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Towards Bridging the Gap Between BDA Challenges and BDA Capability:
A Conceptual Synthesis Based on a Systematic Literature Review
Nico Hirschlein
University of Bamberg, Germany
nico.hirschlein@uni-bamberg.de
Jan-Niklas Meckenstock
University of Bamberg, Germany
jan-niklas.meckenstock@uni-
bamberg.de
Christian Dremel
Norwegian University of Science
and Technology, Norway
christian.dremel@ntnu.no
Abstract
Big data analytics (BDA) and strategies for
implementing BDA have received attention among
researchers and practitioners alike. However, success
stories pertaining to the implementation of BDA remain
scarce. The notion of the BDA deployment gap describes
the chasm between the attributed value potential of BDA
and its actual value realization in organizational
practice. Several research articles indicate challenges
encountered in implementing BDA but lack a
comprehensive systematization of BDA implementation-
related challenges. This research article aims to
systematize those challenges through a systematic
literature review. As a result, we derived five
overarching challenge dimensions related to the BDA
implementation. Based on this systematization, we
adopt the lens of a big data analytics capability and
delineate future research avenues through the
derivation of propositions on how to overcome the BDA
implementation-related challenges, while enhancing
our understanding about how to solve the BDA
deployment gap.
1. Introduction
Big data (BD) epitomizes the enormous potential
to enable data-driven decision-making and is seen as the
new oil for organizations [1], embodying the next
management revolution [2]. However, only the effective
analysis and use of BD, called big data analytics (BDA),
unfolds the exhaustive potential for business value
creation in organizations, facilitating the path from
insights to value [2]. BDA refers to the technologies,
techniques, and processes for using BD to create and
realize business value. For instance, the creation and
realization of business value targets output metrics like
productivity gains and revenue growth [3]. Nonetheless,
the business value realization requires the establishment
of contingent technical assets and complementary
resources [1].
During the past decade, many organizations tried
to adopt and implement BDA, as BDA is nowadays seen
as a necessary technical artifact to stay competitive
within an organization’s environment [2]. Though many
organizations try to adopt and implement BDA,
successful implementation stories remain scarce within
IS research [4]. The notion of the BDA deployment gap
depicts this chasm, stating that there is a significant
discrepancy between the perceived business value
potential of BDA and its actual value realization and
implementation success within organizations [4, 5, 6].
The underlying theoretical lens for explaining value
realization mechanisms from BDA is embodied through
the resource-based view (RBV) and the concept of
capabilities [1]. This lens delineates the process behind
value creation and realization by explaining the required
BDA-related resources and capabilities [7]. Extant
research identified the constitutive elements of a big
data analytics capability and studied its effects on output
variables like firm performance and business value
realization [1, 7], using a set of theoretical perspectives
like the RBV, contingency theory, and service-dominant
logic [8, 9, 10]. As a complementary aspect, several
articles studied BDA adoption with the goal to identify
critical success factors and adoption challenges [5, 11].
However, there are two key shortcomings within the
extant body of literature. First, capability-based
perspectives on BDA only focus on resource-picking
aspects and explain what resources are required for
realizing value, while neglecting to answer how to
orchestrate these resources [12]. Second, there is a lack
in identifying recommendations for the implementation
of BDA and how to address the BDA deployment gap,
as previous research only focuses on the enumeration of
key implementation challenges [4, 5]. To address these
shortcomings, we propose the following research
question (RQ): How can BDA-specific implementation
challenges be systematized and overcome through the
establishment of BDA-related resources?
To answer our RQ, we rely on a systematic
literature review. The next section depicts the
theoretical background for our research endeavor and
outlines the implementation and capability-based
perspective on BDA. In the subsequent sections, we
propose a systematization of BDA implementation-
related challenges and conclude our article with first
steps towards a mapping of challenges to resources,
proposing fruitful avenues for future research.
2. Theoretical foundations
2.1. Big data & big data analytics in IS
research
Big data (BD) represents one of the most
prominent buzzwords in IS research for more than 10
years [1, 13]. The hype around BD is particularly due to
its promised potential for business value realization [3].
Both researchers and practitioners agree on defining BD
based on five distinct characteristics, the so-called V’s.
For the context of our research endeavor in overcoming
the BDA deployment gap, we comprehend BD along the
attributes volume, variety, veracity, velocity, and the
derivable value [3, 13, 14, 15, 16]. From our viewpoint,
the effective use of BD refers to the notion of big data
analytics (BDA), which embodies the key technical
artifact for our article [1]. Within IS research, BDA is
defined through different perspectives. Sample
definitions specify BDA as lifecycle and concept for
analyzing and interpreting data [9, 16], or as application
of analytical techniques to advance business [17]. Some
articles have proven the value potential of BDA, e.g.,
through the establishment of a BDA infrastructure [17],
or the orchestration of contingent resources [8]. Hence,
BDA is called to drive value creation. Consequently, we
define BDA as technologies, techniques, and processes
for using BD to realize business value.
2.2. Big data analytics deployment gap &
implementation-related challenges
Organizations willing to implement BDA as a
means of value creation encounter a diverse series of
potential challenges during the implementation process.
A phenomenon pertaining to these challenges, which
was frequently observed in the extant body of literature,
is depicted as the BDA deployment gap [5, 6]. This term
relates to the “paradox between the enormous potential
of BD across industries, on one hand, and the
observation that actual deployments of BD business
models remain scarce, on the other hand” [4]. The
scarcity of BDA business models is due to the fact that
those require a successful BDA implementation.
Reasons for the existence of the described gap and the
associated rarity of successful implementations
prevalently relate to the challenges encountered in the
implementation process [5]. Deployment gaps and lag
effects are commonly encountered phenomena in IS
research. However, the discrepancy between the
assumed value potential of BDA and its actual value
realization in practice is significantly more prominent
compared to other information technologies. The
preliminary perception gained in initial investigations
on implementations of BDA is that anchoring BDA in a
firm poses BDA-specific technical, organizational, and
personnel-related challenges [11]. From a theoretical
standpoint, it thus far remained unclear, how to
overcome the challenges that impede a successful BDA
implementation. These challenges will therefore be
dissected in detail in chapter 4. What is even more
salient, however, is the observed heterogeneity of terms
used to delineate the challenges encountered in the
implementation process. Exemplary notions include
obstacles, barriers, issues, impediments, and
roadblocks, while the expressions all pertain to the same
concept that hampers the implementation of BDA [18,
19, 20, 21]. To establish a common understanding of the
challenges that firms are required to overcome for a
successful BDA implementation, we conceptualize the
term “BDA implementation-related challenges”. This
term covers the entire breadth of expressions identified
in the extant body of literature, that potentially impede
the effective BDA implementation in organizations.
2.3. A capability-lens on big data analytics
using the resource-based view of IT
The resource-based view (RBV) represents the
most renowned theoretical paradigm to explain
possibilities of organizational value creation and
realization [1, 22]. In line with the extant body of
knowledge, the concepts of resources and capabilities
out of the RBV are prevailing in explaining mechanisms
for value realization from BDA [1, 7, 9, 17]. Thus, the
process of BDA value realization relies on contingent
resources and capabilities [8, 23]. Taking a capability-
oriented stance, the term of a big data analytics
capability (BDAC) has proven as theoretical driver in
explaining the mechanisms behind organizational
benefits through the usage of BDA [1, 7].
There is consensus in the IS research community
to define a BDAC through the lens of the RBV along its
constituent elements, incorporating technical, human,
and intangible resources [1, 9]. Through this lens,
several articles provide exhaustive insights for each
superordinate BDA-related resource [7, 9, 23, 24, 25].
Pertaining to the technical resource category, existing
articles emphasize the need for establishing a multi-
layered BDA infrastructure with several characteristics
like modularity and flexibility, and a corresponding
management for processing and analyzing data [7, 9].
Human resources refer to the whole necessary skill-set
at the employee-level to derive insights out of BD,
summarized with the notion of data literacy [26].
Intangible resources tackle all required complementary
resources to handle BDA in organizations, including
governance, structures, and culture [1, 7]. In the light of
our research endeavor, we adapt and use the distinct
elements of a BDAC to explain what BDA-related
resources are required to overcome certain BDA
implementation-related challenges. In line with the
extant body of knowledge, we argue for orchestrating
BDA-related resources into a BDAC to entirely
surmount those challenges and effectively implement
BDA within organizations [12].
In summary, we define a big data analytics
capability as the organizational competence of
deploying and orchestrating BDA-related resources,
that enable an organization to solve the BDA
deployment gap. A successful overcoming of the
different challenges requires the existence of BDA-
related resources congruent to the challenges, which
need to be synchronized and integrated in an
organizational BDAC.
3. Research methodology
To answer the RQ posed in the introductory
section, we conducted a systematic literature review to
summarize the current state of research on BDA
implementations in organizations. In addition, we
pursue the objective of bridging the deployment gap by
depicting BDA implementation-related challenges and
possible solution avenues. In doing so, we followed the
guidelines for a systematic literature analysis proposed
by vom Brocke et al. (2009) [27].
As a starting point, we substantiated the research
focus in defining the central terms and underlying
concepts that refer to BDA implementation-related
challenges and the foundations and elements of a
BDAC. This step entails the elaboration and definition
of the term “BDA implementation-related challenges”.
Correspondingly, the literature analysis pertaining to the
capability lens comprises the identification of the key
elements of a BDAC, especially regarding the
individual potential of BDA-related resources required
to overcome the previously identified BDA
implementation-related challenges. Former research has
thematized BDA implementation-related challenges to
some extent, while the bandwidth of implementation-
related challenges remains rather limited. Initial
attempts within the extant body of knowledge are
therefore already discussed in chapter 2.
The literature search was streamlined around our
proposed research question and focused on BDA
implementation-related challenges and BDA-related
resources and capabilities. The employed search strings
represented systematic combinations of terms
pertaining to the two overarching topics, namely “big
data analytics”, and synonyms of the term “challenges”
such as “barriers” and “obstacles”, as well as
“capability” and “resource” for the BDAC section.
We scanned the most prominent databases for IS
research (AISel, IEEE Xplore, ACM digital library,
Science Direct, EBSCO Host, T&F) using our search
strings, focusing on results from the last 10 years. For
the identification of relevant articles, we employed an
abstract-based screening method and applied inclusion
and exclusion criteria to evaluate the relevance of an
article for our review. Hence, we only included articles
that specifically discuss BDA-related challenges and
excluded items that only slightly touched the focal topic.
Subsequently, we assessed the quality of articles along
two distinct rankings, namely the VHB Jourqual 3
ranking and the journal ranking developed by the
Australian Business Dean Council 2019. To substantiate
our findings, we frequently discussed the individual
relevance of articles within our research group. Our
literature search led to 20 hits in the BDA
implementation-related challenges domain and 12 hits
on the concept of BDAC.
Following to the completed literature search, we
analyzed the 20 identified articles on BDA
implementation-related challenges using a systematic
coding procedure. Hence, we adopted a three staged
coding procedure along the steps of open, axial, and
selective coding, suggested by Gioia et al. (2013) and
Corbin & Strauss (1998) [28, 29]. In the first step of our
coding procedure, we extracted text fragments from the
articles in our review sample and coded them separately.
This resulted in 218 single challenge statements. During
the axial coding step, we aggregated the single
challenges into 15 2nd order themes, which were lastly
summarized into five dimensions of implementation-
related challenges. The whole coding and mapping
process was conducted in a collaborative manner, which
included iterative discussions between the three authors.
Concluding our research endeavor, we aim to
provide first theoretical and empirical insights on how
to overcome BDA implementation-related challenges
through BDA-related resources. Therefore, our
identified 2nd order themes serve as starting point for our
mapping. For each challenge dimension, we selected
one theme that appeared most pertinent within our
identified review articles, based on its frequency within
the extant body of literature (see Table 1). Subsequently,
the selected 2nd order themes were analyzed through a
BDAC lens. The identification of BDA-related
resources was conducted through the usage of the extant
body of knowledge in form of our review articles on the
constitutive elements of a BDAC. Building on the
selected adequate BDA-related resource, the resource is
explained in the light of the challenges and substantiated
with concrete action items on how to overcome them.
The deduction of concrete action items was performed
through the analysis of case studies that thematized
BDA implementations. We identified eight case studies
that provide in-depth insights on how to implement
BDA within an organization [6, 30, 31, 32, 33, 34, 35,
36] in a systematic literature review using the above
mentioned search terms in combination with the term
“case study”, using the same databases. Based on the
application of the capability-lens and the addition of in-
depth case study insights, we derived propositions for
future research endeavors on how to overcome the BDA
deployment gap.
4. A systematization of BDA
implementation-related challenges
The results of our coding analysis are explained in Table
1. We identified five distinct dimensions of BDA
implementation-related challenges as well as three
summarized themes that further detail each identified
dimension. Each dimension is further described below.
Infrastructure- and technology-related
challenges. This dimension includes challenges
referring to the overall BDA infrastructure, the single
layers in the BDA stack, and the integration of BDA-
specific tools within the technology infrastructure. It
was notably apparent that an immature and inadequate
BDA infrastructure causes also various challenges in
other areas like data management. These include
problems with the bandwidth required for instant data
transmission allowing for real-time processing [37, 38,
Table 1. Systematization of BDA implementation-related challenges (* selected theme f. mapping)
39], and a lack of scalability and integration of data
storage units for large datasets [18, 19, 37, 38, 39, 40].
As a whole, BDA requires a powerful infrastructure that
enables an organization to gain insights from the
available datasets and extract value through the
application of data analysis [2, 19, 41, 42, 43]. The
establishment of a unified IT architecture is closely
related to the described immature BDA infrastructure
characteristics. In particular, a fragmented IT
architecture reduces the interoperability between
corporate IT systems and the BDA technology stack,
requiring the establishment and validation of system
connectivity [11, 18, 41, 42, 44, 45]. Moreover, the lack
of available BDA-specific tools can diminish the
functionality of the BDA technology stack [38, 43].
Data- and data management-related challenges.
This dimension concerns issues that can be attributed to
the data itself and the associated data processing and
analysis. A frequently observed phenomenon is the
insufficient data quality, recognizable through a lack of
data standardization, a high degree of data
heterogeneity, and data inconsistencies as well as
incompleteness [18, 19, 21, 37, 38, 43, 44, 46].
Observable consequences of insufficient data quality
encompass interpretability, reliability issues as well as
lower trustworthiness of derived insights [11, 37, 39, 40,
45, 47]. The unique characteristics of BD furthermore
affect the utilization of data along the entire analytical
lifecycle. Exemplary challenges within this lifecycle
include data transmission, data integration and (pre-)
processing, data mining and analysis, data modeling,
and data accessibility [19, 20, 37, 38, 39, 40, 43, 45, 46,
47]. Further issues arise from the inherent security and
privacy concerns associated with BD [18, 19, 21, 37, 38,
40, 42, 43, 47]. The deficiencies of analytical
techniques, which can be applied to different datasets,
are closely related to the above-mentioned lack of BDA
tools. Both issues hamper the derivation of actionable
insights from the analyzed datasets [18, 19, 37, 42, 45].
Skill- and expertise-related challenges. The third
identified dimension delineates challenges related to
skills and expertise on the employee-level, especially
regarding the creation, development, and management
of BDA-related competencies. Firms require a focused
talent management to hire and retain skilled BDA
personnel, including data scientists and engineers.
However, many organizations struggle in creating a
focused talent management competency, resulting in a
shortage of well-trained employees to support a
successful BDA endeavor [11, 41, 43, 44, 45, 46, 47].
The described scarcity of skilled BDA-experts in the
organization is further aggravated by the current
shortage of specialists on the labor market [11, 19, 40,
42, 44, 46]. The resulting lack of data literacy poses
further challenges. This includes a lack of technical,
analytical, managerial, and relational skills [11, 13, 18,
20, 21, 38, 44, 46, 47, 48]. As described by Vidgen et al.
(2017) and Dremel (2017), the establishment of domain
knowledge can therefore be seen as a key success factor
for BDA [47, 48]. Thus, it is necessary to create an
integrated BDA competence spanning across technical
and managerial domains, which requires the formation
of a central education program. However, dedicated
training programs to educate staff on BDA are yet rarely
established in organizations [11, 38, 40, 43, 48, 49].
Organization- and management-related
challenges. This dimension particularly describes
challenges referring to a strategic management of the
BDA implementation at the organizational level. The
challenges belonging to this dimension accentuate the
crucial role of top management, including funding,
strategic vision, and commitment towards the BDA-
driven transformation [11, 13, 18, 19, 38, 41, 43, 44, 46,
47]. Top management is required to define, measure,
and control the business value realizable through the
implementation of BDA, thus to justify the business
case and the corresponding investments [11, 18, 40, 47,
48, 49]. Besides top management support, an
organization requires the introduction of an efficient
governance framework to control and structure BDA
initiatives across the organization. However,
standardized approaches to govern BDA in
organizations are not available yet, which makes it
difficult for organizations to effectively govern the
entire BDA implementation process. This includes
roles, accountabilities, and consistent processes [11, 38,
39, 47, 49]. The specification of appropriate
organizational structures for BDA projects is inherently
interwoven with governance of BDA efforts. Hence,
BDA requires an organizational frame, including
adapted collaborative structures and working processes
[38, 41, 42, 46, 48, 49]. These structures need to be
accompanied by agile project management and software
development methods to support a swift adaptation in
turbulent environments with a high degree of
uncertainty [42, 46, 48].
Culture-related challenges. This dimension
focuses on issues regarding the behavioral and general
attitude towards BDA. Thereby, the core of those
challenges concerns missing business IT alignment,
which is required for the execution of successful BDA
projects [11, 13, 18, 20, 21, 42, 44, 46, 48, 49]. A tight
collaboration and mutual understanding between
business and IT experts is necessary to secure a business
acumen within the BDA projects [48]. The realization
of business IT alignment entails a mindset change
pertaining to the acceptance of BDA and its effects on
the organization. While business IT alignment can also
be viewed through a structural lens, we considered the
cultural notion of business IT alignment to be
particularly important for the context of BDA
implementation. These mindset changes and the
corresponding cultural transformation are frequently
impeded by a strong resistance to change [11, 18, 21, 41,
44, 46, 47]. This reluctance to change is due to a missing
corporate understanding of what BDA effectively
implies, thus lacking a deeply rooted anchoring of BDA
appreciation within the corporate culture [11, 18, 38, 41,
42, 43, 47, 48]. A lack of fact-based culture thus inhibits
the effectiveness of data-driven decision-making [18].
In sum, we identified 5 overarching dimensions of
BDA implementation-related challenges with 15
associated themes that further describe the
characteristics of the classified challenges. The
developed systematization summarizes the extant body
of knowledge and serves as starting point for the
subsequent mapping of challenges to the BDA-related
resources of a BDAC required for a successful
implementation of BDA.
5. Discussion
To derive initial recommendations to help
overcome BDA implementation-related challenges that
constitute the BDA deployment gap, we adopt a BDAC-
oriented perspective. In the extant body of literature,
establishing a BDAC is considered as an indispensable
driver of implementation success and business value
realization in organizations [1]. Departing from the
BDAC and its constitutive elements, we aim at
proposing a BDA-related resource that particularly
addresses a certain BDA implementation-related
challenge. Thereby, we substantiate the proposed
resource with concrete action items inferred from the
identified case studies to delineate how a specific pain
point embodied in an identified challenge theme could
be adequately treated. In doing so, it is important to
acknowledge that overcoming the described challenges
always entails the orchestration of multiple BDA-
related resources from different categories, which
reflects and emphasizes the capability-driven
perspective on BDA [7]. To summarize the suggested
efforts to overcome a certain challenge, we formulate
propositions that capture the specific pain point and
appropriate counteractions.
5.1. A mapping of challenges and adequate
BDA-related resources
As a starting point, we focus on the challenges that
are primarily addressed by technical resources. From
our viewpoint, the challenges pertaining to
infrastructure and technology as well as data and data
management refer to this category. We adapt the BDAC
perspective suggested by Gupta & George (2016) [7],
who assigned technology and data to the technical
resource dimension. This allocation appears fitting,
since the underlying infrastructure and the data that is
managed based on this infrastructure along its lifecycle
encompass the technical aspects of BDA.
Missing, immature, or inadequate BDA
infrastructure. As part of the infrastructure- and
technology-related challenges, this challenge pertains
predominantly to the BDA infrastructure, its layers, and
the interworking of these layers, aiming at ensuring a
sufficient technical maturity level. To tackle this
challenge, a BDA infrastructure and corresponding
tools need to be gradually established [7]. As the
findings in the cases indicate, the creation of a BDA
infrastructure can be realized through different technical
pathways. As described by Winig (2016) in the case of
General Electrics (GE), a technical platform for
connecting, storing, and analyzing data was created
through the usage of a cloud-based solution called
Predix [36]. The case study of Lufthansa reported by
Chen et al. (2017) describes the creation of a service-
oriented architecture (SOA) for BDA [6]. The use of this
type of architecture ensures modularity and flexibility in
handling and integrating different system components
and tools. In addition, Lufthansa uses an enterprise
service bus as linkage between different IT systems,
providing system interoperability [6]. Alternatively, the
utilization of the Hadoop framework enables
organizations to stepwise create a fully integrated BDA
infrastructure, as reported by Dremel et al. (2020) [31].
To synthesize the different pathways to architecture
realization in the cases, the establishment of a multi-
layered BDA architecture is recommended. As an initial
starting point, a reference architecture suggested by Illa
& Padhi (2018) is used to illustrate the essential layers
of a BDA architecture [50], addressing the tasks of data
streaming and ingestion, data storage, data processing,
and data visualization. To allow for maximum layer
flexibility while ensuring structural cohesiveness, all
layers within the BDA architecture must be connected
to each other using predefined interfaces.
Data usage and handling over the analytics
lifecycle. As part of data and data management-related
challenges, many challenges in handling of BD arise,
especially regarding data processing, storing, and
interpreting, representing the whole analytics lifecycle.
Renowned analytical lifecycles and process models
including the popular CRISP-DM contain a diverse set
of phases, ranging from business and data understanding
to its deployment [51]. To tackle and overcome these
issues, employees must be given the opportunity to
experiment with BD to establish a collective
sensemaking on how to use BD and its underlying
infrastructure. The case study by Koch et al. (2021)
reports on the necessary mindset to drive data handling
and the establishment of data management processes
[32]. According to Chen et al. (2017), data management
processes should be accompanied by implementing
several structural governance mechanisms within a data
management framework, with the goal of establishing
clear responsibilities for the data handling along the
lifecycle [6]. As a starting point for the definition of data
management processes, we suggest the consideration of
the DAMA data management framework [52], which
can be used to specify all necessary data management
process domains. Based on the suggested framework,
more concrete processes for the different domains like
data security and metadata management can be derived.
Based on the insights on how to tackle the
challenges regarding BDA architecture and data
management, we derive the following proposition:
P1: To overcome challenges in the domain of
technology and data management, the establishment of
a multi-layered cohesive BDA infrastructure in
orchestration with the instantiation of data management
processes within a data governance framework is
recommended.
Missing data literacy. As part of the skill- and
expertise-related challenges, the notion of data literacy
embodies an umbrella term for the required individual
competences for handling and understanding BD [26].
BDAC involves human beings as a critical resource for
successful BDA implementations. Human beings
describe the essential resource required for effective
sensemaking from the analysis of large datasets [23].
Effective sensemaking demands the proper usage of
technical resources through a diverse skill set at the
employee-level, while the management expertise needs
to be aligned with the technical skills. However, many
organizations report that their employees have an
insufficient level of data literacy to drive BDA
implementation efforts. The establishment of data
literacy requires a central training and education
program, as stated by Dremel et al. (2020): “We try to
educate our employees and our top management [and]
want to give them an understanding of the world of data
at [PremiumCar].” [31] The benefits of an
organizational education program arise from the central
identification of training needs and the subsequent
possibility of allocating adequate resources. Building on
the notion of adequate resource allocation, the creation
of a data-literate center of excellence (CoE) supports the
diffusion of a firm-wide BDA understanding, as stated
by Krishnamoorthi & Mathew (2018): “Then what is the
role of the 500 people vis-a-vis the rest 99,500 people?
I see our role as the incubator of framework and
approaches to productize and commoditize Analytics”
[33]. To tackle and overcome these issues, employees
must establish sufficient knowledge and skills to be able
to create actionable insights out of BDA. According to
Mikalef et al. (2018) [1], employees need to be skilled
within the technical, business, relational, and analytical
domain. Firms need to be aware that all these skill
domains are required for the creation of sufficient data
literacy. Exemplary skills that belong to the data literacy
concept include data engineering skills, business
acumen, communication, and data visualization skills.
These skills need to be developed in dedicated trainings.
Based on the insights on how to tackle the
challenges regarding skills and expertise, we derive the
following proposition:
P2: To overcome challenges in the domain of skills and
expertise, the initial recognition of the required skillset
for sufficient data literacy, which enables sensemaking
through BDA, and the corresponding development of
training programs is recommended.
The third constitutive element of a BDAC refers to
intangible resources. From our viewpoint, intangible
resources embody complementary organizational
resources that particularly address challenges pertaining
to organization & management as well as corporate
culture. Thereby, we follow the renowned IS business
value perception of Melville et al. (2004) that
conceptualizes an IT capability along technical IT
resources, human IT resources, and complementary
organizational resources [22].
Top management guidance and investment. As
part of organization- and management-related
challenges, both a lack in top management support as
well as investments need to be overcome. The first step
in establishing top-management support is the direct
involvement of the C-level suite in BDA-related topics.
An important aspect of top-management support is that
funding and commitment need to be secured through a
focused assessment of the potential business value
derivable from the BDA implementation. Hence, a clear
investment strategy needs to be developed by the top-
management, which is driven by selected use cases that
promise actual business value. The perspective of
focused top-management support is detailed out of the
CIO’s perspective at Lufthansa: “However, we want to
be with the leading technology but not the ‘bleeding’
technology. We are cautious. We do careful assessment
of the big data technology” [6]. Top management
involvement and value recognition need to be
accompanied by a BDA governance framework to
support a strategy-driven BDA implementation, as
suggested by Chen et al. (2017): “We have a steering
committee on the big data initiative; we went through
our innovation process to discover value from big data,
and we came up with a few lighthouse projects” [30].
Relying on the notions of Mikalef et al. (2020) [53], we
suggest that a BDA governance framework needs to be
developed along practices pertaining to structural,
procedural, and relational dimensions. This framework
needs to incorporate steering committees and a role
taxonomy with defined responsibilities.
Lack of collaboration between business and IT
experts. As part of culture-related challenges, key
issues result from missing cross-departmental
collaboration. The notion of business IT alignment is
called to be a necessary pillar for the execution of
successful BDA projects [9]. However, many
organizations struggle in establishing a common ground
for enabling a collaboration between business and IT
employees. One way to achieve business IT alignment
is the employment of an agile development method such
as scrum, as outlined by Dremel et al. (2020): “We have
to develop a flexibility and agility in regard to our
releases. […] Scrum is one possibility to achieve this
and to get our product management and the developing
team together” [31]. The introduction of new working
modes that bring business and IT closer to another
requires a change management process, as stated by
Beath & Ross (2010) in the case of PepsiAmerica: “For
these initiatives to affect the entire organization or big
pieces of it, you need to have a serious change
management element to the project team. And that
involves communication and education and training”
[30]. Based on these recommendations, we formulate
the following proposition:
P3: To overcome challenges in the domain of
management and culture, the introduction of a BDA
governance framework that includes novel
interdisciplinary working modes, realized through a
change management process, is recommended.
5.2. Implications and limitations
Our findings discussed in chapter 4 possess
implications for academia and practice alike. The
primary theoretical implication is embodied in a state-
of-the-art systematization of the BDA implementation-
related challenges. We provide a structured overview of
BDA implementation-related challenges, aiming at
synthesizing the fragmented literature. Secondly,
through an initial mapping of BDA-related resources to
the identified BDA-implementation-related challenges,
we propose a novel perspective on how to overcome
these challenges using a BDAC lens.
The derivation of propositions out of this novel
perspective informs practical BDA implementation
endeavors in how to overcome the BDA deployment
gap. Simultaneously, these propositions guide scholars
in their future research endeavors, especially for the
execution of qualitative studies, that make use of our
proposed mapping of BDA-related resources to BDA
implementation-related challenges. While prior
attempts only viewed BDA implementation through a
conceptual stance, the formulated propositions advance
our understanding in how to overcome the BDA
deployment gap through the explication of concrete
action-oriented items. A possible future research avenue
lies in measuring the impact of our identified BDA-
related resources on certain output variables like
implementation success and business value realization.
Regarding implications for practitioners, our findings
can help them select case-validated resources to tackle
the challenges encountered in the BDA implementation
on both strategic and operational levels. For the first
time, practitioners are provided with more precise action
items that have proven to help overcome previously
identified challenges in real-case scenarios. Hence, our
identified action items and corresponding resources
serve as initial recommendations on how to successfully
implement BDA within organizations.
Our findings have limitations that need to be
considered when interpreting the results and possible
implications. Most importantly, the extant of literature
on BDA implementations lacks extensive coverage on
successful cases from the industry. Therefore, the
described action items originate from a limited number
of analyzed case studies. In addition, the identified case
studies merely superficially describe how BDA was
implemented and rarely specify how to tackle
encountered challenges. Secondly, this paper only takes
organization-internal challenges into account, while
potential external aspects that impede the BDA
implementation were mostly neglected. Future research
should thus also focus on external factors that pose
challenges for a successful BDA implementation.
Thirdly, we only proposed adequate BDA-related
resources and propositions on one theme per challenge
dimension due to page limitations. This holds true as
well for the number of resources used for the mapping,
which is also due to the limited variety of guiding action
items in the identified case studies. Despite these
limitations, our research still proposes avenues towards
bridging the BDA deployment gap.
6. Conclusion
The underpinnings of a successful BDA
implementation have for long depicted an opaque black
box for both academia and practice. Previous research
predominantly focused on necessary resources and
capabilities that constitute a BDAC, whilst neglecting
the need to address potential challenges encountered
with dedicated resources to help overcome those
challenges threatening a successful implementation.
The visible result of this negligence constitutes the BDA
deployment gap observed in practice. To close this gap,
we conducted a systematic literature review on BDA
implementation-related challenges and provide a
structured systematization of challenges that occur
during the whole implementation process of BDA. We
synthesized the extant body of knowledge on BDA
implementation-related challenges through the
identification of five distinct challenge dimensions.
Building on this systematization, we analyzed case
studies pertaining to their BDA implementation efforts.
Out of these in-depth insights, we developed an initial
mapping of certain BDA implementation-related
challenges to adequate BDA-related resources.
Thereby, we build upon the extant body of literature on
the constitutive elements of a BDAC. As a result, we
formulated three propositions on how to overcome BDA
implementation-related challenges. We intend to
contribute to the body of knowledge on how
organizations can successfully implement BDA and
thus overcome the BDA deployment gap [4]. Based on
our findings, we suggest future research to direct their
endeavors in two possible directions. First, future
research may empirically validate our propositions to
gain a comprehensive understanding of overcoming the
BDA deployment gap. A second direction points
towards the practical investigation of additional BDA
implementation cases to identify countermeasures that
help overcome the BDA deployment gap. Solving these
questions would help companies on their journey
towards a data-driven organization.
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