Towards a taxonomy for business capabilities determining data value

Abstract

Data and its valuation become increasingly crucial for enterprises and academia, which coincides a multitude of data valuation approaches including numerous affected focus areas, dimensions, and characteristics. Therefore, this paper analyzes different approaches to determine data value from a business capability perspective according to the TOGAF standard. Specifically, this paper deals with (a) the development of a taxonomy for data valuation business capabilities (DVBC) as well as (b) the taxonomy validation by the use of existing data valuation approaches. The applied methodologies are taxonomy development techniques for information systems, which are based on a previously executed systematic literature review with a sample size of 67 articles. Further, the data valuation business capability taxonomy is validated through applying two recent data valuation approaches from academia. As a result, the taxonomy developed consists of four business capability layers, nine dimensions, and 36 characteristics. The characteristics are of exclusive or non-exclusive nature, depending on their meaningfulness, and are validated by the successful application of two data valuation approaches. Compiled findings meet both objective and subjective quality standards. With the developed DVBC taxonomy, scholars and professionals are equipped with a tool to classify and structure their data valuation endeavors. In addition, the DVBC taxonomy bridges the domains of information systems, enterprise architecture management, and data management to serve as a foundation for interdisciplinary value generation with and through data.

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Towards a taxonomy for business capabilities determining data
value
Markus Hafner (  markus.hafner@tecnico.ulisboa.pt )
Instituto Superior Técnico
Miguel Mira da Silva
Instituto Superior Técnico
Research Article
Keywords: Data value, data valuation, business capability, taxonomy
Posted Date: February 22nd, 2023
DOI: https://doi.org/10.21203/rs.3.rs-2609006/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License

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Abstract
Data and its valuation become increasingly crucial for enterprises and academia, which coincides a multitude of data valuation
approaches including numerous affected focus areas, dimensions, and characteristics. Therefore, this paper analyzes different
approaches to determine data value from a business capability perspective according to the TOGAF standard. Specically, this paper
deals with (a) the development of a taxonomy for data valuation business capabilities (DVBC) as well as (b) the taxonomy validation
by the use of existing data valuation approaches. The applied methodologies are taxonomy development techniques for information
systems, which are based on a previously executed systematic literature review with a sample size of 67 articles. Further, the data
valuation business capability taxonomy is validated through applying two recent data valuation approaches from academia. As a
result, the taxonomy developed consists of four business capability layers, nine dimensions, and 36 characteristics. The
characteristics are of exclusive or non-exclusive nature, depending on their meaningfulness, and are validated by the successful
application of two data valuation approaches. Compiled ndings meet both objective and subjective quality standards. With the
developed DVBC taxonomy, scholars and professionals are equipped with a tool to classify and structure their data valuation
endeavors. In addition, the DVBC taxonomy bridges the domains of information systems, enterprise architecture management, and
data management to serve as a foundation for interdisciplinary value generation with and through data.
1 Introduction
Generating value from and with data is not just a short-term hype, but a paradigm that has become rmly established in a wide range
of industries and enterprises [1–7]. For this, it is particularly relevant to determine the value of data, the associated data initiatives, and
data-driven use cases in a systematic and standardized manner [2, 8, 9].
The current solutions for determining the data value in science and practice are scattered over a broad range with a wide variety of
content depth. While several enterprises do not determine their data value at all [2], others use estimation techniques based on
knowledge and gut feeling of subject matter experts [10]. Furthermore, a previous and currently submitted literature review unveiled,
that there are data valuation concepts from a multitude of research areas, especially computer science, decision science, as well as
business, management, and accounting. It is noticeable that these data valuation approaches pursue different if not opposing targets
and often occur encapsulated from each other. Consequently, it is challenging for enterprises to identify, classify, and relate data
valuation approaches as well as to implement them in their ecosphere [11–15].
In order to streamline the ideas, concepts, and approaches regarding data value determination and make them applicable to
enterprises, this paper denes data valuation as a holistic business capability. This not only includes the pure determination of the
data value, but also expands the focus to include processes, people, resources, and information [16–18].Furthermore, we hypothesize
that it is benecial for professionals and scholars to obtain a classication and differentiation key for data valuation business
capabilities (DVBC) [19, 20]. Consequently, within the scope of this paper, a DVBC taxonomy is developed, which supports practitioners
in analyzing and understanding the complex topic of data valuation and deriving conclusions. For developing the DVBC taxonomy,
following research question serves as compass:
RQ: What are the main dimensions and characteristics of a data valuation business capability?
For the purpose of contributing to the creation of value from and with data as well as closing the existing research gap, we answer the
research question by describing the scientic background in Section 2. In particular, section 2 discusses the concepts of business
capabilities and data value including their relationship to one another and describes related taxonomies. Section 3 elaborates on the
systematic literature review (SLR) as foundation for the subsequent taxonomy development process tailored to information systems
according to [19]. The nal DVBC taxonomy is presented in Section 4 before being tested in Section 5 using two data valuation
approaches provided by academia. In Section 6 the ndings are discussed before Section 7 completes with a conclusion.
2 Research Background
This section elaborates on the concepts of business capabilities and data value. Furthermore, adjacent and related taxonomies are
described.
2.1 Business capabilities

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The denition of a business capability is not consistently expressed in academia. A literature analysis that has been carried out by [18]
underlines this conclusion and therefore bundles the various denitions of a business capability as follows: “A particular ability that a
business may possess or exchange to achieve a specic corporate goal.” [18].
Since the above-mentioned denition is rather generic, we use the more detailed and practical denition of a business capability
according to
TOGAF
.
TOGAF
is considered as scientically sound [18, 21, 22] and is practically in use [23], which is crucial for the
applicability of a business capability taxonomy in practice. According to
TOGAF
, a business capability consists of the components
information, processes, roles
, and
resources
.
Information
, meaning the knowledge associated with a business capability [16, 17], is
crucial to perform various
processes
[16, 24] within a business capability. These processes may be executed by people [17] associated
with
roles
[16] using tangible and intangible
resources
[16, 24].
2.2 Data valuation business capability
In order to understand what is meant by a data valuation business capability, the delimitation of the term
data value
serves as
foundation. Data and the associated information imply a value in diverse shades that can be generated through the implementation of
data-driven use cases and the direct or indirect sale of data [2–5]. This data value can be of social-ecological, economical [25, 26],
functional and/or symbolic nature [10, 26] with the purpose of adding a measurable business value [13]. Thereby, data value is
determined by a multitude of value drivers [27, 28], underlying theories [29–33], as well as frameworks [34, 35].
The contents and purpose of the above-mentioned data value denitions suggests the application of the business capability concept
in order to achieve the purpose of determining the data value. Thus, the idea of bundling isolated approaches from the eld of data
value emerges, including the data value drivers, theories, as well as frameworks and expanding them in the context of a business
capability. The resulting data valuation business capabilities therefore comprise information, processes, roles, and resources (see
Section 2.1).
2.3 Related taxonomies
In academic literature, there have already been attempts to explain the traits of data value and its determination approaches, for
example in the form of taxonomies. These existing concepts have been elaborated especially in the two domains (IT) business value
and data value.
One taxonomy for classifying value catalogues was developed by [36] to intensify the linkage between enterprise performance and IT
investments. Value catalogs are therefore dened as reference lists that determine the economic impact of the utilization of
information technology. In the value catalogue taxonomy, dimensions are introduced that are used as a foundation for the DVBC
taxonomy. Specically, the dimension relating to
methods for quantifying the value
via value catalogues is particularly close to the
DVBC taxonomy. Thereby, methods under certainty and uncertainty encompassing static, dynamic, and qualitative characteristics are
distinguished. However, the development of additional IT business value assessment approaches are noted as open research gaps,
towards the DVBC taxonomy can contribute.
[37] also adapted the concept of business value and concretized it to articial intelligence (AI) use cases. The resulting taxonomy aims
to identify dimensions and characteristics that enable the value contribution of AI use cases at an organizational scope. The
dimensions
source of business value improvement
and
benet to business value
are particularly relevant for the DVBC taxonomy. The
source of business value improvement
classies the effect implied by an AI use case. This effect, either of automation,
transformation, or information nature, can serve as a basis for formulating potential value drivers and theories for determining the
value. Furthermore, the
benet to business value
dimension distinguishes which type of improvement as a result of an AI use case
contributes to the increase in business value. The characteristics of
cost, quality, revenue
, as well as
risk and complianceperformance
can all impact business value. Thus, these characteristics are declared as non-exclusive since several performance improvements can
be covered simultaneously by an AI use case. A similar logic is used by the DVBC taxonomy in Chapter 4.
In addition to taxonomies in the area of business value and information technology, [38] have developed a taxonomy regarding data
value in the context of decision making. Two main dimensions of data value are dened, namely
data quality
and
data utility
, which
are also included as non-exclusive characteristics in the DVBC taxonomy under the dimension
data value driver
. In the decision-
making data value taxonomy, the data value dimensions and their characteristics are subdivided into more ne-grained metrics that
promote the applicability of the taxonomy. The decision-making data value taxonomy thus serves to promote an understanding of the

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scope and limits of data value but does not address the classication of data valuation approaches in the context of business
capabilities in an enterprise architecture. As a further research step, it is proposed to concretize data value assessment frameworks, to
which the DVBC taxonomy also contributes.
3 Research Design
In the following subsections, the methods used for developing the DVBC taxonomy are described. In addition, relevant literature for the
taxonomy design is analyzed.
3.1 Literature review
The rst step of our research design is adhering to [39] recommendations for systematic literature reviews (SLR) combined with the
forward- and backward-search according to [40].
As a basis for our SLR, we have developed a search query with reference to our research question. The search query ("Compan*" OR
"Enterprise*") AND ("Data Pric*" OR "Data Valu*") AND ("Approach*" OR "Architecture*" OR "Capabilit*" OR "Method*" OR "Model*") NOT
("Data Values") combines data valuation approaches in combination with enterprise architectures and capabilities. Furthermore,
related wording and synonyms are covered, while data values, which refer to technical values of data storages, are explicitly excluded.
To consider a complete set of relevant published full-text access literature (journal articles, conference proceedings, and book
chapters) in English language from 2012 onwards, the search query was applied to the databases
ACM Digital Library, AIS eLibrary
Ebsco, Emerald Insight, IEEE Xplore, ScienceDirect, Scopus, Web of Science,
and
Wiley Online Library
.
In total 306 scientic contributions were recognized as raw sample. Out of the raw sample, 133 duplicates were removed. A thorough
abstract reading was used for the ensuing 173 scientic contributions, which led to the deletion of another 140 articles and a sample
size of 33 articles. According to [40], both backward (+24) and forward search (+10) were applied to ll in any potential gaps in the
search query. We were able to expand the size of the nal sample using these techniques from 33 to 67 scientic contributions. Figure
1 illustrates the systematic literature process and related ndings.
3.2 Taxonomy development process
After the rst foundational step, the systematic literature review, the taxonomy development method according to [19] was applied,
which is tailored to the research area information systems. As the research topic data value is addressed by a wide range of domains
(see Chapter 3.1), the iterative approach of the method is particularly well suited to encompass the complexity and interdisciplinarity
of the topic.
[19] propose two taxonomy development approaches. While the conceptual-to-empirical approach is more suitable when few data is
available on the objects to be classied (in this case, DVBC), the empirical-to-conceptual approach is more suitable for a taxonomy
with a broad data foundation. In this paper, a combination of both approaches was used. In the rst iteration, the conceptual-to-
empirical approach was applied in order to establish guiderails for the proper denition of business capabilities. The depth of the
content of a DVBC in relation to dimensions and characteristics was created using the empirical-to-conceptual approach in further
iterations, as many data valuation approaches are available due to the SLR carried out. Figure 2 below shows the applied method.
The rst step in taxonomy development according to [19] is to determine the meta-characteristics of the taxonomy. It is of particular
relevance that the meta-characteristic is aligned with the purpose of the taxonomy (see Chapter 1). Therefore, the meta-characteristic
may be described as the identication of the especially pertinent layers, dimensions, and characteristics of a DVBC.
In addition, the applied methodology in step 2 provides for dening ending conditions of subjective as well as objective nature. In the
context of this paper,
conciseness
(consider a maximum of ve to nine dimensions [19, 41]),
robustness
,
comprehensiveness
,
extendibility
, and
explainability
are regarded as subjective ending conditions.
In order to meet scientic requirements, [19] further specify eight objective ending conditions. These ending conditions have been
condensed into the four applicable ending conditions
completeness
,
granularity
,
uniqueness
, and
stability
to ensure a simplied
understanding of this taxonomy development without signicantly changing their content. Figure 3 lists and explains the ending
conditions and shows the development of the degree of fulllment of the ending conditions after each iteration.

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3.2.1 Conceptual-to-empirical iteration
In the rst iteration in the taxonomy development process, the framework for detailing a DVBC was created by using the conceptual-to-
empirical approach (see steps three to ve in Figure 2). Specically, the framework serves as a logical bracket of the underlying
dimensions as well as characteristics. The technique of determining a framework by using layers has been applied in various
taxonomies already [42–44] and is therefore dened as state of the art.
The dened layers
information
,
resources
,
roles
, and
processes
are based on the components of a business capability according to
TOGAF
(see Section 2.1) [16, 45]. From this the hypothesis is derived, that every DVBC includes the four dimensions of
information
,
resources
,
roles
, and
processes
.
3.2.2 Empirical-to-conceptual iterations
The second iteration is based on the systematic literature review and therefore follows the empirical-to-conceptual approach in the
taxonomy development process. Based on the SLR, we found that the literature on data valuation is examined from a variety of
angles, especially from the research areas
business, management, and accounting
as well as
computer science
according to [46]. The
high granularity level in the literature regarding approaches and concepts to determine the data value was lifted to a higher level of
abstraction and therefore formed the three taxonomy dimensions
data value driver
,
data valuation theory
, as well as
data valuation
tooling
.
In the third iteration, the
information
layer was completed. In this context, the
purpose
dimension is particularly relevant in order to
adequately frame a business capability [16, 18, 24, 47] as well as to serve as a starting point for determining the data value [27]. In
addition, a DVBC requires information on objects, which are going to be classied in the taxonomy. Consequently, the
data valuation
object
dimension was included.
The fourth iteration sought to complete the
roles
layer. The analysis of the SLR ndings illustrates that two main groups particularly
stood out with regard to their frequency of mentions with reference to past and future research. The rst, most frequently and deeply
discussed group includes all stakeholders involved in determining the data value and is therefore summarized under the
value
determination stakeholder
dimension in this taxonomy [29, 48]. The second relevant group is derived particularly from the relevance
for future research and can be subsumed in the dimension
value auditing stakeholder
[48–51].
The fth iteration deepened the functional content and outcomes of the
process
layer of a DVBC. From this the dimension
component
is formedwhich details the content of a DVBC [52]. Further the outcomes are considered in the
result
dimension [35, 49,
53].
In the sixth iteration, all of the ending conditions of a subjective and objective nature (see Figure 3) were met and thus the taxonomy
development process was terminated. The outcome of the last sixth iteration is declared as a nal DVBC taxonomy and is further
described below.
4 Taxonomy: Data Valuation Business Capability
The nal taxonomy for DVBC is described in the following sections. In total the developed taxonomy in Figure 4 has four layers, nine
dimensions and a subset of 36 characteristics.
We learned during the taxonomy development process that it is impossible to limit the choice of characteristics to be mutually
exclusive for some dimensions since important information would be lost. The dimensions of
data value driver
,
data valuation theory,
and
component
in particular imply the most diverse forms of expression per scientic study, per enterprise, and per data valuation
approach. Consequently, non-exclusive characteristics in these dimensions are supported in accordance with other recently published
taxonomies in the research areas of digital transformation and information systems (Berger et al., 2018; Engel et al., 2022; Gelhaar et
al., 2021; Jöhnk et al., 2017; Lis and Otto, 2021). Further, the visualization style of a morphological box is chosen in order to increase
the intuitiveness and usability of the taxonomy [55, 56].
4.1 Layer 1 – Information

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The rst layer of a business capability, and therefore also of this taxonomy, comprises information or so-called knowledge that a
business capability requires and consumes in order to determine a data value [16]. Specically, the information layer encompasses the
three dimensions
purpose
,
data valuation object,
and
data value driver
.
4.1.1 Purpose
The
purpose
dimension characterizes the goal of the data valuation and thus sets the guide rails for the detailing of a DVBC. Two
characteristics and their combination can therefore be exclusively dened.
While
qualitative data valuation
focuses on generating contextual knowledge about the data value [34, 38, 50],
quantitative data
valuation
concentrates on numerical information [34, 35, 57]. The existing literature shows that a
combination
[34] of both
characteristics to different extents is also possible.
4.1.2 Data valuation object
After the question why data valuation should take place (purpose), the dimension
data valuation object
aims on the question whose
value should be determined.
From this, the two exclusive characteristics
bundled information
and
non-bundled information
can be dened. Bundled information
refers to data clusters that can be logically grouped according to their content in each use case. Examples of this are data products,
data assets, and datasets in various forms. In contrast, non-bundled information are isolated raw data points that have not yet
undergone any logical clustering [15, 58–60].
4.1.3 Data value driver
The third dimension
data value driver
poses the question of which parameters affecting the data value are considered for the data
value determination. Since data valuation is an interdisciplinary topic with an arbitrarily high degree of complexity, it is evident that the
data value driver
dimension cannot have exclusive characteristics. Rather, DVBC can consider a variety of data value drivers. At this
point, it is hypothesized that the more data value drivers are considered, the more accurate the determined data value will be. This
hypothesis underlines the non-exclusive nature of the dimension, although the hypothesis needs to be validated in another study.
We distinguish six data value drivers and, to underline the non-exclusive nature of this dimension, add the characteristic
other
. The
other
characteristic includes both proprietary data value drivers and potential additional data value drivers outside the analyzed
literature.
The characteristic
business utility
includes the impact of a data-driven use case on a process or an enterprise [12, 26, 61]. In addition,
the characteristic
cost
may include expenses associated with the data being evaluated, such as data collection, processing, analysis,
and management costs [29, 34, 62–64] as well as opportunity costs [35, 61, 65]. Furthermore, we complement data management
related data value drivers and tailor them into the characteristics
data durability and lifetime
[66, 67],
data quality
[51, 61, 64, 68, 69],
and
data security and privacy
[64, 70–72]. As a nal characteristic, data value drivers are considered which imply a certain subjectivity.
The
sentiment and perception
characteristic includes, for example, the perceived data value and thus the willingness-to-pay [28, 32,
73–75] as well as risks associated with the valuation and monetization of data [76].
4.2 Layer 2 – Resources
The second layer in the DVBC taxonomy considers the associated resources of tangible and intangible types, which are required for
determining the value of data [16]. As an intangible resource, the dimension
data valuation theory
is introduced. Furthermore, the
dimension
data valuation tooling
represents the tangible dimension of the resource layer.
4.2.1 Data valuation theory
Similar to the data value drivers in Section 4.1.3, the dimension
data valuation theory
is dened as a cluster of non-exclusive
characteristics, since DVBC can be based on numerous theories to determine the data value. Here, we distinguish seven data valuation
theories.

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The rst characteristic
economic
comprises all theories that determine the data value based on price-quantity diagrams, cost curves
and conventional hardware-oriented pricing (cost, competition, customer) [29, 32, 57]. In addition,
game theory
can be used as a data
valuation theory, which can be divided into two characteristics,
cooperative
[30, 31] and
non-cooperative game theory
[63, 75]. The
fourth characteristic
decision theory
summarizes approaches, e.g., analytic hierarchy process [51, 67] or fair knapsack [77], that assess
the data value while taking uncertainty and vagueness into account. A more technical data valuation theory deals with the valuation
of database queries of different types and can therefore be subsumed under the term
query-based
[29, 64, 78–80]. Furthermore, the
sixth characteristic
index-based
deals with the indexation of data value drivers for the determination of an indexed data value [62, 67].
In addition to the aforementioned data valuation theories, which represent certain paradigms in data value determination, the seventh
characteristic clusters all
proprietary
theories that have not been considered in the taxonomy dimension so far or are based on expert’s
gut feeling only [10].
4.2.2 Data valuation tooling
To combine and apply data valuation theories and data value drivers, scholars propose different rather personal as well as rather
application-based approaches. Consequently, we dene three exclusive characteristics in the dimension
data valuation tooling
in our
taxonomy.
The rst characteristic in the data valuation tooling dimension is
interpersonal elaboration
. Interpersonal elaboration is a vehicle to
support data value determination used by multiple scholars. The assessment of the value of data and their use cases by domain
experts is particularly relevant for this characteristic [12, 35, 50].
In contrast, there are
models and applications
that support and facilitate the determination of data value. Models and applications can
occur in various forms. More theoretical constructs such as economic cost or price curves are considered, as are ecosystem-oriented
intermediary solutions, for example in the form of data marketplaces [48, 66, 81].
As a third characteristic in the data valuation tooling dimension, a
combination
of interpersonal elaboration as well as models and
applications is introduced, since particularly practical research approaches suggest these combined data valuation approaches [34,
62].
4.3 Layer 3 – Roles
The third layer of the DVBC taxonomy focuses on the roles and responsibilities that stakeholders, individuals, and organizational units
play in order to determine the value of data [16]. In concrete terms, the roles layer consists of two roles dimensions which deal with the
determination of data value (
value determination stakeholder
) on the one hand and its auditing (
value auditing stakeholder
) on the
other.
4.3.1 Value determination stakeholder
The stakeholders included in the determination of data value play a central role in a DVBC, since they affect the ecosystem of an
enterprise to be addressed. Four exclusive characteristics in the form of a continuum are established in this taxonomy, ranging from
the inclusion of purely
internal
stakeholders to the inclusion of purely
external
stakeholders. Mixed forms of internal and external
stakeholders are dened as intermediate characteristics. These mixed forms include direct collaboration
without an intermediary
as
well as
with an intermediary
such as a data broker or data marketplace [29, 48, 76]. Regardless of the internal or external nature of
data valuation stakeholders, such as data providers [29, 48, 82] or data buyers [29, 48, 82], all are relevant to some extend in a variety
of data valuation approaches, which underscores their necessity in this taxonomy.
4.3.2 Value auditing stakeholder
The second dimension of the roles layer containing of three exclusive characteristics is
value auditing stakeholder
. Auditing the data
value is relevant for validating its determination process and result. At this point,
internal data value auditors
or
third-party data value
auditors
can occur in science and practice [48, 50, 51]. Moreover, no audit can be performed at all, leading to the third characteristic
not existing
.
4.4 Layer 4 – Processes

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The fourth layer of the DVBC taxonomy focuses on related processes and patterns in order to accomplish a certain output of a
business capability [16]. To be more precise, in this case
components
and
results
are dened as dimensions under the process layer.
4.4.1 Component
The components dimension describes the main practices of a business capability, which in turn include individual sub-processes,
activities, and functional modules. The results of the executed SLR suggest that four main characteristics of a DVBC can be formed.
The components and thus characteristics in this dimension can also occur in a combined manner. Consequently, the components
dimension including its characteristics is also dened as non-exclusive.
A predominant number of data valuation approaches focus on the data value assessment, i.e., the pure determination of the data
value, which is why the
data value assessment
is recorded as a characteristic in this taxonomy [12, 50, 83]. In addition, there are
approaches that assign the data value to dedicated entities. The resulting characteristic is described as
data value allocation
[52]. Two
further components include the temporal dimension in their scope of functions. While the
data value prediction
characteristic
forecasts a future data value [50, 52], e.g., via customer lifetime value [34], the
data value monitoring
characteristic compares the
planned and actual data value [52, 83].
4.4.2 Result
The results dimension contrasts three exclusive characteristics that describe the outcome of the DVBC. On the one hand, a
relative
data value
can be determined [35], which compares individual data values, data initiatives, and use cases with each other in relative
terms and outputs them, for example, in order of the corresponding value. On the other hand, an absolute data value can be dened as
the result of a data valuation. A distinction can be made between a
specic absolute data value
and an
approximate absolute data
value
. While the specic absolute data value aims at an exact determination of the data value [34, 53], the approximate absolute data
value focuses on a corresponding estimation of the data value, for example, considering uncertainty or the necessary computing
power [79, 84].
5 Application Of The Dvdc Taxonomy
This section assesses the DVBC taxonomy's utility and exemplies its intended usage, as suggested by [19]. In order to test our DVBC
taxonomy, we have selected two contemporary data value determination approaches that follow a more technology-supported
approach by [35] on the one hand and a more ecosystem-supported multilayered approach by [34] on the other hand. Figure 5
illustrates the ndings of the application, which are further described in Sections 5.1 and 5.2.
5.1 Approach 1– Automatic data value analysis method for relational databases
Approach 1 by [35] is an automated, metric-based data value assessment technique that provides a scoring mechanism for
automatically determining the business value of bundled data aka datasets data in relational databases.
To calculate the data value, a multitude of metrics are considered that are represented as data value drivers in the DVBC taxonomy,
which legitimates the non-exclusive denition of its data value driver dimension. In concrete terms, the metrics
utility
,
volume
, and
usage
of approach 1 can be assigned to the taxonomy characteristic
business utility
. The approach of [35] also reects the
characteristic
data quality
by the metrics
timeliness
and
quality
. The
cost
characteristic is embodied by Bendechache et al. via the
metric
replacement costs
. In addition, [35] also reect the characteristic
sentiment and perception
by including the metrics of
legal risk
and
competitive advantage
.
The resource layer of the DVBC taxonomy is characterized by a combination of
interpersonal elaboration
as well as
models and
applications
for determining the data value for the example of approach 1. On the one hand, Bendechache et al. apply a survey-based
questionnaire, which they is validated using a query-based approach and assign corresponding data value indices in the form of a
scoring system to the datasets. Therefore,
internal stakeholders
are required for determining the data value, even though
no data value
audit
takes place.
As a result of the approach 1 data valuation approach, a
relative data value
is issued by the scoring system, which serves solely for
the
data value assessment
.

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5.2 Approach 2– From qualitative to quantitative data valuation
In contrast to approach 1, the data valuation approach of [34] combines both qualitative and quantitative elements. The object of
valuation in approach 2 is bundled data, described as datasets, which are based on use cases. In addition to considering
business
utility
as a data value driver, data attributes in general are considered, which are represented by the characteristics
data durability and
lifetime
,
data quality
, as well as
data security and privacy
in the DVBC taxonomy. Furthermore,
costs
relating to the data value are
considered, which are processed, among other things, in a data valuation theory with economic characteristics. This is the so-called
cost-based
and
transaction-based data valuation
approach. To complete the data valuation framework,
proprietary
theories are
included, such as dening threshold metrics, which increase or decrease the data value to a dedicated percentage.
To enable data value determination, [34] apply both
interpersonal elaboration,
e.g., in the form of an initial segmentation of relevant
use cases, as well as models and applications, e.g., data marketplaces as vehicles for transaction-based data valuation.
The value determination stakeholders can be
internal and external
, considering intermediaries, e.g., data marketplaces. Similar to
approach 1, there is no data value audit in approach 2
As a result of the data valuation framework according to Stein et al., a
specic absolute data value
is to be ascertained, which is used
for
data value assessment
on the one hand and for
data value prediction
on the other, for example in the form of the customer lifetime
value.
6 Discussion
After the completion of the DVBC taxonomy, the raison d'être requires to be challenged regarding quality and content of the taxonomy.
First of all, it must be noted that although data valuation approaches and concepts are described in this literature sample, they are not
yet dened as business capabilities. Rather, this DVBC taxonomy also serves to raise the multitude of data valuation approaches to
the level of abstraction of a business capability. Therefore, it is crucial to generalize ne-grained concepts and to detail generic
concepts.
For this taxonomy the TOGAF standard served as frame construct providing the layers
information, processes, roles
, and
resources.
At
this point, however, it is important to note that other notions for describing a business capability [17, 18] also have their reason for
existence and would possibly result in a different structuring of the taxonomy.
Regardless of the framework construct for describing a business capability, the quality of the DVBC taxonomy based on it must be
discussed. Therefore, scholars have shown that the taxonomy evaluation criteria
(a) usefulness, (b) applicability, (c)
comprehensiveness, (d) robustness, (e) conciseness, (f) extensibility
, and
(g) explanatory
are of particular relevance [85]. As Figure 3
shows, the evaluation criteria (c) – (g) are explicitly fullled for the development of the DVBC taxonomy. Further, evaluation criteria (a)
– (b) are implicitly fullled by the application of the taxonomy to the two approaches in Chapter 5.
In addition, [85] dene six guidelines to ensure the compliance of excellence standards regarding the development of taxonomies.
Guideline 1
(scoping of taxonomy evaluation),
guideline 2
(justication of objective ending conditions),
guideline 3
(justication of
subjective ending conditions) and
guideline 4
(demonstration of DVBC taxonomy applicability) have been successfully carried out on
the basis of this paper. Further,
guideline 5
(evaluation of DVBC taxonomy usefulness) and
guideline 6
(long-term re-evaluation) are
planned for future work.
Consequently, the excellence standards for the content and quality of the developed DVBC taxonomy are considered as given with the
note of future eld testing and long-term validation.
7 Conclusion
Our research is motivated by several contributions that highlight the signicance of data valuation and demand for more in-depth
study in this area [11–15, 38]. Therefore, this paper focuses on the development of a data valuation business capability in order to
provide a concept to classify as well as structure existing and emerging data valuation approaches from the perspective of a business
capability. To achieve this, the taxonomy development method for information systems by [19] is applied, tested against two
contemporary data valuation approaches [34, 35], and the taxonomy is reviewed for its raison d'être in terms of content [86] and

Page 10/16
quality based on the evaluation criteria by [85]. The result is an excellence-assured data valuation business capability taxonomy
consisting of four layers, nine dimensions, and 36 characteristics.
Our research has limitations. One limitation relates to the structure of the taxonomy, which is based on the practice-approved and
scientically sound
TOGAF
standard, knowing that other structuring concepts exist for describing a business capability. Another
limitation of content relates to the subject of the dimensions and characteristics based on the results of a systematic literature review.
It is possible that particular concepts in the literature may not be covered by this taxonomy. Due to the breadth and scope of existing
data valuation approaches and their meaningful integration in this taxonomy, some taxonomy dimensions have been dened as non-
exclusive, which may dilute the strict delineation of data valuation business capabilities to some degree.
With a view to future scientic work, three thematic areas can be formulated. On the one hand, it is recommended to validate the
developed taxonomy in a eld study of long-term nature. In addition, a data value ontology, which follows scientically sound
standards such as
OntoClean
[87] could be developed on the basis of the taxonomy. Furthermore, the concept for dening data
valuation as a business capability should be tested and rened in real-world scenarios following the design science research paradigm
[86].
Declarations
No research funds and cooperations inuencing the research are to be declared. Further, no competing interests are directly or
indirectly related to the paper.
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Figures
Figure 1
Systematic literature review process based on [39, 40]
Figure 2
Taxonomy development method for information systems by [19]applied to this paper

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Figure 3
Degree of fulllment of taxonomy development ending conditions per iteration
Figure 4
Taxonomy for Data Valuation Business Capabilities

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Figure 5
Application of the DVBC taxonomy to data valuation approaches by [34, 35]
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