of data types and sources that need to be
handled differently from simple email, data logs and credit card records (Preimesberger, 2011). To
be more specific, the variety of Data includes both structured and unstructured data. Unstructured
data lack a predefined structure and normally stem from social networks, web pages and other
similar communication channels. Finally, velocity is about the speed at which this data moves from
end- points into processing and storage (Preimesberger, 2011).
The use of BDA is becoming a trend and several businesses are now investing in BDA, in an initial
level. A survey conducted to Fortune’s 500 biggest corporations in the United States, revealed that
about 85% of respondents already had BDA initiatives planned or in progress (Kiron, 2013). The
potential of BDA is foreseen as tremendous, since it can make information transparent in a higher
frequency, and the collection of data and analysis can lead to better decision making, forecasting of
needs and further adjustment of business. It is observed that by a consistent analysis of Big Data,
firms will transform into ‘intelligent enterprises’ that will enhance their productivity and
competitiveness in the market, by optimizing their operations on precise information coming from
various sources combined (Caesarius & Lindvall, 2012). The BDA phenomenon is forecasted to
expand even more (Parise et al., 2012), and it is also expected to further create new growth
opportunities and new categories of companies (McGuire et al., 2012), by highlighting the path we
are towards a more evidence-based decision making.
Although the term of BDA has been variously defined -mostly concerning the factors that
differentiate them from traditional analytics-, little attention has been drawn in relation to how BDA
can affect organizations’ performance, which may also be due to the recent character of the
phenomenon. The benefits derived from the use of BDA have been forecasted (Savitz, 2012;
McGuire et al., 2012; Brown et al., 2011), but it remains rather unexplored how this new example
of IT can influence firms’ competitiveness and may lead to a Competitive Advantage (CA). This
study will explore how BDA are perceived to affect a firm’s CA.
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1.2. Problem Discussion
In theory, there has been a long debate on what the effect of IT in organization’s performance is and
in what way IT contributes to firms’ competitiveness, but the answer remains rather unclear (Mata
et al., 1995; Mellville et al., 2004; Kohli & Grover, 2008; Wade & Nevo, 2010). Although there is a
recognition of the importance of IT to firms’ competitiveness, academics do not seem to agree on
which the relationship between IT and business performance is, and specifically to how IT can be a
driver of CA to organizations (Wade & Nevo, 2010). In specific, on the one hand, it was argued,
that IT technologies are unlikely to be leading to a CA of a firm, since they are widely available to
all the competition of a market (Mata et al., 1995). However, on the other hand, it was also
observed that those technologies have been shown to provide capabilities which, when combined
with other factors available in a firm-such as the human capital and business knowledge-, can
provide a basis for realization of a CA (Kros et al., 2011; Wade & Hulland, 2004; Wade & Nevo,
2010). As Carr (2003) states, because IT has become a commodity in recent years, it is not
important which technologies companies are adopting, but how they are using them. This confirms
our approach to focus on how BDA are used and perceived to make an organization more
competitive, because that perception influences the way in which an organization uses BDA and
vice versa.
Given that BDA is a very recent example of IT, the existing debate becomes even more acute when
it comes to such a powerful tool: BDA can provide a firm with an amount of data that could not
even be conceived a few years ago (McGuire et al., 2012). Firms have now access to the most
capable information tools so far, but the path between those technologies and CA for firms is rather
unexplored. The purpose of this study is to explore how BDA are used and perceived to affect a
company’s CA. By using the theory developed so far about IT as guidance, we will explore a very
recent IT example, the one of BDA. The study will be focused on the retail industry, because it is an
area of great interest due to the high volume of data being used for analysis (Ggreengard, 2012),
since, as mentioned above, retailers use BDA to analyze various sources from customers’ data in
order to get insight to buying behaviors, personalize offers and increase margins (Savitz, 2012).
Knowledge on how BDA are perceived to lead to CA will contribute to richer insights into the
persisting theoretical problem concerning how IT can lead to firms’ CA. Since the contribution of
IT to business performance is yet unclear (Kohli & Grover 2008), empirical evidence on how
organizations perceive this new and powerful IT example, will also contribute to the existing
debate. The research also contributes to information management by increasing the theoretical and
practical understanding of how BDA can affect firms’ competitiveness and it helps to understand
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the role of such an IT application within a company. Practical implications, useful for organizations
engaging on BDA are the provision of an overview about that recent phenomenon, and helping
organizations on what to expect concerning use of BDA and how this will affect their
competitiveness. This study will help those companies interested in BDA, to better understand how
those can increase their competitiveness either before investing to such a technology, or also after,
in understanding how it should be better used in order to lead to a competitive advantage.
1.3. Research question
The previous discussion leads to the main question of the present research which is the following:
“How are Big Data Analytics used and perceived by an organization to affect its competitive
advantage in retail industry?”
In order to answer that question, we will first provide an overview of the existing literature debate
on IT and how it has been seen in research as a driver for a CA and will then link the theory to the
emerging IT example of BDA and see how BDA are used in retail and perceived to affect the
competitiveness of a firm in our given case. In order to do so, we are going to test a conceptual
model developed by Wade and Nevo (2010) through a case study in the retail industry. Wade and
Nevo (2011) tested already their model through a quantitative study on customer services and found
out that when an IT asset is combined with other organizational resources, the developed synergy
may provide a CA for the company. However, we believe, supported by Saunders (2009), that in
order to understand in depth this new phenomenon and the concepts expressed in the conceptual
model, a questionnaire with closed questions is not reliable enough, even if statistically valid. Thus,
we chose a different approach, which is to test Wade & Nevo’s model (2010) with a qualitative
study, with open questions and a focus on a specific industry, to see if it applies concerning the use
of BDA in retail and firms’ competitiveness.
1.4. Thesis Disposition
The thesis is structured as follows: the present section introduces the research study. It contains
information in order to provide a background to the research area and formulated the research
question. Section 2 continues with a discussion of literature. It presents the existing literature debate
on how IT can lead to a CA, to conclude by linking that literature to the emerging IT example of
BDA. Then, section 3 presents the type of research, the selected strategy behind and the selection of
cases. It also explains the data collection strategies and their analysis, to conclude with the
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limitations and validity of the present study. Section 4 provides the data collected, and section 5
analyses the information gathered from a theoretical perspective, in order to give an answer to the
research question, and draw relevant discussions. Finally, section 6 constitutes the concluding
section of the present thesis; concluding remarks are presented, along with limitations of study and
suggestions for further research.
2. Literature Review
2.1. Contribution of IT to Competitive Advantage
IT has become an integral part of modern organizations and it has changed many business
processes. However, researchers and practitioners have struggled to pinpoint its contribution to
business performance (Kohli & Grover 2008). The increasing attention in literature on IT and how
it can affect firms’ competitiveness is due to the big investments made in IT and the subsequent
need that comes with knowing how those investments can bring value to organizations. The value
of IT in research is underpinned by a Resource-Based View (RBV), which is applied to understand
the relationship between IT and organizational performance. The RBV provides a strategic
framework to assess the competitiveness of organizational resources and how they can lead to a CA
(Barney, 1991; Penrose, 1959; Wernerfelt, 1984). However, despite a significant amount of research
during the past two decades, the results on IT’s business value are rather mixed and there is an
ongoing debate about whether IT improves firm performance and in what way (Kohli & Grover,
2008; Wiengarten et al., 2013). Hence, the role, if any, that IT plays in supporting firm strategies,
remains unclear (Piccoli & Ives, 2005).
2.1.1. Resource Based view
The Resource Based View of the firm was developed firstly by Penrose (1959) who stated that the
uniqueness of a firm is established on the unique combination of its resources. Since then, several
scholars debated the role and the functions of resources within organizations. Wernerfelt (1984) had
yet started to link the use of resources to the performance of the firm. Indeed, he affirmed that
firm’s performance is determined by the resources it owns. Moreover, Penrose (1959), first, and
Tsoukas (1996), later, stressed that resources cannot be seen as something predefined, but, instead,
they provide different services to the company based on the way companies use them.
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Organizational resources are defined as “resources that include all assets, capabilities,
organizational processes, firm attributes, information, knowledge, etc. controlled by a firm that
enable the firm to conceive of and implement strategies that improve its efficiency and
effectiveness” (Barney, 1991, p.101).
Therefore, resources are factors that can either be tangible or intangible (Helfat & Peteraf, 2003),
with physical (tangible) ones being for instance a plant or equipment, and intangible ones being
customer relationship, know-how, or brand-name recognition. Those resources may allow a firm to
achieve a CA. Barney (1991) defines CA as “a process in which the firm is implementing a value
creating strategy not simultaneously being implemented by any current or potential competitors”
(p.102). This definition, given by Barney (1991), points out that in order to lead to CA the company
must own resources with four characteristics/attributes, (Barney, 1991): they have to be valuable,
rare, imperfectly imitable and non-strategically substitutable.
Valuable means that the resource must enable the company to develop a value-creating strategy,
improving its efficiency and effectiveness. In other words, the resource must be used in a way that
leads to a costs reduction or to the development of new products/services. Rare means that not all
the companies can own this resource; by definition, indeed, a firm is implementing a value-creating
strategy, and so a CA, if and only if this strategy is based on resources that competitors cannot
possess. Imperfectly imitable means that competitors cannot copy or imitate the resource; it might
be hard for the competitors to understand the link between the resource and the CA and
consequently, hard to act in order to imitate firm’s strategy. Lastly, not strategically substitutable
means that two different resources that can be exploited separately in order to implement the same
strategy do not exist (Barney, 1991).
RBV theory indicates that the resources that meet those criteria could lead to sustained CA. Then
the concept of complementarity of resources described by Milgrom and Roberts (1995) explained
how different resources can complement each other and enhance business value. Accordingly, it
was stated that the value of the organizational resources can increase in the presence of
complementary resources because it is more difficult for those to be copied by competitors
(Bharadwaj et al., 2007). Regarding IT, Clemons and Row (1991) used complementarity in order to
explain how technology can be considered as a source of value when leveraged with some other
resources. Moreover, Kettinger et al. (1994) overcame the issue of the paradoxical nature of
technology in its contribution to business value because they stated that the combination between IT
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and complementary resources like structure, culture, etc. make difficult for competitors to duplicate
it.
2.1.2. Information Technology resources and competitive advantage
As we saw above, the strategic potential of the resources in the RBV depends on the existence of
the four properties of value, rarity, inimitability and non-substitutability. Following the logic of
RBV, IT resources are defined as technological resources such as tangible assets (in the form of IT
hardware and software), or intangible assets such as technical and managerial IT knowledge
(Melville et al., 2004). IT tangible assets differ from intangible ones because of their physical nature
and from the fact that they can be bought from vendors. Their definition can be summarized as
following: IT tangible assets are widely available, off-the shelf or commodity-like information
technologies that are used to process, store, and disseminate information (Wade & Hulland, 2004).
By linking those IT resources with the strategic potential they can provide, IT assets, being seen as
tangible commodities that everyone can have access on, have never been seen as able to affect CA
because they do not embody the four properties (Mata et al., 1995; Wade & Hulland, 2004),
whereas intangible ones may possess these properties (e.g., IT management skills are firm specific
and cannot easily be traded or transferred; Mata et al., 1995). Subsequently, it has been observed,
that IT intangible resources—and not tangible ones— have been so far at the center of IT-based
RBV research (Wade & Nevo, 2010), and empirical evidence shows that many IT intangible
resources can provide organizational performance gains (e.g., Bharadwaj, 2000; Tanriverdi, 2006).
In specific, it has been argued that since IT has become commonplace, standardized, and available
to all competitors, its potential as a source of differentiation has diminished (Clemons & Row,
1991; Champy, 2003). This statement was also developed by Mata et al. (1995), who had stated that
technology assets such as networks and databases are unlikely to be leading to a CA, since they
could be easily procured in factor markets, so if a firm owns a resource that is possessed by
numerous other competing firms, that resource cannot be a source of CA (Mata et al., 1995).
However, combining hardware and software assets to create a flexible IT infrastructure can be
inimitable, because creating such an infrastructure requires carefully melding technology
components to fit firm needs and priorities (Ross et al., 1996). In addition to a sophisticated IT
infrastructure, skilled human resources, relationships between the IT department and user
departments, and managerial knowledge are valuable resources that are positioned to be of strategic
value (Mata et al., 1995). As Barney (1991) specified, many competitors can possess the same
physical technology but only one of them might own the social resources that are needed in order to
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exploit IT assets and implement a valuable strategy in the same way. For example, Barley (1986)
conducted a study in which he compared two different radiology departments that were using the
same scanners, discovering that, due to different “social” resources, the two departments not only
had a different level of efficiency, but also had two different organizational structures.
Therefore, although tangible IT assets usually do not directly induce CA, these technologies have
been shown to provide capabilities that may lead to enhanced performance of a firm (Kros et al.,
2011; Wade & Hulland, 2004). This is based on the argument that the effects of IT innovation
adoption occur at the functional/operational level via enhancing various aspects of efficiency and
effectiveness (Grant, 1991). As it is argued, the ability to leverage these assets can be indeed a
strategic differentiator (Bhatt & Grover, 2005). When those IT assets are combined with additional
organizational resources, the adoption of off-the-shelf technologies may provide the basis for a firm
to realize CA (Mata et al., 1995; Wade & Nevo, 2010; Hazen & Bird, 2012). Indeed, Wade and
Nevo (2010) developed a conceptual model that describes how IT assets can lead to the creation of
CA by employing the concepts of synergies and strategic potential. They also tried to find empirical
consequences of the model developed by testing it through survey data on IT-enabled customer
service departments (Wade & Nevo, 2011).
2.1.3. Conceptual model
Wade & Nevo (2010) argue that the research made so far, underestimates the fact that IT assets
cannot be considered in isolation, by saying that the fact that IT assets are widely available and that
they are regarded as commodity products does not tell the full story of their business value. Instead,
they argue that IT assets derive their business value from the impact they make on the
organizational resources with which they interact. They name those relations between IT assets and
organizational resources, as IT-enabled resources. The interactions between those two components,
(IT assets and organizational resources), develop emergent capabilities—that are, capabilities that
neither component can possess by itself (Wade & Nevo, 2010). This concept helps in understanding
that an IT-enabled resource cannot be considered only as the sum of its components and so, it
cannot be explained just with the aggregation of the capabilities of its constituent components.
Instead, it can only be explained in its totality (i.e., by considering the relationships among its
components).
In order to be beneficial Wade and Nevo (2010, p.168) believe that an emergent capability must
show “the potential to help an IT-enabled resource achieve organizational tasks or goals”. If this is
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the case we expect that the relation between the components will be synergistic. Thus, synergies are
defined as positive emergent capabilities, or as emergent capabilities that result in positive
outcomes. For instance increased efficiency and enablement of new processes are examples of
synergistic outcomes that can be associated with IT-enabled resources. However, even if they are
not able to define the concept of synergy more concretely, when they tested it, Wade and Nevo
(2011) used a quantitative approach instead of a qualitative one. This leads to a possible decrease in
the reliability of their results because it is not feasible to test abstract definitions with statistic
elements (Saunders, 2009).
Organizations have a tendency to anticipate in advance how IT-enabled resources will help the
organization achieve its goals. They therefore anticipate some potential synergistic relationships
between IT assets and other resources and they invest in an IT asset if it appears to functionally
complement an organizational resource (Wade & Nevo, 2010). In order for a potential synergy to
become realized, two enabler conditions must be present: compatibility and integration (see Figure
1).
Compatibility has been defined as “the ability of an organizational resource to apply an IT asset in
its regular activities and routines” (Wade & Nevo, 2010, p.170) and IT asset-organizational
resource integration effort as “activities taken by the organization’s management to support, guide,
and assist the implementation of the IT asset within the organizational resource” in a manner that is
congruent with the organization’s goals (Wade & Nevo, 2010, p.173). If those two enabling factors
of compatibility and integration apply, then we have an actual synergy.
After the concept of realized synergy, Wade and Nevo (2010) extended a causal chain from this
synergy to strategic potential and finally to CA, based on the RBV as explained above. The creation
of synergy is supposed to positively affect the strategic potential of IT-enabled resources that
acquire value, rarity and inimitability and in this way create CA (see Figure 1). The relation of
synergy with each of those four strategic properties was explained as following:
a) Value Property
It is suggested that synergistic IT-enabled resources are likely to be considered valuable, because
synergies may allow an organization to use an IT-enabled resource to capitalize strategic
opportunities or diverge from potential threats (Wade & Nevo, 2010). For example, if a customer
service department uses an IT asset -like for instance a client management system, to store and
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access information about clients and their desires- then it may be possible to custom-fit a new
product or service to a group of customers with a certain profile (Wade & Nevo, 2010). Also, if a
CRM department uses an IT asset that analyses data and reveals intentions of a customer to switch
to a competitor, it can foresee this threat and try to keep the customer within the company. By those
means, the IT-enabled resources which were created from the synergies between those departments
with the IT-assets are considered to bring value to the organization.
b) Rarity
It is also suggested that a synergistic relation between IT asset and organizational resources might
produce a rare IT-enabled resource. Indeed, even if the IT asset is not rare in the market and several
companies can buy it, this does not mean that all of those companies can develop certain IT-enabled
resources, which makes them rare to obtain (Wade & Nevo, 2010). For example, a study conducted
by Clemons and Row (1988), presents the case of two companies, McKesson and Bergen, that, even
if they were using the same IT assets, achieve different performances thank to the synergistic
relation that only one was able to develop. In particular, only the sales department of McKesson
developed a new, more lucrative, consulting service, an example that highlights the rarity of the ITenabled resources. Therefore synergistic relations can also be considered as rare.
c) Inimitability
Moreover, Wade and Nevo (2010) affirm that a synergistic relation between an IT asset and an
organizational resource might result in an inimitable IT-enabled resource. Indeed, even though an
IT asset can be widely available with a commodity-like nature (Mata et al., 2005), it might develop
complex systems with other organizational resources that will be hard to imitate for competitors. In
fact, competitors may possess both IT-asset and the organizational resource, but, due to complexity,
they might not be able to understand the nature of the relation between them and thus, to develop
the same relation in order to achieve the same performance.
d) Non-substitutability
An organizational resource is considered substitutable if other resources can be used to implement
the same strategy (Barney, 1991). Wade and Nevo (2010) did not find a link between synergy and
non-substitutability. Different organizational resources or combinations of resources could
conceivably be used to achieve the same organizational outcomes as the IT-enabled resource;
therefore this fourth attribute of non-substitutability does not apply in the case of synergistic
relations between IT and other resources.
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Those attributes of value, rarity and inimitability are important for the creation of CA for companies
because they confer strategic potential to firms’ organizational resources. Therefore, those
companies who own those kinds of strategic resources can implement strategies that are unavailable
to their competitors. Further, the CA is sustained when the strategies cannot be duplicated by the
firms’ current and future competitors (Barney, 1991). Strategies are based on collections of
organizational resources and, as such, their ability to confer a CA and sustain it depends upon the
strategic potential of those resources. Therefore, the ability of an IT-enabled resource to generate
sustained competitive advantage and affect performance is contingent upon the above properties.
Conceptual Model on the creation of Competitive Advantage through the development of synergies
Figure 1 - Source: Wade and Nevo (2010), p.171
2.2. Big Data Analytics and Competitive Advantage
By following the model mentioned above of Wade and Nevo (2010) of how IT assets may
contribute to firms’ CA as our guidance, we link the concept of IT assets to BDA.
2.2.1. Big Data Analytics as an IT asset
BDA require new tools for analysis and we have witnessed a sharp rise in dedicated appliances for
high performance analytics: databases for fast query results and open-source tools for high-speed
analytics against various types of BDA sources. Those are information assets, which companies buy
from vendors, like databases and software. They are, in other words, off-the shelf or commodity11
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like information technologies that are used to process, store, and disseminate information (Wade &
Hulland, 2004) and therefore meet the definition of an IT asset adopted on the present paper. In
addition, as stated by Wade and Nevo (2010), even some IT assets that are customizable, and, as
such, may not be regarded as completely undifferentiated, they are still considered commodities
since they are not protected by isolating mechanisms. Therefore, even if companies can sometimes
acquire BDA assets and customize them, they can still be considered as IT assets.
2.2.2. Big Data as a Driver for Competitive Advantage
By following the model presented by Wade and Nevo (2010) as guidance and by considering BDA
as an IT asset we are going to investigate how BDA are perceived by an organization’s management
as a potential source of CA. To be specific, our investigation will focus on the synergies that might
arise between BDA and other organizational resources. Concerning what other organizational
resources can relate with the BDA, those can be for example the IT department and other
departments, such as Marketing Department, Customer Relationship Management Department
(CRM), Sales Department, Logistics Department etc. Other resources can be the companies’ human
capital, people’s skills and knowledge, IT-Business Partnership etc. We will use concepts derived
from the adopted model; we will investigate whether the two enable conditions of compatibility and
integration exist in the system created by the relation between BDA and other organizational
resources and whether synergies are expected to be realized. Those synergies, being the positive
relationship between BDA and other resources, can enable their strategic potential.
3. Method
3.1. Research type
The present study will test if the model proposed by Wade and Nevo (2010) applies in the industry
of food retailers in the case of BDA. Wade and Nevo already published in the 2011 a research
where they tried to test their model empirically. However, they did that in a different industry,
customer services, so we believe that there is still room for a better understanding of their model in
such a different environment. The strategy employed is the one of two case studies because we want
to get a rich understanding of the relative processes enacted and a case study could potentially
provide us with the answer of how something happens, and in that case, how BDA are perceived to
contribute to firms’ CA (Saunders et al., 2009, p.140). We select two cases and investigate them in
depth, in order to find answers to our research question. The research involves two cases to be
studied, because application of BDA is a relatively new phenomenon, which might also have a
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subjective and unclear definition: in a study conducted by The Data Warehousing Institute (TDWI)
in 2011 most participants were familiar with something resembling BDA, but only 18% used the
term “Big Data Analytics” for this (Russom, 2011). This problem along with the novelty of the
whole phenomenon itself has made the application of BDA less easy to identify.
3.2. Selection of cases
The case studies we chose refer to the big food retailers in Sweden and Greece, ICA AB
(Euromonitor International Passport, 2012) in Sweden and Masoutis S.A in Greece respectively. By
big we refer to their market share in the industry. The option of those countries was made of two
reasons: first, they are known to be countries which are on the front of innovation and adopt quickly
new technologies (Florida et al., 2011). Second, it was easier for us to conduct the research in those
countries due to geographical proximity reasons. The reason why cases from the retail industry
were chosen to be studied is because retail industry is one of the industries that in research has been
emphasized as engaging the most with investing to BDA and it is also one area that witnesses the
most beneficial impacts of BDA (Brown et al., 2011). There is an emphasis on qualitative study and
analysis because it is considered helpful to explain a phenomenon and the way it is perceived to
affect an organization. Thus, ICA AB and Masoutis S.A fit the profile of study, they were selected
due to their relevance to the theoretical issue being researched and were used as an example for
theory (Denscombe, 2003), as both have acknowledged to be using BDA systems.
3.3. Data Collection and Analysis
Data were collected through personal interviews with responsible managers at ICA Headquarters in
Stockholm and Masoutis Headquarters in Thessaloniki, especially with interviewees coming from
IT and Marketing Departments. Moreover, additional data contributing to our analysis were five
phone interviews, annual reports, retail industry reports, specialized journals and newspapers.
In specific:
Concerning ICA, two personal interviews were conducted with responsible managers, one from
BI/IT department and one from the CRM department (part of Marketing department). The first
interview was conducted with Olof Granberg, who is software architecture manager in Business
Intelligence and IT Services department and his responsibility is to find technical solutions that
match business needs. The second interview was conducted with David Holmstrand, chief CRM
manager, who is in charge of several of the areas where customer data is refined to valuable
knowledge and, in turn, to actions. Among his responsibilities are personal offers (1-to-1) and
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providing customer insights internally and externally (e.g. with suppliers). In addition, four phone
interviews with managers of ICA stores in Uppsala were conducted (ICA Supermarket Alvikstorg,
ICA Supermarket Samköpt, ICA Supermarket Torgkassen and ICA Nära Folkes Livs), in order to
get their perspective on the topic and see if they have access to BDA practices.
Concerning Masoutis, one interview was conducted with the Marketing Director of the company,
Apostolos Filaktos, who is responsible to supervise all marketing activities of the company,
including Data Analysis for marketing purposes and a phone interview was conducted with
Katerina Papadopoulou, employee within the Data Analysis department.
The choice of these departments was based on their involvement in the BDA process. Since we
wanted to investigate the perception of the companies relating to BDA, we wanted to retrieve two
aspects that can lead to a better perception: an IT perspective and a business perspective. A
perception of a retail company on BDA is efficiently represented by the perception of the IT
department and the CRM/ Marketing department for the following reasons: the IT department is the
one mostly responsible on BDA and it also gets to work with different other departments of the
company, and the CRM/Marketing department is the department which employs the most the
results coming from BDA. Furthermore, since the use of BDA is still on an embryonic level, it is
not deeply rooted in the organizational processes, only a few people are involved with BDA and
therefore the role of management at this stage is crucial. The people interviewed could provide us
with an understanding of how their departments work with BDA as a whole, because of their
position regarding BDA projects. For those reasons we chose to conduct the aforementioned
interviews.
The interviews were organized in a semi-structured and informal way, in order to gain more
flexibility during the discussion and point out areas of specific interest more extensively (Saunders
et al., 2009). The flexibility was needed to explore the complexity and dynamics of the topic.
Moreover, since the study takes on a relatively new topic, semi-structured interviews are
advantageous because they allow the interviewees to explain their response and lead the discussion
into areas not thought of before (Saunders et al., 2009). The interviews lasted approximately one
and a half hour each and they were conducted in English with ICA, because the authors are not
fluent in Swedish, and in Greek with Masoutis, because one author is a native Greek speaker. There
were no barriers of communication since all the ICA interviewees were fluent in English and the
Masoutis ones were Greek native Speakers.
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The interviews were mainly comprised by two parts: a first part which aimed to a general
orientation, and a second one with a more intense focus on BDA as an organizational asset and how
it is perceived to affect the organization’s competitiveness. The semi-structured questions of both
parts were the same in all interviews (see Appendix I), they were used as guidelines, but there were
further explanatory questions in each case, to avoid misinterpretations. The first part included
mainly general questions about what BDA are, how they employ the result of their analysis and
why they started using them. It was also asked which tools are used for BDA, which data they select
and why, and whether they are satisfied with their BDA operations so far (see Appendix I). In
addition, the description of a specific BDA project was requested, in order to gain a deeper
understanding of how they work with them. The second part was more specifically focused on
testing the model as described in section 2. Indeed, we asked questions related to the actual
management and analysis of Big Data and if and how they perceive them to affect firm’s
performance. We asked how BDA are implemented and what the management’s involvement is, in
order to understand if the enabler conditions (compatibility and integration) were effectively
present. It was also asked whether BDA can be seen as an independent source for CA, which the
relationships between BDA as an IT asset and other organizational resources are and how BDA are
perceived to make the firm more competitive (Table I and Appendix I).
The following table (Table 1) presents the questions asked, their purpose, and for the ones
stemming from the model it shows which constructs we intended to capture:
QUESTION
PURPOSE
Part 1 - Orientation interview
Can you tell us in a few words what position you own within Understanding of the position
your organization and what you are responsible for?
and responsibilities of the
interviewed
How would you define Big Data?
Understanding if the definition
we used was coincident with
the one used within the
company
Is your company analyzing Data referred as Big Data already in a Understanding at which level
functional way?
of development the company
was in the use of BDA
a. What systems are you using in order to analyze Big Data? Understanding if the company
b. What are the sources where you gather Data from? Do you use was using structured or also
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Social
Media
as
c. Which data do you select and what for?
Alessandro Galletti & Dimitra Papadimitriou
a
source? unstructured data
a. In which areas do you employ the results from the Analysis of
Big
Data?
b. Do you have any specific questions behind that you are
looking
to
answer?
c. Before adopting Big Data Analytics, did you predict where it
could contribute best?
a. Can you describe us any ongoing Big Data Projects?
b. Who was the one that decided the implementation of Big Data
projects?
c. Are the people working in those projects coming from the IT
department or elsewhere?
What is the main reason why the company started employing Big
Data Analytics? What did they expect to find?
Are you satisfied so far with the results you get of Big Data
Analytics? Do you face any problems in their implementation?
Part 2 - Core interview
Do you know if your competitors are also engaging in the
analysis of Big Data?
Understanding the general idea
that is behind BDA projects in
the company
Understanding in depth the
characteristics of a BDA
project
Understanding of the strategy
behind BDA
General idea about results and
potential problems
Understanding the company
awareness of the competitive
field
Can people from your department employ Big Data Capturing the presence of the
systematically, in their regular activities and routines? Can you first
enabler
condition:
have daily interactions with Big Data Analytics if needed? compatibility
Can you give us an example of how these interactions happen
practically?
a. Were there any activities taken by the organization’s Capturing the presence of the
management to support, guide, or assist the implementation of second
enabler
condition:
Big Data within ICA? What kind of activities? integration effort
b. Do you think that uses of Big Data and other organizational
resources share the same goals? Are they the same with the goals
of the company?
a. Is the adoption of a Big Data Analytics system by itself capable Capturing the presence of
of producing a competitive advantage that will last over time?
synergy
(By this term we mean just the Big Data software and tools as
bought from the IT vendors)
b. (if not), what other organizational resources are needed to be
combined with Big Data Analytics in order to give this advantage
against competitors?
Does Big Data Analytics together with its interactions with your Capturing the presence of the
department provide value to your company? Is there a situation in first factor of the strategic
which Big Data allow you to capitalize strategic opportunities?
potential: value
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Do you consider your department able to develop new
products/services that are not present in the competitors’ offer
thanks to the collaboration with BI department and the use of Big
Data?
Can you give us an example?
Do you think that those innovative products/service you
developed can be imitated by your competitors in an easy and
quick way or not? Could you give us an example?
a. Do you see Big Data Analytics as a way to enhance the firm’s
competitiveness?
b. Do you see Big Data Analytics as a way for the company to
gain an advantage against its competitors?
Would performance of your department be affected, had we
removed the use of Big Data Analytics? If yes, how?
How do you picture your company using Big Data in 5 years?
Capturing the presence of the
second factor of the strategic
potential: rarity
Capturing the presence of the
third factor of the strategic
potential: inimitability
Capturing if the company
experienced the last step of the
model: creating CA
Understanding the importance
of BDA within the company
Vision of the future
Table 1 – Questions and purpose
Notes were taken during the interviews and there were two complementary interviews with the ICA
managers for some further questions which arose after the conclusion of the first one. Then, a report
was drafted containing the information gathered from the interviews and all the interviewees were
asked to review it and provide further feedback if necessary. This was done in order to reduce the
risk of subjective interpretation of the interview data. The feedback provided confirmed the content
of the reports. Regarding the data gathered through reports, journals and newspaper, a scanning of
all the Year-End Reports and all the Annual Reports published by the two companies from 2008
was made in order to find relevant information regarding their BDA projects. The articles found in
Swedish were translated both by a Swedish native speaker colleague of ours and from an online
translator. Accordingly, the translation of reports, questions and answers between English and
Greek were also conducted from the native Greek speaker co-author and the use of an online
translator.
An analysis of the data gathered from the interviews was made on their content, by getting first a
deep understanding of the data available and assessing their quality. The data were organized into
two coherent categories after they were collected, in accordance with the sequence of the questions
asked as presented above: the first category concerns the implementation of BDA and it gives
information about BDA projects, the human capital involved, the top management’s involvement in
BDA, the access of resources in the use of BDA and perceived hurdles arisen in their
implementation; the second category contains data of how BDA is perceived as a driver for CA. The
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data were systematically analyzed and carefully re-read, then condensed and matched with the
concepts developed in the literature review. The focus of the analysis was to respond to the research
question and the data were organized by question to look across both respondents, in order to
identify consistencies and differences. The data categorization brought meaning to the text and gave
a three-fold structure to the analysis, which comes in accordance with the three main stages of the
model, as described in the theory section (see again Figure I); those three categories of analysis are:
1. Creation of potential synergy and enablers for actual synergy, 2. IT enabled resources and
strategic potential, 3. IT enabled resources and competitive advantage. Hence, the method of
analysis chosen for this thesis draws upon the interpretation and synthesis of findings that emerged
from the investigations of the individual interviews.
3.4. Reliability and Validity of research
As it is normally the case with semi-structured or in-depth interviews as a strategy for collecting
data, the validity of this study can be influenced by several factors. The most important include the
willingness from interviewees to cooperate, provision of limited information due to reasons of
secrecy and the application of some relative concepts, such as “ to enhance competitiveness or
performance”, or the notion of competitive advantage. The last factor that can limit the research
validity will be tried to be eliminated by defining all concepts within the context and for the
purposes of the research, and making sure that they coincide with the ones perceived and used by
the interviewees during the selection of data. Another limitation is the existence of bias, since the
research is concentrated on how the interviewees perceive a given situation and that can differ
within different people, different organizations or different industries. In order to improve the
validity of the data gathered through the interviews a triangulation with information collected from
corporate reports, industry reports, specialized journals and newspapers was made. A triangulation,
as stated by Saunders et al. (2009), “is the use of two or more independent sources of data or data
collection methods to corroborate research findings within a study” (Saunders et al., 2009, p.154). It
is a methodology highly recommended when the type of research is a case study conducted through
semi-structured interviews (Saunders et al., 2009). The present study is concentrated in the specific
industry of retail in the Swedish and Greek markets, and it is also based on the way a phenomenon
it is perceived to affect a specific organization. Therefore we reserve our doubts on whether the
results can be generalized under different contexts and different industries.
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4. Case studies
This section includes the data collected from the case studies ICA AB and Masoutis S.A. The data
are presented as mentioned above into two broad categories, which are in accordance with the
questions and the order those were posed (Table I). Those are: BDA implementation within the
firms, in order to understand the way they use BDA and BDA as a driver for CA, in order to
retrieve their perception of the correlation between BDA and firms’ competitiveness.
4.1. ICA Case Study
ICA AB is one of Northern Europe’s leading retail companies, with nearly 2,150 stores in Sweden,
Norway, Estonia, Latvia and Lithuania. Since 1938, ICA AB has been supplying a wide range of
fresh foods to locations throughout Europe. ICA Sverige AB is the Swedish subsidiary of ICA AB
and it has a strong and comprehensive national presence in grocery retail. It had 1,375 outlets in
operation in Sweden at the end of 2010 (Euromonitor International Passport, 2012) and it is the
leader in grocery retailers with retail value share of 36% (Euromonitor International Passport,
2012). ICA Sverige AB operates around the country in cooperation with independent retailers. The
retailers own and manage their own stores, but have agreements with ICA Sverige AB in a number
of important areas.
As mentioned above, the first interview was conducted with Olof Granberg from the IT department,
and the second one was conducted with David Holmstrand, chief CRM manager. The phone
interviews were conducted with the managers of the aforementioned ICA stores in Uppsala, who all
preferred to stay anonymous.
4.1.1. ICA case is a Big Data Analytics case
ICA Sverige AB (from now on referred as “ICA”) constitutes a BDA case and the firm
acknowledged the Analytics systems they use as BDA systems. Big Data were defined the same
way as on the present study from the IT/BI department of ICA, characterized by the three Vs
(Volume, Velocity and Variety). Concerning how those three characteristics apply in the case of
ICA, the following was stated:
Volume is related to the amount of information (transactions) that companies deal with on a daily
basis; ICA defines one transaction as one line on one receipt, for instance: two bottles of milk for
one customer in a specific purchase is one receipt line. However, it’s a hard concept to define
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precisely because it depends on the kind of business we are considering; for some businesses, for
example, 10 million transactions per day are a huge amount of data, for others, instead, like the
bank industry, are not. ICA is claiming to use 50-100 millions of transactions for analysis on a daily
basis (Granberg, 2013).
Velocity regards both how fast data moves and how fast customers’ behaviors change; “if ICA is
able to be there when those behaviors change we might have a huge advantage. Being on the top of
that wave has a big impact on firm’s performance”, Olof Granberg reveals.
Variety regards all the products customers buy and all the ways they do it. That means that they
must be able to present personal offers as precisely as possible. Regarding the variety of the sources
of the data they gather, it was stressed that ICA does not analyze unstructured data, especially the
part that comes from social media. Their focus is on the “point of sale data”, which means the
receipt line, so info stemming from the receipts of the customers purchases. Indeed, the majority of
their data comes from the ICA Card that is both a loyalty and a bank card, from which they can get
a lot of information regarding customers’ behaviors and purchase habits. ICA Card has been
claimed to have an excellent percentage of penetration between ICA customers, which is more than
60% (Granberg, 2013).
ICA has been using BDA for two years now. Although they have recognized already a lot of
benefits that BDA can provide ICA with, it was stressed from all the interviewees that they are still
at a starting point. They are still in the beginning of their use and the company is on its way to have
a deeper understanding of that phenomenon.
4.1.2. Big Data Analytics’ implementation within ICA
As mentioned above, ICA is using BDA with a main focus on structured data coming from the
receipts of the customers’ purchases. Concerning the IT systems they use for BDA, they buy the
databases and the tools needed but they also design their own data models in order to reach a higher
level of flexibility regarding their needs. The main sources are the sales information, such as “line
receipt”, and the ICA Card (Granberg, 2013). This provides several kinds of information, such as
demographic data: household, age, etc. They analyze those data in order to personalize the behavior
patterns and understand each customer’s needs individually. The main areas where the results from
BDA are implemented are: Customer Relation Management (CRM) department in a high degree, in
order to personalize product offers to individual needs and retain customers’ loyalty and Supply
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Chain and Logistics departments in a lesser degree, in order to optimize their operations (Granberg,
2013).
Relating to the main reason why ICA started employing BDA and what they expected to find, it was
stated that there was not a specific reason why they started implementing BDA projects; for them, it
was more like a natural evolution coming from data analytics. BDA is seen as a process, in which
they are just making small steps, thus it is impossible to define a specific moment in which they
started calling them BDA instead of data analytics. Indeed, “data have always been there, they only
grew over time” (Granberg, 2013). However, it was stated that ICA had predicted before investing
in BDA which departments would gain the most from their use; Sales and CRM/Marketing
departments were the ones predicted to gain the most advantage from the implementation of BDA
(Granberg, 2013). This was due to the relationship between the use of customer data with the
business need of personalizing offers and, in that way, enhance marketing.
Regarding the projects implementation, there are two main ways in which ICA is approaching the
analysis of Big Data: one is initiated when a specific department has a specific question that needs
to be answered. In this case the Business Intelligence and IT department spends some time, more or
less one week, of analyzing data and trying to answer this question. The second one is a long-term
project (Granberg, 2013). ICA is developing several “start-up projects” and business cases between
IT/BI and other departments, such as CRM/Marketing, Supply Chain, Logistics and Sales
department. He continued stating that the most effective way is to work with one department at a
time, because in that way they can better specify the needs of each individual case and therefore
select the data that need to be analyzed in order to serve those needs (Granberg, 2013).
An example of a long term ongoing project with the objective of increasing sales, was to study how
customers move inside the stores (“customer lap”) in order to understand if they walk in front of
each shelve or not; the BDA contribution was that ICA got data from the receipt line about which
products each customer bought, data which were used in order to try to understand their “lap”.
Identifying the way customers move inside the stores is also achieved with offering them the
opportunity to scan each product by themselves. In this way it is more feasible to follow their
moves inside the store. Indeed, consumers seem to be increasingly using self-service scanning and
payment systems to conduct their grocery shopping but are also additionally indicating a greater
willingness to use the internet to plan, order and purchase their grocery shopping (Euromonitor
International Passport, 2012), and thus respond positively to those kind of practices initiated.
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Moreover, another important project in which BDA are applied was explained by David
Holmstrand to be the Mina Varor (“My groceries”) one, the first one-to-one marketing project. The
idea of Mina Varor came up after many customers told the company that the offers they receive in
the mail do not fit their needs (ICA Annual Report, 2008). Although the analytics involved are
fairly simple, the sheer volume of information is rather impressive, at least compared to other
marketing operations. Through Mina Varor, ICA presents personalized offers to its customers in
order to improve their satisfaction towards the brand and thus increase their loyalty. To be specific,
it was mentioned that “eleven times a year approximately two million customers receive a unique
combination of up to eight offers, selected from a bank of more than 1,400 offers involving nearly
10,000 items. According to wolframalpha.com, there are 358 758 599 801 819 985 075
combinations of eight offers out of 1,400 possible” (Holmstrand, 2013). This agrees with what had
been also mentioned by Fredrik Persson, the former manager of CRM department of ICA, on an
interview for an online Swedish newspaper; he stated that the service of “Mina Varor” faced an
evolution during the years, thanks to new CRM databases: ICA is now offering discounts not only
on products that customers are used to buy, but also on new products that might be potentially
interesting for them. Moreover, through “Mina Varor”, ICA is sending a newsletter regarding the
new products that are present in the stores and he argued that the sales of new products in the
supermarkets increased (Ollén, 2011). Persson continued stating that this service is also helping the
suppliers because they can use these data in order to decide which new product they should launch
following consumers consumptions habits (Ollén, 2011).
Another solution was to study the “product pairing”: it means checking which products customers
bought in pair (for example hamburger and hamburger bread) and find a way to encourage people to
do this kind of purchases and improve also the placement of the products on the shelves (Granberg,
2013). Fredrik Persson, former CRM manager of ICA, had commented on product pairing, that he
believed the next step will be to “remind” customers products that they might have forgotten; for
example, “product pairing” and information such “others who bought this product, also bought that
product” included in the personalized offers will go in this direction (Ollén, 2011).
BDA are also used to analyse the opportunity to open a new store in a specific location; in this case
they use open source data from Statistics Sweden in order to understand if in that area there are
people
living
with
enough
funds,
that
could
be
potential
customers
(http://www.scb.se/default____2154.aspx). They cross this information in combination with the
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presence of the competitors’ stores in that place, in order to identify the best locations to build their
stores.
Regarding to the human resources that are used during the implementation of those projects in
general, it was affirmed by all interviewees that projects are run by a group comprised by IT
employees with the collaboration of some people from other departments, depending on the
ongoing project. Projects usually involve a project owner from a department on the business side,
resources from other relevant/affected departments, and IT competence. When it comes to handling
BDA, especially customer data, ICA's CRM department is usually involved both on the business
and IT side, implying that a few individuals with specific competencies each participate in several
such projects. Not everyone is involved in every project but there are certain key resources that
have their say in most development activities they have. There are always specialists’ competences
involved in all parts of development, whenever it is needed, e.g. controllers looking at the financial
processes, logisticians handling replenishment matters etc.
In order to understand the way by which various ICA departments have access to BDA, it was
asked whether those departments can systematically employ BDA in their regular activities and
routines. It was confirmed from Olof Granberg, that there are daily interactions between IT and
other departments that are involved in projects with BDA. As it was stated, other departments have
free access to databases if they need to find information already stored. Moreover, he claimed that
knowledge is easily shared within the company and therefore easily accessible for all departments.
As he specified, BDA are not just a project but also an ongoing process. However, David
Holmstrand handled the question a bit differently. Although he affirmed as well that people in CRM
can have regular access to those analytical technological tools, he mentioned one basic factor that
could limit Big Data’s systematic employment. This limitation relates to the effective understanding
of BDA from the majority of the employees. Indeed, he stated that there are a limited number of
employees (specialists) who could, and are, employing BDA in their daily routines and it’s not
easily accessible for the great mass of employees. For this reason, whenever someone needs
something relating to BDA that request needs to be handled by the few individuals who have this
accessibility, often IT specialists.
The aforementioned access to BDA regards only the employees of the different departments within
ICA. In relation to the individual ICA store owners, those are not using BDA in their everyday
practices. From our phone interviews with the store owners, it was confirmed by all that they do not
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consider themselves as users of analytics, because they cannot do anything on their own: their only
access is in a database (ICA Web) in which they can see groups of data concerning their customers.
They can therefore track information about their customers and find for example about their age or
shopping habits. Then they can send a request through the database to the head offices to send for
example some personalized discounts, birthday cards or letters. For example they can ask to
promote a health product with a personalized discount to customers whose data reveal that they tend
to buy health products, or to their best customers. It is the head offices that send those offers to the
relevant customers, addressed as they come from the specific stores. The store owners cannot ask a
specific question through this database in order to be answered by the IT after the analysis of data.
Furthermore, it is only the store-owners or managers themselves that have access to this database
inside the stores, and they claim to be using it on a regular basis.
Regarding the goals that are to be served amongst different departments and BDA, it was stated
from Olof Granberg from IT, who gets to work with different departments, that different
departments usually have different goals. However, during the process of a common project the
objective becomes the same and the departments work together to perform as good as possible. One
specific example of how different goals of different departments are aligned by BDA projects is the
following: Logistics department wanted to achieve a 0% amount of waste of products from the
shelves of ICA shops (i.e. no products on the shelves after the expiring date); however, BDA
showed that 0% could not be a goal to achieve, because, for the sales department, 0% waste means
that ICA is not selling enough. This is because, in order to avoid waste, the amount of products on
the shelves wouldn’t be enough to satisfy customers’ demand. Thus, the project revealed that an
ideal percentage of waste, high enough to satisfy consumers’ needs, is 7% (Granberg, 2013).
The departments’ goals in the use of BDA are claimed by both the interviewees to be in accordance
with the goals of the organization as a whole; ICA wants to remain leader in grocery retail industry,
improve customers’ loyalty, and continue to excel in responsible business (ICA AB, Year-end
report, 2012). In particular, in 2013, ICA will continue to develop a future digital communications
and sales solution. The idea is to consolidate ICA’s offering digitally and lay the foundation for
online sales of foods, non-foods and other products. ICA will also analyze in various ways how it
can offer customers a better selection based on the data it has access to and their priority is to be a
leader in digital sales (ICA AB, Annual Report, 2012, p. 34).
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When discussed about the role of the company’s management in the implementation of BDA, it was
claimed in both interviews, that top management has put in place efforts to align the departments’
different objectives with the enterprise wide strategy. Indeed, it was commonly stated that the
direction to follow is clear and the progress will speed up in the years ahead. Management is
claimed to ensure that BDA and other organizational resources are properly combined and in line
with the organization’s goals and there were claimed to be efforts of encouraging employees for the
implementation of BDA, for example by inviting them to use Analytics for many decisions they
needed to make. The first step of this process is an understanding on the topic itself and it is
believed that ICA’s management has done that, as it is shown in set strategies by initiating those
BDA projects (Holmstrand, 2013). Hence, only small steps have been taken so far and it is not
believed that executing on these strategies has been carried out to a great extent yet. David
Holmstrand stressed out further the importance of management’s involvement, by saying that “it is
really important that the employees have the sufficient mandate to take actions based on the
analytics, no matter if it concerns building a new warehouse or making sure that customers enjoy a
satisfactory shopping experience”. This is where the management support is crucial and where there
is also room for improvement; as it was suggested, although there has been management’s efforts in
encouraging the use of BDA, managers should need to understand better all the advantages of BDA
in order to actively facilitate employees to use them (Holmstrand, 2013; Granberg, 2013).
Concerning the organizational structure and if it has changed in order to help to the best exploitation
of BDA, it was stated from Olof Granberg that there is one modification of structure which is still in
progress: in specific, the IT/Business Intelligence Department has always been at the base of the
organization in the implementation of BDA projects, while interacting with other departments.
Interactions with those other departments have been realized by a few people of each department
who have daily relations with the BI department. However, now they are already working on a
different structure in which BI is still the base, but the interactions and communications between the
different departments are so strong, that Big Data analysts are directly hired by the other
departments because they will totally work for them. Thus, more business analysts who combine
both business knowledge and data analytics knowledge are hired from each department in order to
help them use analytics and serve their needs, by decentralizing gradually the structure from the
main focus on the IT/BI department.
Concerning whether they are satisfied so far with the results they get from BDA, it was mentioned
in all interviews that they are satisfied so far with their implementation in terms of having enhanced
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customers’ loyalty and customers’ satisfaction. However, it was mentioned from Olof Granberg in
IT department, that there are two main problems that they need to solve: the first is that they do not
really know what they can do with all this data. What exactly they need to know is not
straightforward yet. This is a problem that will be aggravated if they start gathering unstructured
data coming from social media, because it will be even more difficult to define a specific
objective/purpose of analysis. The second problem is related with handling volumes, which is more
difficult than most vendors say; they can rarely use the information they get towards a specific
objective and the information they gather is not “ad hoc” information. It means that it is
complicated to select the precise information they need from the huge amount of data they gather.
Indeed, it was stated that they are facing a trade off with the software they use nowadays; they
cannot match full flexibility with performance in terms of volume because the software available
nowadays are not developed for their specific objectives, but only to gather the maximum amount
of data as possible.
Moreover, from a CRM perspective, it was stated that the development generally takes too much
time and other resources (like exploitation of human resources, time and funds) which are
considered luxuries in a market where continuous improvements are essential. However, since it is
seen as a natural process, it was a common statement that they are essentially learning by doing it,
that they are still in an experimentation process and that there is a bigger potential yet to be
discovered (Holmstrand, 2013).
4.1.3. Big Data Analytics as a driver for competitive advantage in ICA
BDA were claimed to have positively affected ICA’s performance by both interviewees but not as
much as it could do, due to the big potential that can be further explored in the future. All
interviewees claimed that BDA have led to an enhancement of the firm’s competitiveness; indeed,
Mina Varor is an important example of how ICA was able to deliver an innovative service to its
customers, and the contribution of the volume of data through BDA is impressive, as David
Holmstrand specified. Moreover, he continued, “even if the process is still at an embryonic stage,
ICA can radically change the way its entire offer is shaped based on BDA in the future, for example
by improving the use of them in category management”. Category management is a strategic
approach in which products are divided in categories. Each category is run as a single business unit,
with its own targets and strategies. To be specific, it requires a continuous exchange of information
and data about each single product category in order to develop a more effective delivery of the
products, and this is where the role of BDA is crucial, in providing those data.
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BDA are also positively seen as a way to gain advantage against competitors for ICA. In fact, both
interviewees in ICA stressed that they see BDA as something that can “absolutely lead to an
advantage against competition”. Concerning how BDA are therefore perceived as a way to lead to a
CA, IT systems by themselves are not seen as they are much of help (Holmstrand, 2013; Granberg,
2013). As stated, they always need to be combined with other resources such as, collaborations with
other departments, human interactions and business knowledge in order to be able to produce a CA.
It was a common statement that ICA needs a strategy that incorporates IT and business knowledge
in order to enable the development of a CA. As it was specifically mentioned by all the
interviewees, Big Data technologies, are not possible to isolate, it is not something that can stand
alone; it needs people with knowledge, competence and mandate, along with a strong, sustainable
management support (Holmstrand, 2013). ICA’s employees must understand what the data/insights
mean and be competent enough to use it in everyday situations. If not, BDA is claimed to be just a
waste of resources (Holmstrand, 2013; Granberg, 2013).
Another factor was also pointed out by David Holmstrand, which, as he mentioned, should not be
neglected: the human and emotional aspect of shopping experiences. By human aspect he meant
human interactions that take place in the stores on a daily basis, i.e. with store employees. It is
believed that the business of food groceries will rely on such interactions for quite some time
because many customers have needs that data analytics by themselves cannot provide, at least not
today (or without major investments). This could be the need of having specific questions being
answered about a recipe, including the employee's own experience and tips, but it could also be just
someone who listens to them. BDA cannot substitute those human interactions within the stores on
the one hand, but what they do, is that they improve those interactions by collecting data on what
customers like to face in ICA’s employees and therefore lead to a CA.
The perception of the interviewees that BDA and IT can absolutely lead to a CA in the industry, is
supported also from other competitors in the Swedish retail industry; Bergendahl & son and in
particular their major brand, City Gross, had affirmed on a Swedish newspaper, regarding the
potential of BDA as a source to CA, that they believe that the key to competition in Swedish Retail
Industry is a smart implementation of IT (Ryberg, 2013). Indeed City Gross, the fourth biggest food
retailer in Sweden (Ryberg, 2013), stated that they are developing a new IT strategy that will might
allow them to be more competitive; customers who scan the products directly in the stores will have
the opportunity to receive real-time offers on the scan screen, connected to the product just scanned.
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This is due to a personal number that they have to insert in the scan which will link the specific
customer, and thus all his previous purchases, to the CRM database (Ryberg, 2013).
When asked whether the interviewees know if their competitors are employing BDA, Olof
Granberg answered that to his knowledge, ICA is the only one engaging in their analysis. David
Holmstrand named two competitors (Coop and Axfood), that he thinks may be handling BDA but
he mentioned that it is anyway to a much lesser degree than they do. Relating to the issue of
whether competitors might be able to imitate the initiatives that ICA has put in place so far in the
application of BDA, it was stated that if the management is strongly committed, it would be
possible but hard for the other retailers, to develop similar services; while the Big Data analytical
tools are widely available for other competitors in the market as well, it was believed that the soft
skills and the combination of them with technological assets can be, in the long term, hardly
imitable. Indeed, people, their knowledge and their commitment, are still considered the core
resources of a company like ICA and this aspect, combined with BDA, is considered a great
advantage against its competitors. This coincides precisely with what the former CEO of ICA,
Kenneth Bengtsson had revealed, referring to their personalized offerings through “Mina Varor”:
“we won’t be alone for long in communicating with and serving customers in a more personal,
targeted way. Our strategies and techniques can always be copied, but our commitment will be
more difficult. It is why our culture gives us a competitive advantage.” (ICA AB, Annual Report,
2008)
As it was highlighted by the interviews, even though the understanding of what BDA can provide
with has grown immensely over the past years, it’s still something new in a business that basically
operates in the same way as always (e.g. there is a very small share of marketing that is 1-to-1 and
most business is executed in physical stores and not online, as David Holmstrand claims). However,
both the interviewees confirmed that a potential interruption of the use of BDA would have a
negative impact on those departments and on the whole company. ICA, for example, wouldn’t
know anymore what its customers were buying, and therefore would not be able to understand
customers’ needs and behaviors, something which would have negative consequences on firm’s
performance.
Concerning the future, in conclusion, it is believed that in 5 years ICA will be able to refine the
“point of sale data”, to develop a better “ad hoc” analysis and to have a good platform for the
analysis of structured data that will allow the company to work with data more quickly and obtain
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relevant information even the last moment. Their goal is to be able to predict customers’ behaviors
and purchase habits in order to pre-calculate their questions, buying the right hardware that allows
them to overcome the existing trade off and reaching a code optimization that allows them to be
able to develop perfect “ad hoc” solutions. It was affirmed that ICA is a market leader in food retail
and desire to maintain this position by increasing their market share and profit by keeping
customers satisfied. It’s not forecasted, however, from the IT department, to begin working with
unstructured data, because it will not be possible, yet, to obtain really relevant information
(Granberg, 2013).
4.2. Masoutis Case Study
Masoutis S.A. (Masoutis) was first established in 1976 in Thessaloniki, Northern Greece, and since
then it possesses the largest network of stores in Northern Greece and is a leader in the retail sector,
while at a Greece-wide level the company is amongst the four (4) largest chains. With 243 stores,
224 supermarkets and 19 wholesale cash & Carry outlets, Masoutis S.A. covers all the prefectures
of Makedonia, Thrace, Thessaly, Epirus, Thesprotia as well as the islands of Limnos and Lesvos.It
employs more than 6000 people (Masoutis Annual Report, 2012) and it has met a significant
increase in its sales the last years, with an impressive growth of sales of 11% between 2011 and
2010 (Kiosses L, Panorama of Greek Supermarkets 2012).
As mentioned above, the people interviewed in Masoutis were: the Marketing Director of Masoutis
Apostolos Filaktos, with his main responsibilities being the supervision of all marketing activities
within the firm, such as: the design, implementation and development of business and marketing
plans, translating business objectives into strategies and growth for Masoutis and the supervision of
all activities of data analysis that are being held within the Marketing Department in order to
correspond to the company’s needs and targets.In addition, a telephone interview with an employee
of the Data Analysis Department Katerina Papadopoulou was conducted, in order to get her
perspective on the topic as well. Katerina’s Papadopoulou main responsibilities are to elicit
information entered in Masouti’s information systems, to check the accuracy of registration, to
detect any errors and ultimately to inform her superiors . After verifying that Masoutis has been
engaging to practices of BDA as defined above on the present paper, we proceed with discussing
the implementation of those practices within the company.
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4.2.1. BDA implementation within Masoutis
The main fields of implementation of BDA practices in Masoutis are similar as the one described
with ICA. When referring to the analysis of Big Data in Masoutis, they mainly refer to the
collection and analysis of data stemming from the receipts from the daily transactions at all the
branches. As such, they are observing what customers are buying the most, in what frequency,
combination of products and during which time periods. As Mr Filaktos reveals, they are quite
satisfied with the effectiveness of their BDA practices and the reason why they are using those tools
are the fact that they consider them as the most suitable and functional to cover the firm’s needs.
However, they believe that the full potential of those analytical practices is yet to be discovered, and
although they seem to be helping the company, in Masoutis they constantly tend to improve their
practices, update and revise them in order to evolve (Filaktos, 2013).
In Masoutis, they are using technological platforms bought from the vendors, but, as it was the case
in ICA, they design their own software in order to serve best the interests of the company. The data
the select mainly relate to costs, sales, profits and markets and the areas where their results are
mostly used are primarily to inform the administrative department for the preparation of accounts,
to provide information to various departments mainly concerning the customers’ preferences in
order to adjust the company’s marketing campaigns and personalize offers (Filaktos 2013,
Papadopoulou 2013). The questions and the search for answers to these are the driving force for the
implementation of practical data analysis and therefore, there are specific questions in different
occasions which they intend to answer when handling those kind of practices.
Some projects which Masoutis have initiated with BDA practices include projects to assist the sales
of products by region and category of the store, to assist sales of products by category and level
code, projects to assist the sales of some specific sections of the stored, like the grocery section,
butcher section etc and finally to increase the profitability and improve the management within the
stores. The analysis of data stemming from purchases has helped the firm understand which
sections perform better, which products are mostly bought and therefore helped them initiate
strategies to improve the marketing of the products and the overall performance of the firm. The
people who were responsible to decide to perform data analysis tasks in Masoutis’ business were
the heads of the various departments of their business (such as the sales, marketing), and the
commercial and financial manager, without exception and the president of the company. The people
who work in these practices come from individually departments of the company, essentially from
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the Data analysis department combined with employees of other departments according to the
specific needs of each project.
The main reason why they start dealing with data analysis, was claimed to be a quest for sound
decision making (Filaktos, 2013). They firmly believed that the BDA would provide them with
answers to critical questions concerning the correct operations of their business. In Masoutis, they
seem satisfied so far with the results they get from data analysis, because the company is claimed to
have plenty and satisfactory tools to extract data and employees claim to have every tool they need
at their disposal, as Katerina Papadopoulou expresses (Papadopoulou, 2013).
4.2.2. BDA as a driver for CA in Masoutis
When discussing the impact BDA has had on the company, it was stated that it is believed that the
data analysis has certainly influenced positively the performance of the organization (Filaktos,
2013; Papadopoulou, 2013). Indeed, both interviewees have highlighted its importance. In specific,
as Filaktos stated, “the more an organization knows about the operation and financial results
deriving from it, the better it can organize its operations, provide and predict economic outcomes
and it takes better strategic decisions which further consolidate it in the market. As a result, the
competitiveness of the firm is also enhanced” (Filaktos, 2013). Data analysis is also seen as a way
for the company to gain an advantage over its competitors, because, since the decisions and goals
put from Masoutis’s management is very often based on data analysis, it is believed that that this
analysis can serve as an advantage over competition as well (Filaktos 2013, Papadopoulou 2013).
However, the adoption of a system of data analysis itself is not seen as capable of producing a
competitive advantage that will hold over time. Both interviewees stress out that those systems are
simply the tools used in order to obtain a competitive advantage. “If the use of these tools is not
done in a prudent and orderly manner and if wrong practices are followed in terms of
administration, then their effectiveness can be annihilated”, says Katerina Papadopoulou. The other
resources that need to be combined in order to give this advantage over competitors are, essentially,
other parts of business, employees, knowledge and business administration. In the sections dealing
with data analysis systems, when employees are fully trained in these systems and have all the
necessary knowledge required in order to be able to give the most to their work, along with
continuous education and motivation from top management, it is believed that it can create the best
conditions for increased competitiveness over other companies in the same industry (Filaktos 2013,
Papadopoulou 2013).
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When asking whether employees of the company can work with data analysis systematically in
regular activities and routines, Katerina Papadopoulou, from her personal experience reveals that
“all authorized employees can have daily access to company data and retrieve any information that
is necessary for analysis” (Papadopoulou, 2013). Both interviewees agreed on the fact that plenty
activities were taken from the organization's management to support and assist in the
implementation of analyzing data: The management of the company every year invests significantly
on staff training on new systems data analysis. It is also believed that the company's objectives are
the same the objectives of other organizational resources and the use of data analysis, since they
work and are used on its behalf and the company sets the guidelines to apply.
It is believed that data analysis practices, along with their interactions with other departments, offer
value to the company, because they have helped to exploit strategic opportunities through
benchmarking data analysis. In specific, they are guided to improved performance through
continued determination, understanding, and adapting discrete practices and procedures identified
within and outside the activities of each department. The development and consolidation of multiple
data sources leads to the extraction of information that can be converted into knowledge, revealing
trends and patterns in data to make informed decisions at the right time in the long run will raise the
value in Masoutis’s business (Filaktos 2013, Papadopoulou 2013). This conversion into knowledge,
which results into right decisions and a corporate culture driven by sound and fact-based decision
making, is a result of long term interactions of BDA practices with organizational resources which
are specific in every business context and quite difficult to imitate, as Apostolos Filaktos says.
It is supported that the performance of Masoutis would be significantly deteriorated, had one
removed the use of BDA, since they started getting used to a decision making which is based on
facts and they gain knowledge that they could not even imagine in the past from analyzing data
from various sources. They would not know anymore what the customers want and how to achieve
that. Concerning the near future of BDA practices in 5 years, it is supported that due to the
increasing demands of the administration, arising from the strong competitiveness of the industry,
Masoutis we will be using much more "powerful" systems analysis in terms of capabilities. These
practices will become more analytical, ie developed at a greater level, it will be of such a great
volume and velocity capabilities and whatever the management asks, concerning any decision or set
goals, will be feasible to be documented and analyzed. Moreover due to BDA they might be able to
implement new strategies and invent different bases and priorities for decision making.
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5. Analysis
This section is going to analyze the information gathered from the case studies from a theoretical
perspective, in order to give an answer to the research question of how BDA are used and perceived
to lead to organizations’ competitive advantage. By using the conceptual model of Wade & Nevo
(2010) as guidance, we start by examining whether the two enable conditions of compatibility and
integration exist in relationship between BDA and other organizational resources, to continue with
the concept of synergies which can lead to strategic potential.
5.1. Potential Synergy and Enablers of Actual Synergy
Synergies have been explained to be the positive capabilities arising from the interactions between
IT assets and other organizational resources (a system named as IT-enabled resources). In fact, as it
was confirmed by the interviewees, BDA cannot be seen in isolation, as something that can stand
alone, but only in collaboration with other factors or resources. This coincides with our adoption of
the model of Wade and Nevo (2010) who drew their attention to the relationships emerging
between IT assets with other resources. According to that, before the proof for existence of an
actual synergy, there is an anticipation of a potential synergy and also two enabling conditions of
integration and compatibility. If those factors apply, then there is an actual synergy.
Anticipation: Organizations tend to anticipate how IT-enabled resources may contribute to the
achievement of organizational goals, thus anticipate potential synergies (Wade & Nevo, 2010) and
it was in fact stated from the cases that before engaging in the investment of Big Data databases and
tools, they had anticipated that those would help mainly in spotting the behaviors of customers and
helping with increasing their loyalty with the collaboration with the Marketing departments. Thus,
they anticipated a potential synergistic relationship between BDA and Marketing department, seen
as another organizational resource.
Integration: Integration efforts were discussed to refer to the efforts of management to assist the
implementation of the IT asset within the organization. In the context of this paper, integration
effort is needed not only to ensure that an IT asset and an organizational resource interact, but that
they do so in a manner that is congruent with the organization’s goals (Wade & Nevo, 2010). In
fact, ICA has claimed that management had evaluated the potential synergy between BDA and
other organizational resources and ensured that they are properly combined and in line with the
organization’s goals. There were claimed to be efforts of encouraging employees for the
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implementation of BDA and there was also another type of integration effort activity that is still in
progress; this refers to the modification of organizational structure in order to assist the best
exploitation of BDA, mentioned above. In addition, Masoutis’s management is claimed to invest
significantly on staff training on new systems data analysis.
Regarding the issue of goals-sharing, both companies have witnessed that during the process of a
common project the different objectives become aligned and the departments work together to
perform as good as possible, and that the management puts in place efforts to align different
objectives with the enterprise wide strategy. It is observed that management’s efforts to integrate an
IT asset and other organizational resources positively impact the extent of realized synergy and the
extent of their compatibility (Wade & Nevo, 2010, p.173), and both companies have verified the
existence of efforts of integration from their management, in a way which is aligned with the
companies’ goals. However, it was retrieved from all interviews that there is room for improvement
and that management should get better familiarized with the potential of BDA. It was also evident
that one of the hurdles they meet in the BDA implementation is that they do not really know what to
do with all the data they gather, and therefore that they do not have a straightforward strategy on
what exactly is that they need to know. We argue that only when management realizes what they
can reach by exploiting BDA in their full potential, they will be able to employ a clear strategy,
relatively inform employees and facilitate their employment through more projects with
management sponsorship and training sessions. In addition, it was highlighted from Masoutis the
management’s efforts to train the relevant employees to new BDA systems, but it was never
mentioned that there were real efforts to motivate new employees to start using data analysis for
various matters.
Compatibility: Concerning the compatibility between BDA and other organizational resources, both
companies have confirmed that there are daily interactions between the departments that are
involved in projects with BDA. They claim to have free access to databases if they need to find
information already stored and organizational resources-departments are claimed to be able to apply
BDA in their regular activities and routines. This shows the existence of compatibility to a certain
degree, which is an attribute that makes the synergy feasible, since a synergistic relationship will be
able to occur if the two components are able to work together on a regular basis. However, we can
say that the degree of existing compatibility is limited due to the fact that “only a few employees
have the necessary understanding of BDA needed in order to employ them on a regular basis”
(Holmstrand, 2013). The mass of employees normally requests the use of BDA to be done through
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them and this phenomenon might weaken the perceived degree of compatibility. It was specified
that BDA are not treated by ICA just as a single project but as an ongoing process; however, this
does not seem to be the case to us because simply having access to databases does not make it a
continuous process, neither can it be an ongoing process when the access to BDA in reality is
feasible only through a few employees. In addition, the same procedure seems to be happening in
Masoutis as well, with Katerina Papadopoulou stating that “all authorized employees can have daily
access to company data and retrieve any information that is necessary for analysis” and the rest of
the employees have access to data only through the access of the authorized ones.
With the information derived, our cases seem to possess the enabler concepts of integration and
compatibility (in the extents described above), which convert the potential synergies anticipated by
the companies to actual ones (see figure 2). We will see now how those synergy relate to strategic
potential and further CA and how BDA are perceived to associate with the three attributes of Value,
Rarity and Inimitability as recognized from the conceptual model of Wade and Nevo (2010).
5.2. The strategic potential of IT-enabled resources
Since synergistic relationships seem to exist in the cases (which form IT-enabled resources), it is
interesting to see how important a role BDA play in that synergy. We can verify whether they are
crucial for the existence of the synergistic relationship, by asking if their removal would annul the
synergy (Wade & Nevo, 2010). Indeed, both companies highlighted BDA as a major contributor by
saying for instance that in case one had to remove BDA from the organization’s reach, the effect
would be tremendous: in the case of the departments which use them the most and therefore
implement the major synergies (like CRM department), it was stated that the interruption of the use
of BDA would have a negative impact on those departments, but also for the whole company. They
“would not know for example, what their customers were buying and why, with negative
consequences on firm’s performance” (Holmstrand, 2013).
The strategic potential of BDA with other resources is coming through the existence of the three
properties of value, rarity and inimitability (see Figure 2):
Value: In relation to whether BDA have provided value for the organization, ICA claims that it has
done so, by allowing the company to capitalize strategic opportunities through the provision of new
products/services. In specific, with BDA, ICA can now store and access data about their clients and
their preferences and subsequently customize products and offers on a personal level. Indeed, the
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company claims to have gained value through the development of innovative services such as
“Mina Varor”, and the role of BDA was crucial during this process because of the high Volume,
Variety and Velocity of the data collected. BDA have also helped the company, as explained, to
identify a percentage of waste necessary to cover the different needs of both Logistics and Sales
departments, and also helped with building new stores in the most appropriate locations. BDA are
also claimed to enhance existing services by making them more efficient, like, for example, the
improvement of the products’ placement on the shelves and even in ameliorating the human
interactions between ICA employees and customers that take place daily in the stores. It was also
mentioned that BDA can lead to the provision of completely new ways of products offering by the
approach of category management, which will be another way to bring value to ICA. We believe,
however, that this will require more time, necessary for ICA to better investigate the full potential
of BDA and improve the way those are implemented inside the company. In addition, in Masoutis it
is claimed that the development of BDA practices leads to the extraction of information that can be
converted into knowledge, revealing trends and patterns in data to make informed decisions at the
right time in the long run will raise the value of their business.
Rarity: Both companies agree also on the fact that even if BDA tools are not rare in the market and
several companies can buy them, this does not mean that all of those companies can develop certain
IT-enabled resources, which makes them rare to obtain (Wade & Nevo, 2010). For example, as ICA
claims, although their competitors have access to the same tools and databases of BDA as they do,
it is only ICA that have developed the specific collaborations between those assets with their other
resources in order to achieve better performance.
Inimitability: Those kinds of synergistic relations between BDA and other organizational resources
are claimed by both companies to be difficult to imitate as well. Due to complexity, competitors
might not be able to understand the nature of the relation between them and thus, to develop the
same relation in order to achieve the same performance. Indeed, it was particularly stressed by the
interviewees and also supported from ICA’s CEO in their annual report, that are the soft skills, such
as knowledge, human relations and management commitment that allow the company to gain and
maintain over time an advantage on their competitors. Thus, what is hardly imitable for the other
retailers are exactly the relations between those IT assets with the above organizational resources.
This exact argument was also stressed out by Filaktos in Masoutis S.A, who stated that “a whole
corporate culture driven by fact-based decision making is required, which is a result of long term
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Alessandro Galletti & Dimitra Papadimitriou
interactions of BDA practices with organizational resources which is specific in every business
context and quite difficult to imitate” (Filaktos, 2013).
5.3. IT Enabled Resources and Competitive Advantage
As mentioned above, the attributes of value, rarity and inimitability can lead to the creation of CA
for companies because they confer strategic potential to firms’ organizational resources (Wade &
Nevo, 2010). Therefore, those companies who own those kinds of strategic resources can
implement strategies that are unavailable to their competitors (Barney, 1991). Therefore, the ability
of BDA to generate CA and affect performance is contingent upon the above properties. The
analysis conducted so far revealed that based on the interviewees’ perception, the two cases present
both enabler conditions (compatibility and integration) requested by the model developed by Wade
and Nevo (2010) in order to transform the potential synergy in actual and also the three factors
(rarity, value, non-imitability) which confers strategic potential (see Figure 2). In addition, all
interviewees stated several times that the positive relations between BDA and other organizational
resources can allow their companies to gain a CA. It does so, by developing new services for their
customers (Mina Varor), exploiting market opportunities (opening a new store in a not served area),
improving the efficiency of its supply chain (category management), increasing the amount of sales
through the study of “customers lap” and “product pairing” and by increasing sound decision
making. These are all examples of practical applications that allow the company to improve its
performances and they all derive from the IT-enabled resources and actual synergies.
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Figure 2 - Application of the model of Wade and Nevo (2010) in the cases
5.4. Discussion
In terms of factors that relate to a CA, our findings suggest that a high level of management efforts
of integration and a high level of compatibility, are perceived to be associated with a higher
likelihood and extent for the development of the subsequent synergies but also the relative strategic
potential. This means that the stronger the existence of the enabler factors is, the stronger the
strategic potential of the enabled resources will be (Wade & Nevo, 2010, p.173). Indeed, a potential
critical aspect in our case might be considered the lack of employees with the sufficient knowledge
to understand and manage BDA; this might reduce the grade of compatibility, because it makes
harder to develop routines and daily interactions between departments and resources. Moreover, the
fact that BDA do not seem to be, in practice, an ongoing process, but independent projects, shows
that the extent of compatibility is limited and needs to be enhanced in order to result in the creation
of stronger subsequent synergies. In addition, the interviews with the shop owners of ICA revealed
that they are not closely involved with BDA. Thus, we argue that, because they are the closest to the
customers and to their needs, involving them in particular BDA projects would positively affect
compatibility and the strategic potential of the enabled resource.
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Regarding management efforts, it was stressed how important the mandate from the management is
in order to empower the use of BDA even between employees and departments that are not that
used to them. We believe that this might be even more important at the starting point of the process,
where the companies are nowadays, because cultural changes within the organization should be
strongly supported from the very beginning of the process by all the management team (Thompson
et al., 2010, p.399). However, it might be possible that, especially because only small steps have
been made so far, both companies’ strategies on BDA seem to be lacking. This because, even if
there is a clear idea of the direction that has to be followed, it was stated that “they do not really
know what to do with all the data they gather” (Granberg, 2013). We argue that this problem,
although mentioned, it was underestimated by our interviewees and, instead, it should be considered
a strategic issue that will take time and resources to solve in order to understand the complete
potential of BDA. Furthermore, in the case of ICA, since store owners cannot directly pose their
questions that can be answered with data analysis to the head offices, this problem becomes even
more acute: store owners’ questions may reveal every-day needs arising from the actual practice.
Those needs, in turn, can provide insight on how to use the data being gathered. The same applies in
the case of Masoutis S.A, where is stated “that they foresee the future of BDA as being capable to
reply to any kind of question that the management may pose” (Papadopoulou, 2013), assertion
which reveals that it will still remain up to the management to try to find what knowledge may be
missing from the company’s reach and not to the mass of employees about everyday matters.
Thus, much attention should be put on integration efforts as an enabler condition not only of
synergies and, thus, CA, but also as a way to enhance compatibility (Wade & Nevo, 2010, p.173).
Indeed, the reinforcement of integration efforts, through training sessions, management
sponsorships, etc. will improve employees’ knowledge about BDA and thus enhance daily routines
and interactions within the company. In fact, we believe that management efforts are not as efficient
as they should be in order to integrate completely BDA in firms’ operations, as the limited amount
of employees with sufficient knowledge proves. In addition, the lack of strategy might be also
considered the reason of the partial satisfaction expressed by all the interviewees regarding the
successful implementation of BDA. We believe, indeed, that a higher management commitment can
lead to a better implementation of the projects and thus to a better use of resources such as time and
money defined as “luxuries” in that industry by the interviewees. In addition, the training sessions
should focus not only to new IT platforms but also to expansion of the employees that have access
to them, in order to enhance the degree of the existing compatibility.
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Moreover, as it was stated by David Holmstrand, retail industry in Sweden is a business that did not
face revolutionary changes in its operations during the last years. This means that the vast majority
of consumers still prefer to shop in the physical supermarket instead than online, thus the
“emotional” aspects of the shopping experience is still important. As he said, “BDA is still
something new in a business that basically operates in the same way as always”. With data,
however, revealing that the amount of customers who are willing to shop online is constantly
increasing (Euromonitor International Passport, 2012), we argue that: the business should probably
not operate the same way as always, but, instead, follow those trends; ICA, for instance, is a market
leader in the industry and wants to remain market leader in digital sales as well (ICA AB, Annual
Report, 2012).
6. Conclusions and beyond
6.1. Concluding Remarks
The purpose of this thesis was to see if BDA are used and perceived to lead to firms’ CA through
the development of synergistic relationships with other resources. We reached our objective through
capturing ICA’s and Masoutis’s managers’ perception on the issue. In fact, the analysis of our
empirical investigations has revealed a pattern on how BDA are perceived to lead to a CA for firms:
BDA can be seen as a driver that can lead to CA due to the synergies that are developed with other
organizational resources. Moreover, since there is an involvement of other resources, the analysis of
our findings highlighted the importance of the two enabler factors of integration and compatibility
in the creation of the relative synergies. This is because those two factors facilitate the creation of
the aforementioned synergies. It was also presented how BDA are perceived to bring a CA to the
organizations by making resources more strategic, through the attribution of three factors of value,
rarity and inimitability. Indeed, it was revealed that those relations between BDA with other
resources such as soft skills, knowledge and human capital are perceived to be rather difficult to
imitate for competitors and to bring value to the organization.
However, even though BDA are perceived as capable of providing a company with a CA, they are
connected to several problems. First of all, there are problems which are connected to
management’s commitment and management’s efforts to integrate BDA effectively within the
organization. In addition, there are problems related to the ability of an organization to make BDA
compatible to its processes and easily accessible to everyone inside the company. To be specific, an
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example of problem that limits such perceived compatibility can be the way by which BDA are
routinized in the everyday tasks and by whom are implemented. Another hurdle can be a lacking
strategy which clearly defines what exactly the company wants to know from all the data gathered.
Therefore, those are the areas where organizations should probably focus their BDA investments in
their quest for a CA and employ straight-forward BDA strategies.
6.2. Contribution
In terms of contribution to literature, this thesis, by directly studying how BDA is perceived to lead
to a CA, provided a base for a better understanding on how IT as a resource can help companies
achieve a CA. It also applied a model adopted from Wade and Nevo (2010), and gave some
empirical evidence concerning the application of the model in a specific example of IT and
enhanced it therefore also with empirical validity. It also contributed to IT literature by providing a
basis for a deeper understanding of what constitutes BDA and how they are used from an
organization to become more competitive. Overall, the novelty of the study is the connection
between such a recent technological application of BDA and the study of their effects under the
light of the traditional and long existing theory of Resource Based View of the firm, in order to
understand the effect of a new technology to firms’ competitiveness.
Concerning the implications in management, the present study provided an understanding of how
BDA can be implemented within corporations in order to reach better performance and further gain
a CA. Organizations interested in the use of BDA can better understand which factors are perceived
to lead to CA. The strategic potential of BDA is contingent upon the organization’s ability to relate
those assets to other resources and therefore create strategic IT-enabled resources. Thus, it alerts
management to consider the importance not only to the IT assets in which they invest but mainly to
the other resources in which those are implemented. Specifically, managers’ interest should be
focused on how to integrate BDA investments in the way the organization functions and how to
make them compatible with other organizational resources, since their importance in reaching a CA
is crucial. Managers are advised to acknowledge this importance, since it could help them exploit a
higher business value of IT within their organizations.
6.3. Limitations of Study Results and Suggestions for Further Research
While our chosen approach was appropriate for studying this framework, we are aware that some
factors might not be ideally captured and there were several factors which limited our study. First of
all, the study was based in a limited amount of interviews, and although they were with the most
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knowledgeable on the matter, they might not represent the whole picture of the organization, which
can be better captured in further studies. In addition, we are investigating the perceived
organizational effects of a phenomenon which is very recent, and therefore companies engaging in
BDA are still in the beginning of that process. This fact might negatively influence the perceived
creation of the relevant synergies with other resources and the subsequent creation of a CA.
Therefore it is suggested that the effects of BDA on firms’ competitiveness should also be studied
in the years ahead, when companies now more about their use and have discovered better their
potential and which other organizational resources to combine them with.
Furthermore, since this study involves concepts of competition, such as the term of “Competitive
Advantage”, it is valuable for research to know more information on how other competitors of a
market engage in such a phenomenon, something which unfortunately was not feasible on the
present study. A further research should investigate the topic into more competitors of the same
market and thereby compare their answers by drawing relative conclusions, and in this case increase
the validity of the research. In addition, a comparative study might also include organizations which
belong to different industries and that are handling different types of BDA, and compare their
answers on perceived enhancement of competitiveness. Last but not least, due to the novelty of the
phenomenon this study did not measure actual facts of how BDA have affected firms’ performance
but the perceptions of that. Therefore, further studies of actual facts that prove that there is
causality between the use of BDA and effects on firms’ competitiveness would be desirable in the
near future.
Moreover, the importance of the two enabling conditions’ influence on the creation of potential
synergies and a further CA was hereby highlighted. However, it is still under-investigated how
exactly those two conditions enable the creation of synergies and also, how do they further
influence CA. In other words, how does greater degree of integration and compatibility lead to
stronger synergies and higher chances of CA? Development of further models and frameworks
would be desirable.
Another interesting factor involves the exploitation of the created synergies from organizations and
how those might lead to a CA. How do different organizations exploit different synergies created?
An understanding of what makes successful organizations stand out from unsuccessful ones even
though they all possess the same IT assets and comparable organizational resources, would be
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useful. Future research might be able to help understand better this issue by highlighting the barriers
that prevent the unsuccessful organizations to realize those potentials.
Finally, in the model proposed by Wade and Nevo (2010) the factor of non-substitutability is not
considered because it was stated that different synergies might lead to the same performance and
thus to the creation of a CA. This is not in exact accordance with the theoretical definition given by
Barney (1991), where resources’ strategic potential depends on the existence of all four factors of
value, rarity, inimitability and non-substitutability in totality. This aspect should be considered in
the study of competitive advantage. The link between the strategic potential and the creation of
competitive advantage might not be as strong as it would be with all the 4 factors employed.
Therefore, further research should be drafted in order to examine more in depth whether the fourth
condition of non-substitutability applies, if not, for what reason and provide relevant justifications
concerning the contribution of those three attributes to the creation of competitive advantage.
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Personal Interviews
Granberg, O, Software architecture manager in Business Intelligence and IT Services department of
ICA AB, Stockholm, April 12, 2013, ICA AB, Stockholm. Personal interview
Holmstrand, D, Chief manager in Customer Relationship Management department of ICA AB,
Stockholm, May 6, 2013, ICA AB, Stockholm. Personal interview
Filaktos, A, Director in Marketing department of Masoutis S.A, Thessaloniki, July 11th 2013,
Masoutis S.A, Thessaloniki, Greee. Personal interview.
Phone Interviews
Manager of ICA Supermarket Alvikstorg, Uppsala, May 29, 2013, ICA AB, Stockholm. Phone
interview
Manager of ICA Supermarket Samköpt, Uppsala, May 29, 2013, ICA AB, Stockholm. Phone
interview
Manager of ICA Supermarket Torgkassen, Uppsala, May 30, 2013, ICA AB, Stockholm. Phone
interview
Manager of ICA Nära Folkes Livs, Uppsala, May 30, 2013, ICA AB, Stockholm. Phone interview
Papadopoulou, K, Employee in Data Analysis Department of Masoutis S.A, Thessaloniki, July
16th, 2013, Masoutis S.A, Thessaloniki, Phone interview
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Appendix 1
INTERVIEW QUESTIONS
Part 1-Orientation Interview
The purpose of those questions is for us to try to get an understanding of whether ICA is using Big
Data Analytics and in which way.
1.
Can you tell us in a few words what position you own within your organization and what you
are responsible for?
2.
How would you define Big Data?
3.
Is ICA analysing Data referred as Big Data already in a functional way?
4.
a. What systems are you using in order to analyse Big Data?
b. What are the sources where you gather Data from? Do you use Social Media as a source?
c. Which data do you select and what for?
5.
a. In which areas do you employ the results from the Analysis of Big Data?
b. Do you have any specific questions behind that you are looking to answer?
c. Before adopting Big Data Analytics, did you predict where it could contribute best?
6.
a. Can you describe us any ongoing Big Data Projects?
b. Who was the one that decided the implementation of Big Data projects?
c. Are the people working in those projects coming from the IT department or elsewhere?
7.
What is the main reason why ICA started employing Big Data Analytics? What did they
expect to find?
8.
Are you satisfied so far with the results you get of Big Data Analytics? Do you face any
problems in their implementation?
Part 2-Core Interview
The purpose of the following questions is to try to understand whether and how Big Data are
perceived to have an impact on the competitiveness of the firm.
1. Do you know if your competitors are also engaging in the analysis of Big Data?
2. Do you think that Big Data has positively affected the performance of your organization so far?
If yes, how?
3. a. Do you see Big Data Analytics as a way to enhance the firm’s competitiveness?
b. Do you see Big Data Analytics as a way for the company to gain an advantage against its
competitors?
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4.
Alessandro Galletti & Dimitra Papadimitriou
a. Is the adoption of a Big Data Analytics system by itself capable of producing a competitive
advantage that will last over time? (By this term we mean just the Big Data software and tools
as bought from the IT vendors)
b. (if not), what other organizational resources are needed to be combined with Big Data
Analytics in order to give this advantage against competitors?
5.
Can people from your department employ Big Data systematically, in their regular activities
and routines? Can you have daily interactions with Big Data Analytics if needed? Can you give
us an example of how these interactions happen practically?
6. a. Were there any activities taken by the organization’s management to support, guide, or assist
the implementation of Big Data within ICA? What kind of activities?
b. Do you think that uses of Big Data and other organizational resources share the same goals?
Are they the same with the goals of the company?
7.
Does Big Data Analytics together with its interactions with your department provide value to
your company? Is there a situation in which Big Data allow you to capitalize strategic
opportunities?
8. Do you consider your department able to develop new products/services that are not present in
the competitors’ offer thanks to the collaboration with BI department and the use of Big Data?
Can you give us an example?
9.
Do you think that those innovative products/service you developed can be imitated by your
competitors in an easy and quick way or not? Could you give us an example?
10. Would performance of your department be affected, had we removed the use of Big Data
Analytics? If yes, how?
11. How do you picture ICA using Big Data in 5 years?
Greek version
Ερωτήσεις
Μέρος 1-Γενικές ερωτήσεις
Ο σκοπός αυτών των ερωτήσεων για εμάς είναι να κατανόησουμε του κατά πόσον η επιχείρησή σας
χρησιμοποιεί τεχνικές ανάλυσης δεδομένων μεγάλου όγκου (Big Data Analytics) και με ποιον τρόπο.
1. Μπορείτε να μας πείτε με λίγα λόγια ποια είναι η θέση που κατέχετε μέσα στον οργανισμό σας
και ποιές είναι οι ευθύνες σας;
2. Πώς θα ορίζατε ότι χρησιμοποιείτε την ανάλυση δεδομένων καταναλωτών με λίγα λόγια;
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3. Θεωρείτε ότι χρησιμοποιείτε ως τώρα πρακτικές ανάλυσης δεδομένων επιτυχώς και με
λειτουργικό τρόπο;
4. a. Τι τεχνολογικά συστήματα χρησιμοποιείτε προκειμένου να αναλύσετε δεδομένα;
b. Ποιες είναι οι πηγές από όπου συγκεντρώνετε δεδομένα;
c. Ποια δεδομένα επιλέγετε προς ανάλυση και για ποιο λόγο;
5. a. Σε ποιους τομείς χρησιμοποιείτε τα αποτελέσματα από την ανάλυση των δεδομένων;
b. Έχετε συγκεκριμένες ερωτήσεις πίσω από αυτό που ψάχνετε να απαντήσετε;
6. a. Μπορείτε να μας περιγράψετε κάποια projects ανάλυσης δεδομένων στα οποία έχετε
δουλέψει;
b. Ποιος ήταν αυτός που αποφάσισε την εκτέλεση εργασιών ανάλυσης δεδομένων στην επιχείρησή
σας;
c. Από που είναι οι άνθρωποι που εργάζονται σε αυτές τις πρακτικές? Προέρχονται από το τμήμα
πληροφορικής, marketing ή αλλού;
7. Ποιος είναι ο κύριος λόγος για τον οποίο αρχίσaτε να ασχολείστε με την ανάλυση δεδομένων; Τι
περιμένατε να βρείτε;
8. Είσαι ικανοποιημένος μέχρι στιγμής με τα αποτελέσματα που θα έχετε από την ανάλυση
δεδομένων; Έχετε αντιμετωπίσει τυχόν προβλήματα κατά την εφαρμογή τους;
Μέρος 2-Core Ερωτήσεις
Ο σκοπός των ερωτήσεων που ακολουθούν είναι να προσπαθήσουμε να καταλάβουμε αν και πώς
θεωρείτε ότι η ανάλυση δεδομένων έχει αντίκτυπο στην ανταγωνιστικότητα της επιχείρησης.
1. Ξέρετε αν οι ανταγωνιστές σας επίσης ασχολούνται με την ανάλυση δεδομένων;
2. Πιστεύετε ότι η ανάλυση δεδομένων έχει επηρεάσει θετικά την απόδοση του οργανισμού σας
μέχρι τώρα; Εάν ναι, πώς;
3. a. Βλέπετε την ανάλυση δεδομένων ως ένα τρόπο για να ενισχυθεί η ανταγωνιστικότητα της
επιχείρησης;
b. Βλέπετε την ανάλυση δεδομένων ως ένα τρόπο για την εταιρεία να αποκτήσει ένα πλεονέκτημα
έναντι των ανταγωνιστών της;
4. a. Είναι η υιοθέτηση ενός συστήματος ανάλυσης δεδομένων από μόνη της ικανή να παράγει ένα
ανταγωνιστικό πλεονέκτημα που θα κρατήσει την πάροδο του χρόνου; (Με τον όρο αυτό εννοούμε
μόνο το λογισμικό ανάλυσης δεδομένων και τα τεχνολογικά εργαλεία που αγόρασατε από πωλητές)
b. (Αν όχι), τι άλλοι πόροι απαιτούνται να συνδυαστούν προκειμένου να δώσουν αυτό το
πλεονέκτημα έναντι των ανταγωνιστών (για παράδειγμα τμήματα της επιχείρησης, εργαζόμενοι,
γνώση κα);
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Master Thesis – Spring 2013
Alessandro Galletti & Dimitra Papadimitriou
5. Μπορούν οι εργαζόμενοι της επιχείρησης να δουλεύουν με την ανάλυση δεδομένων
συστηματικά, σε τακτικές δραστηριότητες και τις ρουτίνες τους; Μπορούν να έχουν καθημερινές
αλληλεπιδράσεις με την ανάλυση δεδομένων αν χρειαστεί; Μπορείτε να μας δώσετε ένα
παράδειγμα για το πώς αυτές οι αλληλεπιδράσεις συμβαίνουν στην πράξη;
6. a. Υπήρξαν δραστηριότητες που λαμβάνονται από τη διοίκηση του οργανισμού για την
υποστήριξη, καθοδήγηση, ή να βοηθήσουν στην εφαρμογή της ανάλυσης δεδομένων από
περισσότερους εργαζόμενους; Τι είδους δραστηριότητες;
b. Πιστεύετε ότι οι χρήσεις της ανάλυσης δεδομένων και άλλων οργανωτικών πόρων μοιράζονται
τους ίδιους στόχους; Είναι το ίδιο με τους στόχους της εταιρείας;
7. Μπορούν οι πρακτικές ανάλυσης δεδομένων, μαζί με τις αλληλεπιδράσεις του με το τμήμα σας,
να προσφέρουν αξία στην εταιρεία σας; Σας έχουν βοηθήσει να αξιοποιήσετε ευκαιρίες
στρατηγικής σημασίας;
8. Πιστεύετε ότι το τμήμα σας είναι σε θέση να αναπτύξει νέα προϊόντα / υπηρεσίες που δεν είναι
παρόντες στους ανταγωνιστές σας χάρη στην ανάλυση δεδομένων; Μπορείτε να μας δώσετε ένα
παράδειγμα;
9. Πιστεύετε ότι αυτά τα καινοτόμα προϊόντα / υπηρεσίες που αναπτύσσονται μπορούν να
αποτελέσουν αντικείμενο μίμησης από τους ανταγωνιστές σας με έναν εύκολο και γρήγορο τρόπο
ή όχι; Μπορείτε να μας δώσετε ένα παράδειγμα;
10. Θα επηρεαζόταν η απόδοση του τμήματός σας, εάν απομακρύναμε τη χρήση της ανάλυσης
δεδομένων; Εάν ναι, πώς;
11. Πώς φαντάζεστε ότι θα χρησιμποποιείτε πρακτικές ανάλυσης δεδομένων σε 5 χρόνια?
Ευχαριστούμε πολύ.
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