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Delineating the Business Value of Data-driven Initiatives in Organizations –
Findings from a Systematic Review of the Information Systems Literature
Nico Hirschlein
University of Bamberg
nico.hirschlein
@uni-bamberg.de
Jan-Niklas Meckenstock
University of Bamberg
jan-niklas.meckenstock
@uni-bamberg.de
Sebastian Schlauderer
University of Bamberg
sebastian.schlauderer
@uni-bamberg.de
Sven Overhage
University of Bamberg
sven.overhage
@uni-bamberg.de
Abstract
A key objective of data-driven transformations is to
utilize big data analytics (BDA) to create data-driven
business value (DDBV). While prior research shows the
potential of BDA to achieve DDBV, the concept remains
blurry and an overview of realizable DDBVs is still
lacking. To better understand the multidimensionality of
the DDBV concept and to obtain insights into the
bandwidth of achievable DDBVs, we conducted a
systematic review of the information systems literature.
Based on our results, we present a comprehensive
overview of 34 DDBVs, which are classified according
to their tangibility and locus of value realization.
Furthermore, we describe three research deficiencies:
(1) the missing operationalization of the DDBV concept,
(2) the lack of explanatory mechanisms for DDBV
realization, and (3) missing qualitative, in-depth
insights into DDBV realization processes. Future
research may build upon our systematization and help
closing these research gaps, thereby increasing the
success likelihood of data-driven initiatives.
Keywords: Data-driven Business Value, Data-driven
Organizations, Big Data Analytics, Literature Review
1. Introduction
Big data (BD) is frequently being hailed as the new
oil for organizations (Wiener et al., 2020). Accordingly,
BD as general topic and big data analytics (BDA) as the
approach to extract value from BD are becoming ever
more popular in academia and practice. Recent studies
show that organizations can indeed benefit from the
application of BDA in various ways. Often, BDA is
therefore even viewed as facilitator of a data-driven
transformation, fostering the emergence of data-driven
organizations (DDO) as a whole (Schüritz et al., 2017).
Despite the promising results from early studies,
however, the business value resulting from the use of
BDA has remained blurry. So far, there exists no clear
definition of data-driven business value (DDBV) as a
concept. Related work instead mostly studied the effects
of BDA usage on a few popular (comparatively generic)
factors like competitive advantage or organizational
performance. Other (more concrete) factors like process
transparency have only been studied sporadically.
Hence, current research lacks a holistic overview of the
business values that can be achieved from the usage of
BDA in organizations, thereby neglecting the
multidimensionality of the concept. First steps to
synthesize extant findings and categorize DDBVs can
be found in literature (Elia et al., 2020; Vitari &
Raguseo, 2020). Yet, the according approaches do not
explain the identified business values in detail and rather
focus on discussing prerequisites to realize them. A
coherent systematization of DDBVs with detailed
explanations is still missing. Without a precise
definition of DDBV as a concept and understanding its
multifaceted nature, it remains difficult to assess the
potential of data-driven initiatives in concrete scenarios,
however.
To contribute to the closure of this literature gap,
we present the findings of a literature study, in which we
aimed at identifying and systematizing the concrete
business values that result from data-driven initiatives.
We particularly examine the following research
questions: “Which business values can be achieved by
data-driven initiatives in organizations? How can the
term ‘data-driven business value’ and its multifaceted
nature be conceptualized and systematized?”
To answer these research questions, we conducted
a systematic review of the information systems (IS)
literature. Based on an in-depth analysis of 37 articles,
we present 34 DDBVs and classify them according to
the IS business value taxonomy of Schryen (2013). In
addition, we delineate a research agenda based on three
identified deficiencies in the current knowledge base:
(1) a missing operationalization of the DDBV concept
itself, (2) a lack of mechanisms that explain the
realization of DDBVs, and (3) missing in-depth insights
into the processes of DDBV realization.
Our findings complement current research
activities on the effects of data-driven initiatives in
organizations in two ways: first, we present a unique
overview of DDBVs and provide details regarding the
business values themselves as well as the effects of their
realization. Thereby, we go beyond discussing abstract
values such as competitive advantage, thus describing
the known bandwidth of realizable DDBVs and helping
to better understand the multidimensional nature of the
concept. Second, we provide concrete avenues for future
research to further investigate und understand DDBV as
a concept. Our research endeavor can thus be seen as a
step to systematically unveil and depict the business
value potentials of data-driven initiatives.
We proceed as follows: in section 2, we discuss the
theoretical background, the data-driven business value
concept, and related work. Thereafter, we describe the
research methodology behind our literature review
(section 3). The data-driven business values identified
during the literature review are presented in section 4.
In section 5, we discuss the findings and implications
for research and practice. Section 6 concludes the paper.
2. Theoretical background
2.1 DDOs and the role of BDA
Big data (BD) is one of the most frequently
discussed topics in organizational practice, IS research,
and computer science today (McAfee et al., 2012;
Wiener et al., 2020). It is characterized along several
V’s (Mikalef et al., 2018), emphasizing the extensive
amount of data (volume), the diversity of data sources
and types (variety), the speed of data generation
(velocity), the authenticity of data (veracity), and the
possible value implied in the data itself (value).
The extraction of value from BD is embodied in the
notion of BDA. As such, BDA can be viewed as a
“holistic process that involves the collection, analysis,
use, and interpretation of data” (Akter & Wamba, 2016,
p. 178). BDA further describes a means for “advancing
business“ (Grover et al., 2018, p. 390), and generating
competitive advantages (Vitari & Raguseo, 2020).
Thereby, researchers and practitioners agree that value
creation represents the key objective of BDA. We define
BDA as technologies, techniques, and processes for
using BD to realize business value.
While BDA as a concept has been clarified, specific
characterizations of firms that use BDA for value
realization are mostly lacking. Schüritz et al. (2017)
define a DDO as an organization that “heavily relies on
data to make decisions and take actions” (p. 394). While
this definition emphasizes the relevance of creating
actionable insights, the business value resulting from
data-driven initiatives is left unclear.
Extant research has identified elements of a DDO,
including technical aspects like data science, managerial
aspects such as data-driven business models, and
organizational aspects including a data-driven culture
(Hupperz et al., 2021). Establishing a DDO moreover
requires the holistic diffusion of analytical capabilities
in several functional areas (Hagen & Hess, 2020).
With a particular emphasis on the objective of
business value realization, we define a DDO as an
organization that holistically establishes BDA and
infrastructure capabilities to implement data-centric
sense-making mechanisms and employs data-driven
insights for business advancements (e.g., through data-
driven business models or innovations) to realize data-
driven business value.
2.2 Business value of data-driven initiatives
The resource-based view (RBV) is regarded as the
most prominent theoretical paradigm to explain BDA
value realization (Akter et al., 2016; Grover et al.,
2018). Extant research determined the establishment of
a BDA capability as a necessary prerequisite to realize
business value (Mikalef et al., 2018). Following the
RBV, a BDA capability requires the orchestration of
technical, human, and management-related resources
(Grover et al., 2018). Thereby, the data and the BDA
infrastructure are assigned to the technical resource
component (Gupta & George, 2016). Technical and
business knowledge are attributed to the human resource
(Mikalef et al., 2018). Concerning management
resources, a BDA capability entails elements such as
BDA governance mechanisms as well as a data-driven
culture (Gupta & George, 2016; Mikalef et al., 2018).
Further research on business value realization
originates from the debate on IS business value. The
RBV perspective is also frequently applied in IS
business value models that refer to assets, resources, and
capabilities to explain IS business value realization
(Schryen, 2013). To grasp the multidimensionality of IS
business value, Schryen (2013) proposes a taxonomy
that classifies its manifestations along their tangibility
and their locus of value realization (p. 151).
The first criterion distinguishes intangible and
tangible business values. Performance-related business
values depict a popular example of tangible business
values as they are measurable. In contrast, intangible
business values like an improved organizational agility
can be hardly quantified but can be interpreted
qualitatively.
The differentiation between internal and external
business values depicts the second criterion of the
taxonomy. Internal business values discuss beneficial
effects within an organization, for instance an improved
decision-making. External business values comprise
market- and competition-oriented business values,
taking the environment of an organization into account.
Similarly, research on the effects of data-driven
initiatives has illustrated the multidimensionality of the
DDBV concept by identifying values like competitive
advantage (Brynjolfsson et al., 2011) or operational
performance (Akter et al., 2016). We conclude that the
DDBV concept has several forms of manifestation,
allowing for an application of the IS business value
perspective in our research context.
Accordingly, we define data-driven business value
as the multidimensional beneficial effects resulting from
the establishment and utilization of data-driven
resources and capabilities, which are observable within
an organization and in interaction with its competitive
environment.
2.3 Related work
So far, research has mostly focused on examining
particular business values resulting from BDA usage,
among them competitive advantages, decision-making
support, or innovation (Duan & Cao, 2015; Vitari &
Raguseo, 2020). Accordingly, findings regarding the
effects of BDA usage are scattered across the literature.
There also exist first steps to synthesize these
findings and to delineate the DDBV concept (Elia et al.,
2020; Vitari & Raguseo, 2020). Elia et al. (2020) present
a BDA value creation model with a set of business
values identified from management and computer
science literature. While the model contributes to a
better understanding of the multidimensional nature of
the DDBV concept, the paper does not provide detailed
explanations of specific BDA business values.
In addition, values such as “hiring of big data
experts” or “use of scalable open source technologies”
(Elia et al. 2020, p. 622) rather seem to characterize
prerequisites for value realization than actual outcomes
of BDA application. The presented systematization of
BD business values hence still seems to lack a detailed,
coherent perspective. Another shortcoming concerns the
omission of the IS literature despite its importance with
respect to research on BDA. In addition, the model only
covers findings published from 2013 to 2017, thus
calling for an update.
While previous research made first steps towards
systemizing DDBVs, the approaches lack conceptual
clarity and a holistic overview. With our study, we aim
at complementing these approaches by providing a new
perspective on the multifaceted nature of DDBVs with
a particular focus on findings from IS research. Through
the development of a categorization of DDBVs, we aim
at delineating the DDBV concept and defining it more
precisely. We also identify concrete avenues for future
research to better understand the concept.
3. Research methodology
To identify and systematize DDBVs, we conducted
a systematic literature review that follows the procedure
proposed by vom Brocke et al. (2009). As we intend to
delineate the term DDBV, we screened the extant body
of knowledge to identify benefits of BDA usage before
classifying them into a coherent concept according to
the IS business value taxonomy of Schryen (2013).
For the first step of defining the review type, we
built upon the framework of Cooper (1988). Our goal
was to integrate extant research outcomes on DDBVs
covered in the IS domain in a conceptual manner,
thereby taking a neutral perspective with the goal to
inform practitioners and scholars (Cooper, 1988).
In a second step, we determined key concepts for
our research endeavor. We emphasized BD as well as
BDA and “data-driven” as key terms for the data-related
domain. We complemented these terms with different
notions of business value, such as benefits, advantages,
and other termini. Based on these terms, we created the
following search string:
(("BDA" OR "Big Data Analytics" OR "data analytics" OR
"advanced analytics" OR "data driven") AND (value* OR “business
value” OR benefi* OR advantage* OR perform* OR achiev* OR
increase* OR success* OR profit* OR accept* OR adoption*))
The third step entailed the search for publications.
Since we adopted an IS business value-oriented stance,
we only searched for publications within the IS domain.
Hence, we browsed the AIS Electronic Library using
our search string. Additionally, we employed the Web
of Science search engine to select relevant publications
from the Senior Scholars Basket of IS Journals. As a
result, we identified 459 initial hits. In our selection
funnel, we defined distinct inclusion and exclusion
criteria to establish a collection of relevant articles. Only
articles that discuss DDBV facets were included in the
final sample. Furthermore, selected publications that are
not characterized as completed research were excluded.
In addition, we limited the time frame for relevant
publications from 2010 onwards, since the hype around
BD in both industry and academia surfaced at the
beginning of this decade (McAfee et al., 2012; Wiener
et al., 2020). To ensure the selection of high-quality
papers, we focused on leading IS conferences and
journals according to current rankings. Thereby, we
reduced the initial sample to 201 articles. Subsequently,
we performed a title- and abstract-screening to identify
articles that discuss DDBVs in detail. Concludingly, we
analyzed the resulting 75 articles in an independent full-
text screening. After iterative discussions, we defined a
final sample of 37 articles.
In a fourth step, we performed an in-depth analysis
of the 37 articles, aiming at identifying distinct DDBVs.
To extract and systematize the multiple facets of DDBV,
we adhered to a grounded theory approach suggested by
Gioia et al. (2013) to establish a coherent data structure.
Our procedure entails three consecutive steps: data
extraction, aggregation, and synthesis. In the initial step
of data extraction, we scrutinized the articles in our
review sample for statements that explicitly mention a
particular DDBV. We extracted 217 text fragments,
which were then labelled according to the depicted
DDBV in an open coding step. As a result, we identified
34 single DDBVs. Subsequently, we aggregated the 34
DDBVs into 11 2nd order business value themes in an
axial coding step. Thereby, we clustered closely related
DDBVs that emphasize a similar value target of DDBV
realization. For instance, we grouped improved
decision-making and better-informed planning
decisions into the 2nd order theme decision-making
support, since both address a similar aspect of DDBV
realization. In a final step, we classified the 11 2nd order
business values themes according to the IS business
value taxonomy proposed by Schryen (2013), thereby
performing a selective and theory-informed coding. For
example, the 2nd order theme decision-making support
was categorized as an internal and intangible value, as
decision-making embodies an intra-organizational
process and improves an organization’s capability to
make efficient high-quality decisions (Schryen, 2013).
Note that we also found a DDBV called “improved
organizational performance”, which we classified as
internal and tangible business value. While we rather
consider organizational performance to be a business
value theme, several papers do not further disaggregate
this generic business value. We discuss this “blurriness”
of some business values in more detail in chapter 5.1.
To substantiate our understanding of the DDBV
concept, we depict the 34 identified DDBVs according
to the IS business value taxonomy. We also provide
illustrative statements from literature to delineate the
beneficial effects of data-driven initiatives. In addition,
we present an agenda for future research and managerial
practice to further investigate the multifaceted nature of
DDBV, as proposed by vom Brocke et al. (2009).
4. Delineating data-driven business value
A quantitative analysis of the literature sample
shows that most of the identified studies employed a
quantitative methodology (n=25). Seven studies used a
qualitative approach and five built upon mixed methods.
21 articles were published within the last five years, thus
highlighting the recent debate on DDBV realization.
More details of our quantitative analysis can be found
online: doi.org/10.6084/m9.figshare.20894191. In our
qualitative analysis, we classified the identified DDBVs
according to the IS business value taxonomy of Schryen
(2013), taking their tangibility and locus of realization
into account. Table 1 provides a summary of the results.
It contains the individual DDBVs, the overarching value
themes, and depicts corresponding articles that discuss
the respective DDBV. In the following subsections, we
delineate those DDBVs and present illustrative
statements from literature to provide an inside view.
4.1 Tangible and internal DDBVs
During our literature analysis, we identified the
DDBV themes prediction capability, operational
performance, and firm & organization performance as
part of the tangible and internal dimension. In the
prediction capability theme, improved forecasting was
found as a significant benefit for DDOs. By using BDA,
order forecasting can be realized with an extended
timespan (Dremel et al., 2017). Procurement generally
profits from BDA, as procurement plans for upcoming
seasons can be based on sales forecasts, while real-time
sales data can be leveraged to adapt plans for the current
situation (Du et al., 2020). Hence, the accuracy of
predictions was found to be improved by BDA. As
stated by Du et al. (2020), “the shipping accuracy
increased from 60 % to 85 %” (p. 130), indicating better
demand prediction. In a similar vein, BDA was found to
better predict the static stability in air cargo scenarios,
also being “faster than a regular physics engine” (Mazur
et al., 2022, p. 12). This implies that BDA can provide
more accurate predictions faster.
Data-driven initiatives were also found to improve
the internal operational performance of DDOs.
Generally, the usage of BDA has a potential to increase
an organization’s process performance. Various
organizational processes were found to be improved by
BDA, for instance delivery processes (Grover et al.,
2018), palletizing processes in the air cargo domain
(Mazur et al., 2022), and investment processes in the
venture capital sector (Weibl & Hess, 2019). More
concretely, process efficiency was increased
significantly. Efficiency gains especially concern the
temporal dimension of process execution (Eggers et al.,
2021; Weibl & Hess, 2019). As stated by Du et al.
(2020), the application of BDA shortens “the average
production cycle from 9 to 5 days” (p. 130). A reduced
cycle time, indicating higher efficiency, can for instance
be achieved by automating process steps (Gust et al.,
2017; Müller et al., 2018). Similarly, increased
productivity was identified in several cases. Apparently,
BDA as an “information processing capability” (D.
Chen et al., 2015, p. 27) helps to increase a firms asset
productivity. Moreover, several authors noted the
influence of BDA on productivity-based metrics
(Brynjolfsson et al., 2011; Müller et al., 2018). Besides
optimized productivity (D. Chen et al., 2015), BDA also
enables improved resource utilization (Grover et al.,
2018). Particularly, idle times of resources were
reduced, indicating higher exploitation of resource
capacity (Du et al., 2020; Wagner et al., 2015). Seubert
et al. (2020) also describes the potential of BDA to
reduce waste in operating processes as a concrete
DDBV. Compared to human planning, BDA yielded a
waste reduction of up to 55 % (Seubert et al., 2020).
BDA moreover has a potential to improve firm
performance. Scholars frequently noted improved
organizational performance as a consequence of BDA
usage (Cao & Duan, 2015; Du et al., 2020; Kitchens et
al., 2018; Müller et al., 2018). In particular,
informational benefits created through BDA can “lead
to superior firm performance” (Someh & Shanks, 2015).
4.2 Tangible and external DDBVs
Tangible and external DDBVs can be divided into
strategic & competitive performance, financial
performance, and customer benefits. Several authors
describe a higher strength of the competitive position of
the firm. Here, the most prominent DDBV manifests as
increased competitive advantages. Generally, “an
organization’s ability to process data […] and utilize
insights […] can enhance business competitiveness”
(Cao & Duan, 2014, p. 12). Due to enhanced innovation
capabilities enabled by BDA, organizations can achieve
Dimension
Value Theme
Data-driven Business Value (DDBV)
Authors
∑
Tangible / Internal
Prediction Capability
Improved Forecasting
[12, 13, 18, 24, 27, 28]
6
Improved Accuracy
[13, 25]
2
Operational
Performance
Increased Process Efficiency
[13, 15, 18, 25, 26, 27, 32, 34]
8
Increased Productivity
[5, 9, 26]
3
Improved Resource Utilization
[13, 17, 18, 31, 33]
5
Reduced Waste
[29]
1
Increased Process Performance
[2, 15, 17, 18, 25, 27, 28, 31, 34, 37]
10
Firm & Organization
Performance
Improved Organizational Performance
[6, 13, 22, 26, 32]
5
Tangible / External
Strategic &
Competitive
Performance
Increased Competitive Advantage
[5, 7, 11, 14, 21]
5
Increased Market Value
[5]
1
Improved Market Perception
[17]
1
Improved Inter-Organizational Collaboration
[13, 27, 34]
3
Improved Supply Chain Performance
[10]
1
Financial Performance
Realization of Cost Savings
[17, 18, 27, 29, 31, 34]
6
Increased Revenue
[4, 13, 17, 19, 22, 33]
6
Increased Return on Investment
[34]
1
Customer Benefits
Improved Customer Experience
[1, 17]
2
Improved Customer Treatment
[17, 27]
2
Improved Customer Satisfaction
[2, 13, 17]
3
Improved Fraud Prevention
[17]
1
Intangible / Internal
Decision-Making
Support
Generally Improved Decision-Making
[6, 8, 12, 13, 16, 18, 19, 24, 31, 32]
10
Improved Decision Quality
[16, 18]
2
Improved Decision-Making Efficiency
[16]
1
Better-Informed Planning Decisions
[13, 18, 23, 25]
4
Knowledge Generation
& Usage
Improved Generation of Business Insights
[3, 12, 15, 17, 23, 32, 37]
7
Increased Process Transparency & Awareness
[15, 18]
2
Organizational
Flexibility &
Adaptability
Increased Organizational Agility
[20]
1
Reduced Time to Market
[32]
1
Increased Business Growth
[9]
1
Intangible /
External
Customer Approach
Improved Customer Acquisition
[1, 4, 12, 13, 17, 23, 31, 36]
8
Improved Customer Retention
[17, 31, 34]
3
Service &
Innovation Capability
Increased Innovation Capability
[3, 14, 30, 35]
4
Increased Potential for new data-driven Services
[12, 26, 34]
3
Continuous Service Optimization
[12, 15]
2
[1] Abhari et al. (2021)
[13] Du et al. (2020)
[25] Mazur et al. (2022)
[2] Adjerid et al. (2018)
[14] Duan and Cao (2015)
[26] Müller et al. (2018)
[3] Alexander and Lyytinen (2019)
[15] Eggers et al. (2021)
[27] Oesterreich et al. (2020)
[4] Bragge et al. (2013)
[16] Ghasemaghaei et al. (2018)
[28] Papapanagiotou et al. (2021)
[5] Brynjolfsson et al. (2011)
[17] Grover et al. (2018)
[29] Seubert et al. (2020)
[6] Cao and Duan (2015)
[18] Gust et al. (2017)
[30] Shuradze and Wagner (2018)
[7] Cao and Duan (2014)
[19] He et al. (2021)
[31] Sodenkamp et al. (2015)
[8] D. Chen et al. (2021)
[20] Hyun et al. (2020)
[32] Someh and Shanks (2015)
[9] D. Chen et al. (2015)
[21] Kamioka et al. (2017)
[33] Wagner et al. (2015)
[10] W. Chen et al. (2020)
[22] Kitchens et al. (2018)
[34] Weibl and Hess (2019)
[11] Danielsen et al. (2021)
[23] Kucklick et al. (2020)
[35] Wölfl et al. (2017)
[12] Dremel et al. (2017)
[24] Lash and Zhao (2016)
[36] Yao et al. (2012)
[37] Zhang et al. (2014)
Table 1. A classification of data-driven business values along the taxonomy of Schryen (2013)
further competitive advantages (Duan & Cao, 2015).
Also, firms that adopt BDA and thereby increase their
level of IT capital show tendencies of higher market
value. This holds especially true for the application of
BDA for organizational decision-making (Brynjolfsson
et al., 2011). Similarly, applying BDA as an innovative
technical artifact has been found to lead to reputational
benefits, indicating an improved market perception.
This can be particularly attributed to “being on the
forefront of BDA” (Grover et al., 2018, p. 407).
Concerning the position of a firm within a business
sector, BDA use helps to improve inter-organizational
collaboration. For instance, the adoption of BDA in the
supply chain of an organization supported “increased
commitment from manufacturing and retail partners” (p.
130) in the case of Du et al. (2020). In this vein, cross-
organizational data platforms also contribute to an
increased data exchange (Weibl & Hess, 2019).
Concerning the collaboration of industry partners, W.
Chen et al. (2020) also report an improved supply chain
performance, as “BDA would help organizations better
implement […] agile supply chain strategies to improve
supply chain performance” (W. Chen et al., 2020, p. 13).
BDA use can furthermore improve the financial
performance. Here, BDA can support the realization of
cost savings, i.e. operational costs (Someh & Shanks,
2015; Weibl & Hess, 2019). In the case of UPS, BDA
helped to “reduce fuel consumption, […] and
maintenance costs” (Grover et al., 2018, p. 408).
Besides cost savings, increased revenue can be realized
through BDA usage (Grover et al., 2018; Kitchens et al.,
2018). As sales figures increased due to optimized data-
driven services (Bragge et al., 2013; Wagner et al.,
2015), significant increases in the resulting revenue
were observed (Du et al., 2020). Similarly, investments
augmented by data-driven insights have proven to lead
to a higher return on investment (Weibl & Hess, 2019).
Another DDBV theme that affects the external
organizational environment concerns benefits for the
customers. As a first business value, the application of
BDA has shown to generally improve the customer
experience (Grover et al., 2018). In addition, the
adoption of BDA supports the establishment of
increased overall customer equity for the firm (Abhari
et al., 2021). The improved customer experience can be
achieved by granting the employees “more autonomy to
participate in customer experience co-creating and co-
delivery in the way they prefer” (Abhari et al., 2021,
p. 7). Likewise, BDA use improves customer treatment,
especially in the healthcare sector. For instance, BDA
supports “effective and personalized treatment
decisions” (Grover et al., 2018, p. 406), leading to
“better and individualized therapies” (Oesterreich et al.,
2020, p. 12). This causes a higher satisfaction of the
customers, i.e., increasing patient satisfaction in
individualized therapies through data-driven insights
(Adjerid et al., 2018; Grover et al., 2018; Oesterreich et
al., 2020). This holds also true for improved customer
satisfaction in a business context (Du et al., 2020;
Grover et al., 2018). Adding to the customer
satisfaction, fraud prevention depicts another DDBV,
since BDA supports the detection of abnormalities,
thereby reducing fraud probability (Grover et al., 2018).
4.3 Intangible and internal DDBVs
Next, we assess DDBVs in the intangible and
internal value dimension. As overarching themes, we
identified decision-making support, knowledge
generation & usage and organizational flexibility &
adaptability. A central purpose of data-driven initiatives
depicts an improved decision-making support. This
particularly addresses organizational decision-making
in general. Typically, BDA enhances an organization’s
decision-making capability (D. Chen et al., 2021; He et
al., 2021) by “augmenting decision makers rather than
replacing them” (Du et al., 2020, p. 133). In addition,
using multiple data sources also makes business
decisions more robust (Gust et al., 2017). Taking a
closer look at decision-making, both decision quality as
well as decision-making efficiency can be enhanced by
using BDA. As such, Ghasemaghaei et al. (2018) report
that “employing big data […] increases the quality of
[…] decisions” (p. 10), thereby improving the overall
accuracy (Gust et al., 2017). Furthermore, decision-
making can be performed more efficiently with the help
of BDA (Ghasemaghaei et al., 2018). As another facet,
BDA helps firms make better-informed planning
decisions. Based on BDA-driven insights, organizations
can improve their capacity and resource planning
(Kucklick et al., 2020). In comparison to human
planning, recommendations based on BDA yield better
results than human judgement (Du et al., 2020).
Another prominent business value theme depicting
intangible and internal DDBVs concerns knowledge
generation and usage. Several authors highlight the
ability of BDA to improve the generation of business
insights. As such, BDA helps organizations “to gain
insights into their business, customers and markets”
(Someh & Shanks, 2015, p. 13). In this vein, extracting
insights from large data streams (e.g., social media)
“[enhances an] organizations management practice”
(Zhang et al., 2014, p. 16). Since new data-driven
insights foster an increased transparency, BDA also
provides benefits on the process level. In this regard,
BDA usage enables increased process transparency and
awareness. For instance, using process mining as a data-
driven technology helps organizations create process
transparency (Eggers et al., 2021), thereby “achieving
alignment of processes across departments” (p. 502).
As a last theme, BDA can help firms improve their
flexibility and adaptability. While depicting a small
theme, the DDBVs within this theme illustrate the
potential for organizations to constantly evolve even in
volatile environments. In this vein, BDA adoption was
found to stimulate the agility of an organization (Hyun
et al., 2020). As with agile methodologies, the agility
from BDA insights can lead to a reduced time to market
(Someh & Shanks, 2015). As a further benefit,
organizations can leverage BDA to stimulate their
business growth, particularly “to an even greater degree
in dynamic environments” (D. Chen et al., 2015, p. 28).
4.4 Intangible and external DDBVs
Lastly, we identified intangible and external
DDBVs. The DDBV themes herein include customer
approach and service & innovation capability.
Using BDA enables firms to take a more targeted
customer approach. This particularly concerns
customer acquisition, as BDA helps firms to address
their target groups and “thus [makes] marketing
activities more effective and efficient” (Dremel et al.,
2017, p. 87). Moreover, BDA supports the identification
of customer purchase patterns (Grover et al., 2018),
enabling efficient marketing campaigns (Sodenkamp et
al., 2015; Yao et al., 2012). BDA also allows firms to
monitor the customer and optimize the customer journey
(Bragge et al., 2013; Du et al., 2020), while identifying
“reasons for customer attrition” (Grover et al., 2018,
p. 408). BDA similarly facilitates customer retention by
improving the quality of customer “interactions […] and
discover[ing] customer opportunities and problems”
(Grover et al., 2018, p. 408). In this vein, BDA-driven
insights allow to “increase customer retention [and]
strengthen customer engagement” (Sodenkamp et al.,
2015, p. 14). Existing services optimized through BDA
can also provide ways of binding the customer “more
closely to the company” (Wagner et al., 2015, p. 13).
As another interlinked business value theme, BDA
allows firms to achieve a superior service & innovation
capability. Here, increased innovation capability
manifests itself through “enhanced new product novelty
and meaningfulness” (Duan & Cao, 2015, p. 12). In
addition, the use of BDA intensifies “radical as well as
incremental innovations” (Shuradze & Wagner, 2018,
p. 4230). As a result, adopting BDA “can payoff in
terms of higher innovation performance” (Wölfl et al.,
2017, p. 9). In addition, BDA offers potential for
generating new data-driven services (Dremel et al.,
2017), realizable by designing “products and services
that offer superior value to the customer and are distinct
from competition” (Müller et al., 2018, p. 504). In a
similar vein, data-driven services (Dremel et al., 2017)
and personal services, particularly in the healthcare
industry (Oesterreich et al., 2020), can be enhanced
through continuous service optimization based on BDA.
5. Discussion and Implications
5.1 Delineating a research agenda for DDBVs
During our analysis, we identified a variety of
DDBVs that demonstrates the multifaceted nature of the
concept. As one remarkable result, the use of BDA not
only generates DDBVs within the firm, but also proves
beneficial in the competitive environment and in the
interaction with the customer. Our study, however, also
reveals deficiencies in BDA research. In the following,
we discuss three research deficiencies (RD) of extant
BDA literature to guide future research that aims at
better understanding DDBV as a concept.
RD1: Missing operationalization of DDBVs
A first shortcoming of BDA research concerns the
blurriness of the DDBV concept. Despite a variety of
researchers that emphasize the potential of BDA to
realize DDBV, the operationalization of the beneficial
effects frequently remains rather coarse and on a high
level of abstraction. Our systematic review accentuates
the need to further disaggregate certain DDBVs. This is
particularly apparent in the case of process performance
(Adjerid et al., 2018), organizational performance
(Someh & Shanks, 2015), competitive advantage
(Kamioka et al., 2017), and the generation of business
insights (Kucklick et al., 2020). While it has been found
that the usage of BDA may lead to the realization of
such business values in general, it is left somewhat
unclear which concrete values can be achieved. In
contrast, other DDBVs were conceptualized in greater
detail. As an example, Ghasemaghaei et al. (2018)
delved deeper into the beneficial effects on decision-
making, stating that BDA use leads to an improved
decision-making performance and discussed improved
decision quality and decision-making efficiency as
concrete values. Further publications depict DDBVs in
a more graspable way. These include - among others -
increased revenue (Du et al., 2020; Wagner et al., 2015),
reduced waste (Seubert et al., 2020), and cost savings
(Gust et al., 2017). Based on this observation, we
encourage future research to analyze DDBVs on a more
detailed level.
RD2: Lacking explanatory mechanisms of
DDBV realization
A second relevant aspect concerns the mechanisms
that lead to a distinct DDBV. While the RBV depicts a
frequently employed concept to theoretically explain
business value realization mechanisms (Mikalef et al.,
2018), other theoretical approaches to delineate DDBV
realization are needed for further clarification. One
possibility to further sharpen the understanding of
DDBV realization is to consider extant research in the
business intelligence (BI) domain (Shiau et al., 2022).
Several articles have developed theoretical explanations
for BI business value realization, for instance through
the lens of absorptive capacity or the general systems
theory (Trieu, 2017). As a consequence, research about
DDBV realization could be informed by extant findings
and perspectives from the BI domain (Marjanovic et al.,
2022; Shiau et al., 2022).
Another avenue for future research is to examine
the interplay between distinct DDBVs and its potential
to facilitate DDBV realization. Several studies indicate
relationships among DDBVs. For instance, Gust et al.
(2017) mention that an improved forecasting results in
the realization of cost savings. Likewise, Wagner et al.
(2015) depict the optimization of resource utilization
through BDA resulting in reduced idle time, which in
turn led to an increased revenue. DDBV realization
should hence not only be attributed to distinct data-
driven resources, but future research should also
examine the interplay of several DDBVs, drawing a
more holistic picture of DDBV realization processes.
RD3: Missing in-depth insights into DDBVs
Although the field is still nascent, our analysis
shows that a majority (n=25) of articles employs a
quantitative stance to examine the potential of BDA for
DDBV realization. Yet, qualitative studies revealed
more accurate explanations of DDBVs and provided a
larger bandwidth of DDBVs (Dremel et al., 2017; Du et
al., 2020; Gust et al., 2017). This RD is interwoven with
the two preceding RDs, as missing insights into
successful data-driven initiatives and the lack of
explanations of value realization mechanisms may stem
from rather high-level approaches found in a
considerable number of quantitative studies. While
quantitative studies allow to confirm relationships
between BDA usage and beneficial effects, qualitatively
uncovering the breadth of DDBVs appears more
promising. We therefore encourage future research to
use qualitative approaches to derive more
comprehensive explanations of DDBV realization,
particularly producing insights on the interplay between
DDBVs. Once a more holistic perspective is established,
quantitative studies may complement this perspective to
confirm the identified relationships.
5.2 Theoretical and practical implications
Our results have implications for academia and
practice alike. As regards academia, we provide an
updated perspective on DDBV realization forms and
established a coherent systematization of the concept. In
doing so, we complement previous systematizations
(Elia et al., 2020) by delving deeper into the bandwidth
of possible DDBVs and the multidimensionality of the
concept. Thereby, we also shed light on previously
overlooked DDBVs like reduced waste (Seubert et al.,
2020) and increased organizational agility (Hyun et al.,
2020). Moreover, we provide characteristic quotes from
literature to describe each DDBV in detail. The variety
of newly identified DDBVs indicates that the business
value realization potential in data-driven initiatives yet
needs to be fully uncovered. We also identified several
intangible business values, thereby extending the so far
performance-oriented business value discussion in the
BDA domain (Vitari & Raguseo, 2020). This extension
is in analogy to Marjanovic et al. (2022), who propose
to re-think the current business value understanding in
the BI domain, especially with respect to the
consideration of more intangible business values. To
further investigate and understand DDBV as a concept,
we moreover provide concrete directions for future
research and particularly call for qualitative studies to
obtain more in-depth insights.
For practice, the presented classification of DDBVs
delivers an instrument to plan and evaluate data-driven
capabilities in a goal-oriented approach. Organizations
ought to define a vision that accentuates their key
objectives and the DDBVs that should be realized when
implementing BDA. Our classification can guide
organizations to decide whether to pursue internal
DDBVs, for instance to optimize operational efficiency,
or to focus on strengthening its competitiveness and
position in the market. Additionally, our findings can
help DDOs to identify areas where DDBVs are not yet
realized, allowing them to take appropriate actions to
further unfold their data-driven value realization
potential and thus serving as a means of benchmarking.
5.3 Limitations
Our findings are not without limitations. While we
assessed articles individually to avoid a subjective bias,
a residual probability of having excluded potentially
relevant articles remains. Nonetheless, we deem the
sample to be representative for the IS research domain.
A second limitation also relates to the representativeness
of the sample. Since we only focused on publications
from the IS research domain, recent articles from related
fields (e.g., computer science) are not included in our
sample. Given the breadth of facets in our article,
however, we believe that the identified DDBVs still
depict the quintessence of DDBVs in BDA research.
Future research may complement our findings by
updating the perspectives from other domains, thereby
further extending the DDBV concept. Lastly, we solely
adopted a positive perspective on BDA to identify the
benefits of BDA application. Potential downsides like
privacy invasion (Sodenkamp et al., 2015) were out of
scope, thus potentially portraying a deceptive picture.
Comparing the dark side of BDA with the identified
DDBVs presents itself as an avenue for future research
that could lead to a more balanced conception of BDA.
6. Concluding remarks
While the potential of BDA as the new oil for firms
(Wiener et al., 2020) is widely acknowledged, the
concretely achievable DDBVs still remain somewhat
unclear. The results of our literature study contribute to
the closure of this literature gap. Our systematization of
34 DDBVs provides a unique overview and shows the
breadth of potentially achievable business values.
Thereby, we extend previous understandings by also
taking intangible business values like organizational
agility into consideration. Although the knowledge base
is still nascent and in-depth insights are often lacking,
we found several indications, which support the claim
that BDA depicts a central strategic asset for firms to
thrive in the future (McAfee et al., 2012).
The identified research deficiencies moreover
provide concrete avenues for future endeavors to further
investigate the value realization mechanisms of BDA.
In so doing, we hope to contribute to the body of
literature that aims at explaining the vast potential to
realize DDBVs with the organizational use of BDA and
intends to close the so-called BDA deployment gap
(Wiener et al., 2020).
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