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BIG DATA IS POWER: BUSINESS VALUE FROM A
PROCESS ORIENTED ANALYTICS CAPABILITY
Rogier van de Wetering1, Patrick Mikalef2 and John Krogstie2
1 Faculty of Management, Science and Technology, Open University of the Netherlands,
Valkenburgerweg 177, 6419 AT Heerlen, the Netherlands
2 Department of Computer Science, Norwegian University of Science and Technology, Sem
Saelandsvei 9, 7491, Trondheim, Norway
rogier.vandewetering@ou.nl, {patrick.mikalef,
john.krogstie}@ntnu.no,
Abstract. Big data analytics (BDA) has the potential to provide firms with com-
petitive benefits. Despite its massive potential, the conditions and required com-
plementary resources and capabilities through which firms can gain business
value, are by no means clear. Firms cannot ignore the influx of data, mostly un-
structured, and will need to invest in BDA increasingly. By doing so, they will
have to, e.g., necessitate new specialist competencies, privacy, and regulatory
issues as well as other structural and cost considerations. Past research contribu-
tions argued for the development of idiosyncratic and difficult to imitate firm
capabilities. This study builds upon resources synchronization theories and ex-
amines the process to obtain business value from BDA. In this study, we use data
from 27 cases studies from different types of industries. Through the coding anal-
yses of interview transcripts, we identify the contingent resources that drive,
moderate and condition the value of a BDA capability throughout different
phases of adoption. Our results contribute to a better understanding of the im-
portance of BDA resources and the process and working mechanisms through
which to leverage them toward business value. We conclude that our synthesized
configurational model for BDA capabilities is a useful basis for future research.
Keywords: Big Data, Big Data Analytics Capabilities, Qualitative Coding, Re-
source-based View (RBV), process stages.
1 Introduction
The current political, economic, social, technological and environmental climate in
which firms currently operate, is becoming more and more dynamic and complex. As
today’s firms are feeling pressure to improve their decision-making capabilities, big
data provides a path to higher value and can potentially provide them with a competitive
edge [1]. Therefore, currently, firms are exploring the role and use of big data as a
means to address the ever-increasing complexities and as a strategic information tech-
2
nology (IT) investment. Since there are many definitions of terms like ‘business intel-
ligence,’ ‘data analytics,’ ‘business analytics’ and ‘analytics’—a term that has emerged
as a catch-all term—we define big data as the massive amounts of various observational
data which support different types of decisions [2]. In practice, big data enables busi-
ness and IT managers and executives with a strategic tool, if leveraged effectively, can
provide real-time information that can guide future moves. Although big data provides
firms with many valuable opportunities, there are, however, many challenges that need
to be addressed and overcome. Think, for instance, about identifying the best possible
hardware and software and determining the best suitable infrastructure solution. Also,
think about the cost of maintaining relevant data quality dimensions (e.g., complete-
ness, the validity of data, consistency, accuracy), and also privacy issues related to the
direct and indirect use of big data sources. In light of the above, big data analytics ca-
pabilities (BDACs) have become increasingly important in both the academic and the
business environment. For now, we regard these particular capabilities as an overall
competence that has multiple complementary dimensions that collectively enable firms
to be competitive. BDACs are widely considered to enable enterprises to transform
their current business models and value-added processes [3, 4]. If we have to believe
the white papers, industry reports, and consulting studies, e.g., from Gartner, Forrester,
McKinsey, Deloitte, big data analytics (BDA) will be among the most actively investi-
gated and piloted technologies by enterprises over the next couple of years. However,
talent shortages, privacy, cost concerns, and nascent offerings may impede effective
firm adoption.
Despite valuable contributions in this particular domain, there is still limited under-
standing on how firms need to change to embrace, adopt and deploy these data-driven
innovations, and the business shifts they entail [5]. Over the last years, the scope and
approach of most scholarly efforts concerning BDA primarily focus on infrastructure,
intelligence, and analytics tools. In turn, these contributions substantially disregard
other related resources, as well as how these socio-technological developments should
be incorporated into strategy and operations thinking. Dealing with these particular and
aligning all organizational and IT capabilities is thus considered to be one of the grand
challenges ahead to get sustainable results from technological innovations, including
BDA [6, 7]. However, synthesizing from extant literature, we contend that the previ-
ously mentioned predicaments remain largely unexplored [5], severely hampering the
business and strategic potential of big data. This apparent lack of foundational empirical
work significantly hinders research concerning the value of BDA. Furthermore, it
leaves practitioners in unchartered territories when faced with implementing such ini-
tiatives in their firms while addressing the challenges and opportunities associated with
BDA.
In summary, big data is not a magical panacea; it is still data that daily processes and
enterprise-wide capabilities need to incorporate. Against this background, this current
paper tries to explore the process through which BDA value is obtained and explores
the resources that are important when investigating BDA and how they relate to suc-
cessful adoption. Achieving business value from BDA is crucial because ultimately,
this value is what gives firms a competitive advantage [8]. IS research may address this
particular challenge by exploring the process to generate value from BDA and which
contingent resources play a crucial role in this complex, multifaceted process. While
3
limiting our current scope, we follow the core notion of BDA value by Grover et al. [8]
and regard BDA value as ‘the novel and valuable insights to exploit new business op-
portunities or defend competition threats.’
Thus, our research questions are ‘Through which process stages do firms have to go
for big data analytics initiatives to add business value?’ Moreover, ‘What configura-
tions of big data analytics capability resources—for each of the distinct, but related
process stages—should firms then pay attention to during the implementation of big
data analytics initiatives?’
We structure the rest of the paper as follows. The next section concerns the theoret-
ical background of this study. Then, we proceed to outline the research methodology,
present the data collection methods and our sample, as well as how we uncover patterns,
relationships through the use of qualitative coding. We end this paper with main find-
ings, followed by a discussion and suggestions for future research.
2 Theoretical background
The vast majority of current scholarship in the area of IT-business value research
have grounded their arguments on the RBV of the firm [9]. The RBV is a widely
acknowledged theory that explains how firms achieve and sustain a competitive ad-
vantage as a result of the resources they own or have under their control. The RBV is
grounded in foundational economic scholarship concerned with firm heterogeneity and
imperfect competition [10]. A ‘resource’ in modern research was subsequently split to
encompass the processes of resource-picking and capability-building, two distinct fac-
ets central to the RBV [11]. Scholars also defined resources as tradable and non-specific
firm assets, and capabilities as non-tradable firm-specific abilities to integrate, deploy,
and utilize other resources within the firm Amit and Schoemaker [12]. In general, in-
formation systems (IS) studies that embrace this particular theoretical view, postulate
that IT resources that are valuable, rare, inimitable and non-substitutable (VRIN) will
be more likely to outperform competitors. The scholarship recognizes that competence
in leveraging IT-based resources in combination with other organizational resources is
a source of competitive and advantage across various industries [13-15]. These studies
also suggested that firms that fail to invest in particular types of resources under specific
conditions may cause the collapse of the value of the rest. Although the RBV perspec-
tive may provide some critical insights on the necessary types of IT resources that a
firm must own or have under its control, it does not define how they collectively should
be leveraged to derive value from them. As can be gleaned from the above, there is a
need to reframe the theoretical standpoint from which IT-business value and also the
value of BDA can be examined. We now focus on what BDA is.
2.1 Big Data Analytics
IDC [16] expects that from 2005 to 2020 the digital universe will grow by a factor of
300, from 130 exabytes to 40,000 exabytes. This data growth, coupled with technology
4
advances such as open source technologies, mobile and app innovations, cloud compu-
ting, will fuel enterprises’ demand for integrated BDA solutions. In the context of big
data, it is important to identify the different types of resources, since the level of their
infusion in various business functions can be a source of competitive differentiation
[17]. When these resources and their related activity systems have complementarities,
they are more prone to lead to competitive advantage [18]. To date there have been
studies that attempt to define the buildings blocks of firms’ big data analytics capability,
that is the resources that are necessary to build upon [4, 5, 19, 20]. In essence, these
scholarly contributions adopt their conceptualizations from previous IT (capability) lit-
erature, with little regard towards the particularities and conditions of the big data con-
text. Scholars argue that it is essential to comprehend the full spectrum of factors that
are relevant to obtain business value form BDA [5]. Most research is somewhat frag-
mented which makes it difficult to evaluate the business value.
3 Research methods
3.1 Critical literature review
The purpose of this research is to explore the process through which firms create busi-
ness value from BDA and which contingent resources play a crucial role in this com-
plicated, multifaceted process. To achieve this, we contend that it is necessary to ex-
plore the underlying phenomena and processes of BDA and explore the core body of
literature to develop a clear overview and taxonomy of the phenomena of interest.
Henceforth, we started a critical literature review with the primary focus on the building
blocks of a BDA capability and on the possible catalysts and hindrances in attaining
business value. We employed a relatively comprehensive review of BDA with the pri-
mary aim to identify the central concepts that underlie the dimensions of the theories
used within the context of big data. As a final step, we tried to understand the im-
portance of these concepts through firms that have initiated big data projects and initi-
atives. Table 1 shows the result of our literature review and hence the identified BDA
resources and capabilities.
3.2 Case studies and data collection procedure
As our primary aim is to explore how BDA value is obtained and identify those BDA
resources that are important throughout different phases of adoption, we followed a
multiple-case study approach. This approach is suitable for our research, mainly be-
cause we want in-depth information about BDA phenome in practice; it allows us to
present rich evidence and a clear statement of theoretical arguments [21]. This method-
ology is well-suited to study organizational issues [22] and allows us to gain a better
understanding of how BDA resources and capabilities add value. Moreover, this ap-
proach allows us to apply a replication logic through which we treat all cases as a series
of experiments that confirm or negate emerging conceptual insights [23]. We collected
data through a series of in-depth, semi-structured interviews—to avoid biased re-
5
sponses—with field expert and senior managers from different (international) organi-
zations, i.e., public, private, industry and consulting. Interviews are a highly efficient
way to gather rich and empirical data.
Table 1. Thematic support for critical Big data analytics resources and capabilities
Big data analytics resources and capabilities
References
Tangible
- Technology: New technologies are essential to handle the large volume, diversity, and
speed of data accumulated by firms. Further, firms employ novel approaches for ex-
traction, transformation, and analysis of data.
[19], [20]
- Data: Firms tend to capture data from multiple sources, independently of structures and
on a continuous basis. Aspects concerning data such as quality, sources, methods for
curating are important in deriving business value.
[24], [25]
- Financial: Financial resources can be considered as direct investments in support of
these technologies or working hours allocated to experimentation with utilizing the po-
tential of big data.
[20], [4]
Human Skills
- Technical Skills: Technical skills refer to the know-how that is necessary to leverage
the new forms of technology and to analyze the varied types of data to extract intelli-
gence from big data.
[19], [20]
- Managerial Skills: Managerial skills pertain to competencies of employees to under-
stand and interpret results extracted from big data analytics and utilize them in mean-
ingful ways.
[20], [26]
Intangible
- Organizational Learning: Organizational learning concerns the degree to which em-
ployees are open to extending their knowledge in the face of new emerging technolo-
gies.
[27]
- Data-driven Culture: A data-driven culture describes the degree to which top manage-
ment is committed to big data analytics, and the extent to which it makes decisions
derived from intelligence.
[19], [20]
Also, the interviews allowed us to carefully identify both the technical aspects re-
lated to implementation, as well as the interaction with the business side of the com-
pany. Interviewees were carefully selected using a systematic, convenient, non-proba-
bilistic technique to gain maximal insights from different respondents who cover each
relevant BDA aspect. We identified experts that have the knowledge and experience of
working in a competitive and highly dynamic market which necessitated the adoption
of big data as a means to remain competitive. See table 2 for an overview of all re-
spondents. All interviews were performed face-to-face, except two interviews that were
taken using Skype, in a conversational style, opening with a discussion on the nature of
the business and then proceeding on to the themes of the interview guideline. When
necessary, questions were clarified to encourage more accurate responses. Overall a
semi-structured study protocol was followed during the investigation and during the
process of collecting data [28]. In total 27 interviews were held with key and senior
informants from different firms, departments—through which we obtained additional
secondary company-related documents—including big data and analytics strategists,
CIOs, and senior business managers. We recorded all interviews with upfront (signed)
consent and subsequently transcribed them.
6
Table 2. Profiles of the interviewees
Firm
Industry
Employees
BDA objective
Key respondent *
(Years in the firm)
1
Consulting Services
15.000
Risk management
Big Data and Ana-
lytics Strategist (4)
2
Oil & Gas
16.000
Operational efficiency, Decision-mak-
ing
CIO (6)
3
Media
7.700
Market intelligence
CIO (3)
4
Media
380
Market intelligence
IT Manager (5)
5
Media
170
Market intelligence
Head of Big Data
(4)
6
Consulting Services
5.500
New service development
CIO (7)
7
Oil & Gas
9.600
Process optimization
Head of Big Data
(9)
8
Oil & Gas
130
Exploration
IT Manager (6)
9
Basic Materials
450
Decision-making
CIO (12)
10
Telecommunications
1.650
Market and service intelligence
CDO (5)
11
Financials
470
Auditing
IT Manager (7)
12
Retail
220
Marketing, Customer intelligence
CIO (15)
13
Industrials
35
Operational efficiency
IT Manager (5)
14
Telecommunications
2.500
Operational efficiency
IT Manager (9)
15
Retail
80
Supply chain management
CIO (11)
16
Oil & Gas
3.100
Maintenance, Safety
IT Manager (4)
17
Technology
40
Quality assurance
Head of IT (3)
18
Technology
180
Customer relationship management
IT Manager (7)
19
Oil & Gas
750
Decision making
CIO (14)
20
Technology
8
Business intelligence
CIO (3)
21
Basic Materials
35
Supply chain management
CIO (6)
22
Technology
3.500
New business model development
CDO (8)
23
Technology
380
Personalized marketing
IT Manager (2)
24
Basic Materials
120
Production optimization
IT Manager (4)
25
Technology
12.000
Customer satisfaction
CIO (15)
26
Technology
9
Product function / machine learning
CIO (2)
27
Telecommunications
1.550
Fault detection, Energy preservation
CIO (9)
* Note: CIO = Chief Information Officer, CDO = Chief Digital Officer
3.3 Coding, classifying and mapping procedure
We used qualitative coding techniques to systematically analyze, organize and visualize
the data [29]. We reviewed, analyzed, organized and documented all obtained data on
different occasions using open coding schemes [28]. Together with the outcomes of the
critical literature study as well as all transcripts from the interviews, we clustered data
into a tabular structure. This approach allowed us to identify those resources and capa-
bilities, across three phases of development, which applied to each respective case in
7
our research. We used the applied technique iteratively to gain as much insight as pos-
sible. Two of the co-authors completed the independent coding of the transcripts by the
defined themes. Each coder read the transcripts independently to find specific factors
related to the required resources of a BDAC, as well as on business value derived from
such investments. We repeated this process until the inter-rater reliability of the two
coders (matched in pairs) was greater than 90 percent [30].
4 Findings
4.1 Phases in the development of Big Data Analytics Capabilities
Organizations need to focus on the full range of (IT) resources which are needed to
build a difficult to replicate BDAC and understand through what mechanisms and under
what conditions it can deliver business value [20]. We, therefore, tried to synthesize
and integrate the above theoretical perspectives and working mechanisms, and com-
bined with extant literature and outcomes from the interviews on BDA and explore their
importance in driving business value. The outcome is the Configurational Big Data
Analytics Capability Model (CBDACM), see table 3. The CBDACM consists of two
complementary aspects, i.e., (1) the three different phases and (2) different configura-
tions of BDA resources and capabilities tailored per phase and type of organization
(i.e., SMEs and large firms). The phases—a firm has to go through in obtaining value
from BDA—consist of (I) Strategic initiation, (II) Use-cases and data-driven pilots, and
finally (III) Adoption and maintenance. Our model accentuates the process-oriented
view on how firms can use, align and efficaciously adopt BDA to create business value.
As this model is grounded in complementary resources, capabilities, and working
mechanisms, it is consistent with the RBV of the firm [9], and recent literature on BDA
[3, 4, 20, 31, 32]. We address each of these distinct phases in the next sections.
Phase I: Strategic initiation. The first phase according to the interviewees is about the
initiation of BDA within the firms. Firms usually have to identify strategic priorities
and ask ‘crunchy questions.’ This first step in the initiating phase is independent of the
underlying data (4Vs) and therefore applicable to both traditional and BDA. Therefore,
this phase requires senior management involvement and a project champion that sup-
port this significant development. Example crunchy questions might be “what are cus-
tomers currently saying about our organization?”, or “how loyal are our customers,”
“which indicators measure and represent our enterprise-wide performance?” Part of this
first phase (and this might even be considered a sub-phase) is also the assessment of
the current BDA capabilities. This particular assessment, by the judgment of the ex-
perts, is crucial for the identification of both the scope and requirements for BDA ini-
tiatives as well as the capabilities. The standard assessment could include (but is not
limited to) data and systems, general BI and analytics maturity and capabilities and
8
related skills sets
1
, potentially other relevant aspects like formulated IT strategies, pri-
orities, policies, associated budgets, and investments. These capability assessments are
crucial for identification of the scope and requirements of data-driven and big data ini-
tiatives.
“…Data, infrastructure, system and application assessments allow us to provide
valuable information about the data assets that can be leveraged.”
Phase II: Use-cases and data-driven pilots. Based on our analyses, we identified
a second phase, i.e., Use-cases and data-driven pilots. Interviews show that the first step
in this second phase is the identification and definition of various ‘Use Cases.’ In this
step, challenges within strategic focus areas are identified based on specific and explicit
business need, ambitions, requirement and also possible suitability for BDA, i.e., ‘the
problem.’ Various experts pointed out that these use cases (or stories for that matter)
should define ‘the problem’ relative to the foreseen analytical data lifecycle (consisting
of the following cycle steps: collecting, processing, analyzing, reporting and archiv-
ing/maintenance). After this, firms should, in essence, define a technical approach by
identifying a suitable approach based on the data lifecycle, volume, variety, and veloc-
ity (or even 4V). Moreover, in this process, a clear distinction should be made between
analytical techniques that scale up existing (analytic/data) assets and the once that pro-
vide the firm with new relevant data perspectives. Our coding process suggested that
this part of the Use Case is followed by the refining of a particular business decision
based on analytic results. Outcomes suggest that a second sub-phase of the Use-cases
and data-driven pilots phase, thus, concerns the roll-out of pilots and possible proto-
types. This phase is an essential part of this phase as it could save valuable time and
money for firms as firm target value providing initiatives. A key attribute for data-
driven pilots is the involvement of the leadership. The following excerpt from a senior
manager clarifies this view:
“Ensure direct connection to the business decisions and stakeholders involved to
generate and evaluate results quickly.”
In this process firms should also seek for low-risk, high-value pilot projects as these
might be able to contribute to the foundation for BDA capabilities while simultaneously
cultivating early, and sustaining sponsorship.
Phase III: Adoption and maintenance. The final phase is about the adoption and
maintenance of BDA initiatives. Conceptualization of our coding procedures suggests
that adoption situationally requires both organizational change and a robust technical
environment should be maintained. Interviews suggest that within this phase firms need
to exploit talent, user skills, innovative technologies, and best-practices to continuous
iterative exploration and investigation of past business performance to gain insight and
drive business strategy. This step also links this final phase to the first one. So, our
outcomes suggest that for every type of big data solution firms need to embrace agility,
1
As no single person has all the required skills for BDA success, typical assessments should
cover skill sets across teams, departments in order to identify possible skill gaps and develop-
ment needs.
9
while at the same time (technical) data governance needs to be in place to deliver busi-
ness insights cost-effectively. What we understand from all the interviewees is that
BDA capability transformations require both hard and soft skills and firm resources.
Moreover, as most firms have been heavily investing in enterprise systems to streamline
their processes and recently started cultivating a mindset that focusses on analyzing
data and information to improve performance.
“We see a clear shift from what modern firms and business and IT executives need
to do, an innovative process of automating, to what they need to know on a daily basis.”
4.2 Configurations among the Big data analytics capabilities
Through our analyses, we identified a coherent set of concepts and notions. Collec-
tively, these resources, i.e., ‘Tangible,’ ‘Human Skills,’ and ‘Intangible,’ comprise what
is referred to in the literature as a big data analytics capability. In this research, we apply
a practical mapping approach following a configurational approach [33] using our rich
qualitative data from the interviews. Configuration theory views a multitude of varia-
bles simultaneously through a ‘holistic’ lens. Thus, different configurations of these
(BDA) capabilities can yield superior performance (or ‘business value’). Hence, we
visualize each possible combination of resources and capabilities of these solutions (of
grouped firms) in the form of a matrix. In our research, we use black circles () to
denote that the particular resource was important. Blank circles (), on the other hand,
indicate the absence of it in the investigated cases. In doing so, we try to elucidate
patterns of elements that collectively lead to our focal outcome of interest.
Table 3. Configurational Big Data Analytics Capability Model (CBDACM)
Phase I
Phase II
Phase III
I
II
III
IV
V
VI
VII
VIII
Context
Large
SME
Resources
Tangible
Technology
Data
Financial
Human Skills
Technical Skills
Managerial Skills
Intangible
Organizational Learning
Data-driven Culture
We currently do not distinguish between the main elements of a particular configuration
with larger circles and minor elements (less critical) with smaller ones. Blank spaces
can be considered an indication that the specific condition is insignificant or a don’t
care situation in which the condition may be either present or absent. Also, for each
phase, we distinguish patterns of elements for two types of firms, i.e., A) SMEs and B)
large firms. Table 3 shows the importance of each resource across the three phases.
10
Solutions I and II correspond to large firms. In both solutions, financial and mana-
gerial skills are essential for the initiation of BDA within the firms. Solution III, how-
ever, applies to SMEs where analyses showed explicit support for financial resources
as a direct investment in the support for BDA. Within Phase II we can distinguish two
solutions (IV and V). Firms of solution IV (corresponding to large firms), showed a
strong presence of tangible and intangible resources, and human skills. The focus for
these firms in this phase is now on the know-how that is necessary to leverage BDA
technology and to analyze data. On the other hand, firms of solution V, which were in
the SME size-class, continued to show the presence of technological and financial re-
sources as well as slight focus to extend employee knowledge in the face of emerging
technologies. Finally, within the final phase, we identified three solutions of grouped
firms. Solutions VI and VII focus on strong tangible resources, while the final solution
(SME size-class) shows agility in tangible resources and human skills, while the focus
is on accentuating and strengthening the already present data-driven culture and
knowledge extension capability. These configurational forms in which firms create
business value from BDA capabilities demonstrate an asymmetrical relation as their
composition differs across three different phases differ. These fine-grained outcomes
shed light on necessary capability conditions that co-exist and drive business value. Our
outcomes align well with recent studies that argue that specific combinations of firm
resources, competence, and capabilities enable firms to survive, thrive, and support
evolutionary fitness with the external environment [34, 35].
5 Discussion, concluding remarks and future work
This study tried to unfold and get a better understanding—through 27 interviews with
field experts—of the process through which BDA value is obtained and explores the
importance of complementary resource and capabilities, as well as factors that enabled
or hindered the potential value of big data investments, throughout the different phases.
This research, therefore, makes several contributions to the current BDA research base.
First, our study contributes to the emerging literature of capturing the business value of
BDA investments [4, 19, 20]. Second, we examined the different configurational forms
in which firms generate business value from BDA. Finally, this study synthesized the
CBDACM from the literature and subsequently extended and validated this model
through interviews with big data field experts and consultants. Moreover, our configu-
rational model highlights the importance of different configurations of resources tai-
lored per phase. These configurations—that views a multitude of variables simultane-
ously through a ‘holistic’ configurational lens—differ per phase, as each phase focusses
on different BDA aspects to create business value. These outcomes are essential be-
cause we demonstrate and contend that several important factors need to consider when
implementing big data projects and initiatives. In terms of practical implications, our
study unveils to managers the potential process and core-resources they should focus
on when planning to delve into a big data analytics projects. Our model suggests it is
imperative to turn data into actionable intelligence by developing the BDA capability
to look forward, inform and optimize decision making. What is important, is that firms
keep aligning their BDA initiatives with business needs. Practitioners should, therefore,
understand the firms’ ambitions, the business strategy, and key performance indicators,
11
and then work backward to determine what information and analysis are needed to sup-
port those priorities. Big data must cut across the entire firm, and executives and deci-
sion-makers have a crucial role in creating awareness. Typically SMEs can achieve this
easier than larger firms. When most important stakeholders know why big data is es-
sential and how they are expected to contribute, firms can avoid significant missteps.
Training and education, in that respect, are key tools for making sure everyone is on
board. Also, BDA quite often requires widespread changes to processes, data standards,
governance, organizational structures, governance, and IS/IT. Firms should therefore
effectively focus attention on building broad-based support and helping the organiza-
tion overcome resistance to change. As a first step, firms should be deploying an honest
assessment—in understanding the current BDA capabilities—and the emerging gaps
they will need to close to get more value from BDA investments.
There are limitations regarding our study. First, we currently only did interviews
with the goal of obtaining a deep and rich understanding of BDA. Although our study
is a decent starting point, we cannot generalize the outcomes based on the current scope
of analyses. Future research could build on these outcomes and further validate the
constructs through, e.g., survey research. A large-scale quantitative analysis could pro-
vide more granularity towards the conditions and limits to which big data analytics add
value, and shed some light on contextual factors that are of importance, mainly using a
complexity science approach [35]. Also, we currently did not explicitly compare across
industries, companies of different size and countries. These are also avenues for future
research. Future research could then also explore how firms can synthesize and define
improvement activities that best meet firms’ current and future innovation needs. Fi-
nally, future research could investigate the conditions that coerce firms to start investing
in big data, such as competitive pressures, as well as lag effects which may delay the
realization of business value.
To conclude, our contribution to the big data theory and practice accentuates the
process-oriented view on how firms can use, align and efficaciously adopt BDA to cre-
ate a sustained business advantage. We argue that the CBDACM is a useful contribution
to the literature on how firms gain value from BDA efforts.
Acknowledgments
This project has received funding from the European Union’s Horizon
2020 research and innovation programme, under the Marie Sklodowska-
Curie grant agreement No 704110.
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