1 Introduction

Generating value from and with data is not just a short-term hype but a paradigm firmly established in various industries and enterprises [1,2,3,4,5,6,7]. For this emerging paradigm, it is particularly relevant to determine the value of data, the associated data initiatives, and data-driven use cases systematically and standardized [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 many 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, as well as to implement data valuation approaches in their ecosphere [11,12,13,14,15].

This paper defines data valuation as a holistic business capability to streamline the ideas, concepts, and approaches regarding data value determination and make them applicable to enterprises. This not only includes the pure determination of the data value but also expands the focus to include processes, people, resources, and information [16,17,18] in data valuation endeavors. Furthermore, the authors hypothesize that it is beneficial for professionals and scholars to obtain a classification 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 targeted conclusions for their individual data monetization paths. For developing the DVBC taxonomy, the following research question serves as a compass:

RQ

What are the main dimensions and characteristics of a data valuation business capability?

To contribute closing to the creation of value from and with data and close the existing research gap, we answer the research question by describing the scientific background in Sect.  2. In particular, Sect.  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 the foundation for the subsequent taxonomy development process tailored to information systems, according to [19]. The final DVBC taxonomy is presented in Sect. 4 before being tested in Sect. 0 using two data valuation approaches provided by academia. In Sect. 6, the findings are discussed before Sect. 7 completes with a conclusion.

2 Research background

This section elaborates on the concepts of business capabilities and data value. Furthermore, adjacent taxonomies are described.

2.1 Business capabilities

The definition of a business capability is not consistently expressed in academia. A literature analysis carried out by [18] underlines this conclusion and therefore bundles the various definitions of a business capability as follows: “A particular ability that a business may possess or exchange to achieve a specific corporate goal” [18].

Since the above definition is rather generic, we use the more detailed and practical definition of a business capability according to the TOGAF standard. TOGAF is considered scientifically 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 comprises 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

The delimitation of the term data value serves as the foundation to understand what a data valuation business capability means. Data and the associated information imply a value in diverse shades that can be generated by implementing data-driven use cases or through the direct or indirect sale of data [2,3,4,5]. This data value can be of social-ecological, economical [25, 26], functional, and/or symbolic nature [10, 26] to add a measurable business value [13]. Thereby, data value is determined by a multitude of value drivers [27, 28], underlying theories [29,30,31,32,33], as well as frameworks [34, 35].

The contents and purpose of the above-mentioned data value definitions suggest applying the business capability concept to achieve the purpose of determining the data value. Thus, the idea of bundling isolated approaches from the field of data value emerges, including the data value drivers, theories, as well as frameworks and expanding them in the context of a business capability. Therefore, the resulting data valuation business capabilities comprise information, processes, roles, and resources (see Sect. 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 catalogs was developed by Seufert et al. [36] to intensify the linkage between enterprise performance and IT investments. Value catalogs are therefore defined as reference lists that determine the economic impact of the utilization of information technology. In the value catalog taxonomy, dimensions are introduced as a foundation for the DVBC taxonomy. Specifically, the dimension relating to methods for quantifying the value via value catalogs 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 is noted as an open research gap toward the DVBC taxonomy can contribute.

Engel et al. [37] also adapted the concept of business value and concretized it to artificial 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 of source of business value improvement and benefit to business value are particularly relevant for the DVBC taxonomy. The source of business value improvement classifies the effect implied by an AI use case. This effect, either of automation, transformation, or information nature, can serve as a basis for formulating value drivers and theories for determining the value. Furthermore, the benefit 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 compliance performance can all impact business value. Thus, these characteristics are declared non-exclusive since an AI use case can cover several performance improvements simultaneously. The DVBC taxonomy uses a similar logic.

In addition to taxonomies in the area of business value and information technology, Lega et al. [38] have developed a taxonomy regarding data value in the context of decision-making. Two main dimensions of data value are defined, 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 fine-grained metrics that promote the applicability of the taxonomy. The decision-making data value taxonomy thus promotes an understanding of the scope and limits of data value but does not address the classification 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

The following subsections describe the methods used to develop the DVBC taxonomy. In addition, relevant literature for the taxonomy design is analyzed.

3.1 Literature review

The first step of our research design is adhering to [39] recommendations for SLRs combined with the forward- and backward search, according to [40].

As a basis for our SLR, the authors developed a search query concerning 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 with enterprise architectures and capabilities. Furthermore, related synonyms are covered, while data values, which refer to technical values of storages, are explicitly excluded.

To consider a complete set of relevant published full-text access literature (journal articles, conference proceedings) in the 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. Figure 1 illustrates the SLR and related findings.

Fig. 1
figure 1

Systematic literature review process based on [39, 40]

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In total, 304 scientific contributions were recognized as raw sample. Out of the raw sample, 133 duplicates were removed. An abstract reading was used for the ensuing 171 scientific contributions, which led to the deletion of another 140 articles and a sample size of 31 articles. According to [40], both backward (+ 23) and forward search (+ 10) were applied to fill in potential gaps in the search query. Thus, the authors expanded the size of the final sample from 33 to 64 scientific contributions.

3.2 Taxonomy development process

After the first 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 a wide range of domains addresses the research topic data value, the iterative approach of the method is particularly well suited to encompass the complexity and interdisciplinarity of the topic.

Nickerson et al. [19] propose two taxonomy development approaches. While the conceptual-to-empirical approach is more suitable when little data is available on the objects to be classified (in this case, DVBC), the empirical-to-conceptual (E2C) approach is more ideal for a taxonomy with a broad data foundation. In this paper, a combination of both approaches was used. In the first iteration (iter.), the conceptual-to-empirical (C2E) approach was applied to establish guiderails for the proper definition 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.

Fig. 2
figure 2

Taxonomy development method for information systems by [19] applied to this paper

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The first 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 Sect. 1). Therefore, the meta-characteristic may be described as the identification of the especially pertinent layers, dimensions, and characteristics of a DVBC. In addition, the applied methodology in step 2 defines ending conditions of subjective and objective nature, as illustrated in Table 1.

Table 1 Degree of fulfillment of taxonomy development ending conditions per iteration
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In the context of this paper, conciseness (consider a maximum of five to nine dimensions [19, 41]), robustness, comprehensiveness, extendibility, and explainability are subjective ending conditions. To meet scientific 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 simplified understanding of this taxonomy development without significantly changing their content. lists and explains the ending conditions and shows the development of the degree of fulfillment of the ending conditions after each iteration.

3.2.1 Conceptual-to-empirical iteration

In the first iteration of the taxonomy development process, the framework for detailing a DVBC was created using the conceptual-to-empirical approach (see steps three to five in Fig. 2). Specifically, the framework serves as a logical bracket of the underlying dimensions as well as characteristics. The technique of determining a framework using layers has already been applied in various taxonomies [42,43,44] and is therefore defined as state-of-the-art.

The defined layers of information, resources, roles, and processes are based on the components of a business capability according to TOGAF (see Sect. 2.1) [16, 45]. From this, the hypothesis is derived: 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 SLR 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 various angles, especially from the research areas of 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 to adequately frame a business capability [16, 18, 24, 47] as well as to serve as a starting point for data valuation [27]. In addition, a DVBC requires information on objects, which will be classified 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 findings illustrates that two main groups particularly stood out about their frequency of mentions concerning past and future research. The first, most frequently, and intensely 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 [48,49,50,51].

The fifth iteration deepened the functional content and outcomes of the process layer of a DVBC. From this, the dimension component is formed, which 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 Table 1) were met, and thus the taxonomy development process was terminated. The outcome of the last sixth iteration is declared as a final DVBC taxonomy and is further described below.

4 Taxonomy: data valuation business capability

The final taxonomy for DVBC is described in the following sections. In total, the developed taxonomy in Table 2 has four layers, nine dimensions, and a subset of 36 characteristics.

Table 2 Taxonomy for data valuation business capabilities (*E, Exclusive; N, Non-Exclusive)
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During the taxonomy development process, the authors learned that it is impossible to limit the choice of characteristics to be mutually exclusive (indicated with letter E) 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 scientific study, per enterprise, and per data valuation approach. Consequently, non-exclusive characteristics (indicated with letter N) in these dimensions are supported in accordance with other recently published taxonomies in the research areas of digital transformation and information systems [37, 44, 78, 79]. Further, the visualization style of a morphological box is chosen to increase the intuitiveness and usability of the taxonomy [80, 81].

4.1 Layer 1—information

The first layer of a business capability, and therefore also of this taxonomy, comprises information or so-called knowledge that a business capability requires and consumes to determine a data value [16]. Specifically, the information layer encompasses 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 defined.

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, 54]. 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 of why data valuation should take place (purpose), the dimension data valuation object aims at the question whose value should be determined.

From this, the two exclusive characteristics of bundled information and non-bundled information can be defined. 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, 55, 56].

4.1.3 Data value driver

The third dimension of 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. Instead, 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, 58]. In addition, the characteristic cost may consist of expenses associated with the data being evaluated, such as data collection, processing, analysis, and management costs [29, 34, 59, 60] as well as opportunity costs [35, 58, 82]. Furthermore, we complement data management related data value drivers and tailor them into the characteristics of data durability and lifetime [61, 62], data quality [51, 58, 60, 63, 64], and data security and privacy [60, 65,66,67]. As a final characteristic, data value drivers are considered, which imply a particular subjectivity. The sentiment and perception characteristic includes, for example, the perceived data value and, thus the willingness-to-pay [28, 32, 57, 68, 69] as well as risks associated with the valuation and monetization of data [70].

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 Sect. 4.1.3, the dimension data valuation theory is defined 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.

The first characteristic of 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, 54]. 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 [57]. The fourth characteristic decision theory summarizes approaches, e.g., analytic hierarchy process [51, 62] or fair knapsack [71], that assess the data value while considering uncertainty and vagueness. 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, 60, 72,73,74]. Furthermore, the sixth characteristic, index-based deals with the indexation of data value drivers for determining an indexed data value [59, 62]. 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 an 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 define three exclusive characteristics in our taxonomy's dimension data valuation tooling.

The first characteristic of 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 data value and their use cases by domain experts is particularly relevant for this characteristic [12, 35, 50].

In contrast, some models and applications support and facilitate data value determination. Models and applications can occur in various forms. More theoretical constructs such as economic cost or price curves are considered, as well as ecosystem-oriented intermediary solutions, such as data marketplaces [48, 61, 75].

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, 59].

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 to determine data value [16]. In concrete terms, the roles layer consists of two role dimensions that 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 an enterprise's ecosystem. 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 strictly external stakeholders. Mixed forms of internal and external stakeholders are defined as intermediate characteristics. These diverse forms include direct collaboration without an intermediary as well as with an intermediary such as a data broker or data marketplace [29, 48, 70]. Regardless of the internal or external nature of data valuation stakeholders, such as data providers [29, 48, 83] or data buyers [29, 48, 83], all are relevant to some extent 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 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, leading to the third characteristic not existing.

4.4 Layer 4—processes

The fourth layer of the DVBC taxonomy focuses on related processes and patterns to accomplish a certain output of a business capability [16]. To be more precise, in this case, components and results are defined as dimensions under the process layer.

4.4.1 Component

The components dimension describes the leading 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 component dimension, including its characteristics, is also defined 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, 76]. In addition, some approaches 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, 76].

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 defined as the result of a data valuation. A distinction can be made between a specific absolute data value and an approximate absolute data value. While the specific 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 [73, 77].

5 Application of the DVBC taxonomy

This section assesses the DVBC taxonomy's utility and exemplifies its intended usage, as suggested by [19]. 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.

Table 3 illustrates the findings of the application, which are further described in Sects. 5.1 and 5.2.

Table 3 Application of the DVBC taxonomy to data valuation approaches (● = [34]; ○ = [35])
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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 definition 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 reflects 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 reflect the characteristic sentiment and perception by including legal risk and competitive advantage metrics.

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 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 to determine the data value, even though no audit takes place.

As a result of the approach 1, a relative data value is issued by the scoring system, which serves solely for the data value assessment.

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 of 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 defining 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 specific 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

In order to better contextualize the DVBC taxonomy within existing literature, the contents of this paper are discussed and categorized both in terms of content and methodology at this juncture. This serves to render the quality and potential limitations of the paper transparent.

6.1 Methodology-related discussion

The methodological approach adheres to established academic standards for the construction of the systematic literature reviews [39] and taxonomies within the domain of information systems, specifically emphasizing digitalization, data, its value, and the associated ecosystems [42,43,44, 84].

However, it must be noted that although data valuation approaches and concepts are described in the present literature sample, they are not yet defined as business capabilities. Instead, this DVBC taxonomy serves to raise the multitude of data valuation approaches to the level of abstraction of a business capability to categorize them in the DVBC taxonomy. The latter becomes evident in the overview of capabilities in handling data value by Zeleti and Ojo [85]. Notably absent from this list is the inclusion of data value determination as a capability. This omission can be attributed to two factors. Firstly, the paper by Zeleti and Ojo [85] was published in 2017, and since then new insights into data valuation have emerged. Furthermore, the capabilities identified by Zeleti and Ojo [85] intersect significantly with the DVBC taxonomy, indicating that the present DVBC taxonomy implies many elements of Zeleti and Ojo [85], thus serving as a meaningful complement.

To comprehend and compare existing data valuation concepts within business capabilities, it is crucial to generalize fine-grained concepts and detail generic concepts. For this DVBC taxonomy, the TOGAF standard served as a frame construct providing the layers of information, processes, roles, and resources. At this point, however, it is essential to note that other notions for describing a business capability [17, 18] also have their reason for existence and could result in a different structuring of the taxonomy.

6.2 Content-related discussion

The content-related discussion of the DVBC taxonomy assesses the extent to which it complements existing literature, addresses research gaps, and justifies its raison d'être.

Initially, the DVBC taxonomy utilizes and complements existing taxonomies in literature. Firstly, the DVBC taxonomy leverages the “taxonomy of incentive mechanisms for data sharing in data ecosystems” of [84] as an impulse generator and builds upon this taxonomy by initiating a step prior to data sharing, specifically focusing on data value determination. However, the layers, dimensions, and characteristics identified by Gürpinar et al. [84] are partially utilized and further developed in the DVBC taxonomy.

Secondly, the DVBC taxonomy complements the reference value catalog taxonomy of [36] by introducing a data-centric perspective. This includes, for instance, addressing methods for determining value in general (qualitatively and quantitatively) and augmenting them within the data context in the DVBC taxonomy. Additionally, [36] emphasize uncertainty within value determination, which, within the DVBC taxonomy, is only partially represented in the dimension of result. While the goal of complexity reduction has been achieved, it may be necessary to provide further detail on uncertainty for future expansion and application of the DVBC taxonomy.

Thirdly, the “decision-making data value taxonomy” by [38] defines data value based on data utility and quality. Concerning the analyzed literature, it can be noted that while these two dimensions are indeed components of data value and a business capability for its determination, they are not entirely comprehensive. Hence, this DVBC taxonomy integrates data utility and data quality as characteristics under the dimension of data value driver. Additionally, the DVBC taxonomy incorporates further characteristics, dimensions, and layers to ensure a more applicable taxonomy in practical contexts.

Due to the DVBC taxonomy's emphasis on the practical applicability and implementation of a DVBC capability within an enterprise, two perspectives are not explicitly included in the taxonomy but serve as essential supplements: data value definition and external influences. The scientific literature provides a multitude of definitional frameworks and delineations of the term data value [11, 25, 26], which are recommended to be considered when discussing DVBC. However, their differentiation does not appear as a dimension or characteristic in the DVBC taxonomy to reduce complexity at this point and render the DVBC taxonomy applicable to professionals. Furthermore, the external perspective on data value determination, particularly the market and competitive structure of an enterprise [75], can be particularly relevant for implementing and applying a DVBC. This is why the work of [75] is viewed as a complementary pillar to the present DVBC taxonomy.

To conclude the content-related discussion, a fundamental aspect can be noted, which is often emphasized in the scientific literature and underscored by the DVBC taxonomy. It is the necessity of a symbiosis between business and technological perspectives in the purposeful management of data, and consequently, in the determination of data value [3, 14]. For instance, while DVBC taxonomy dimensions such as data valuation object, data value driver, or data valuation tooling predominantly address technological aspects of data valuation, the dimensions of value determination, value auditing, as well as stakeholder and component focus more on the business or procedural perspectives.

6.3 Quality-related discussion

To ensure high quality of the taxonomy and identify potential weaknesses, as well as areas for future research, it is imperative to subject the DVBC taxonomy to quality standards. 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 [86] and met by the DVBC taxonomy (see Table 1).

Usefulness refers to how the DVBC taxonomy, serving as the examined artifact, aids practitioners in accomplishing a specific objective [86]. Given that one objective of this study is categorizing diverse data valuation concepts as business capabilities, it is plausible to characterize the resultant DVBC taxonomy as useful.

Applicability, denoting the extent to which the artifact is put to practical use [86]. In Sect. 5, the DVBC taxonomy is assessed by applying two data valuation concepts, indicating that the developed taxonomy possesses scientific applicability. Nevertheless, the applicability of the DVBC taxonomy in real-world settings remains an area warranting further investigation in subsequent research endeavors.

Comprehensiveness, in the sense of the DVBC taxonomy's ability to adequately differentiate various data valuation concepts [86], is indeed achieved. The DVBC taxonomy encompasses technological and business viewpoints, mirroring the depth and breadth found in other scientifically robust taxonomies within information systems [42, 44].

Robustness, defined as the durability of the DVBC taxonomy over time [86], can also be affirmed within the scope of this paper. This is because the DVBC taxonomy exhibits a consistent nature from iteration two of the taxonomy development process onwards. However, it is worth noting that future research should focus on scrutinizing the robustness of the DVBC taxonomy through real-world case studies.

Conciseness, which pertains to the simplicity of the DVBC taxonomy [86], can also be verified at this juncture. This is substantiated by the strictly defined upper limit of nine dimensions [19, 41] and by the deliberate exclusion of factors such as external elements that could divert and complicate the focus of the DVBC taxonomy. To prevent any potential gaps in understanding the DVBC taxonomy, additional complementary taxonomies and frameworks from academia were described and related to the DVBC taxonomy.

Extensibility, defined as the capacity to incorporate additional dimensions and characteristics [86], is met, especially concerning potentially new characteristics. The DVBC taxonomy does not impose restrictions on the inclusion of new characteristics within existing dimensions. The same holds true for the extension of further dimensions. However, it is worth noting that in doing so, it is ideal not to compromise the quality criterion of conciseness or if necessary, to do so only with a valid justification.

Explanatory applies when the DVBC taxonomy can describe the objects under evaluation [86], such as data valuation concepts. At this juncture, the dimension of being explanatory can also be verified. This is because the scope of nine dimensions and a total of 36 characteristics provides the requisite breadth and depth for explaining and categorizing data valuation concepts.

In addition, [86] define six guidelines to ensure the compliance of excellence standards regarding the development of taxonomies. Guideline 1 (scoping of taxonomy evaluation), guideline 2 (justification of objective ending conditions), guideline 3 (justification of subjective ending conditions), and guideline 4 (demonstration of DVBC taxonomy applicability) have been successfully carried out based on 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 methodology, content, and quality of the developed DVBC taxonomy are considered as given with the note of future field testing and long-term validation.

7 Conclusions

Our research is motivated by several contributions highlighting the significance of data valuation and demand for more in-depth study in this area [11,12,13,14,15, 38]. Therefore, this paper focuses on developing a data valuation business capability to provide a concept to classify and 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 and tested against two contemporary data valuation approaches [34, 35], and the taxonomy is reviewed for its raison d'être in terms of methodology, content [87] and quality based on the evaluation criteria by [86]. The result is an excellence-assured data valuation business capability taxonomy consisting of four layers, nine dimensions, and 36 characteristics.

The implications of the developed DVBC taxonomy are considerable versatility. For academic scholars, these implications manifest in two primary ways: Firstly, the taxonomy establishes a vital connection between the realms of information systems, enterprise architecture management, and data management. This connection is instrumental in catalyzing the growth of these domains for fostering the advancement of comprehensive data valuation concepts. Secondly, the taxonomy enhances clarity and insight into extensively debated subjects concerning the nature of data value and the strategies for its operationalization. By incorporating enterprise architecture management as a central bracket, this taxonomy contributes to transparency and enables the transfer of data valuation theories from academia to practical deployment in real-world enterprises.

For professionals, the implications of this taxonomy are threefold. Firstly, considering that a substantial number of enterprises continue to encounter challenges in effectively gauging data value, this taxonomy serves as an illuminating lighthouse. It shifts the perspective on data valuation away from a sporadic and non-standardized notion toward an integral business capability firmly embedded within an enterprise's architecture. Secondly, the layers, dimensions, and characteristics outlined in the DVBC taxonomy may serve as a bedrock for professionals, facilitating the development of enterprise-specific assessments of data valuation maturity. This, in turn, assists enterprises in systematically constructing their data valuation capabilities while pinpointing pivotal action areas in this domain. Lastly, professionals operating within the CIO domain can leverage the taxonomy to comprehend and effectively communicate to stakeholders that data valuation transcends being solely a technological or business matter. Instead, it represents a symbiosis of these facets, underscoring the need for cohesive collaboration. This realization empowers professionals to engage the necessary stakeholders in a manner that fosters value generation with and through.

Our research has limitations. One limitation relates to the structure of the taxonomy, which is based on the practice-approved and scientifically sound TOGAF standard, knowing that other structuring concepts exist for describing a business capability. Another content limitation relates to 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 defined as non-exclusive, which may dilute the strict delineation of data valuation business capabilities to some degree. Another limitation lies in the explicit exclusion of external elements related to data valuation, which is aimed at maintaining the focus and simplicity of the taxonomy. Nevertheless, it is important to note that this may necessitate an expansion of the DVBC taxonomy in future practical settings, a possibility facilitated by the design of the taxonomy itself.

With a view to future scientific work, three thematic areas can be formulated. On the one hand, it is recommended to validate the developed taxonomy in a field study of long-term nature. In addition, a data value ontology, which follows scientifically sound standards such as OntoClean [88] could be developed based on the taxonomy. Furthermore, the concept for defining data valuation as a business capability should be tested and refined in real-world scenarios following the design science research paradigm [87].

8 Data availability statement and declarations

The information utilized for performing both the upstream SLR and the existing classification scheme, aimed at establishing a data valuation business capability, has been sourced from the institutional licenses of Instituto Superior Técnico, Lisbon. The search engines employed within the scholarly databases ACM Digital Library, AIS eLibrary Ebsco, Emerald Insight, IEEE Xplore, ScienceDirect, Scopus, Web of Science, and Wiley Online Library were implemented following the protocol detailed in Sect. 3.1 and illustrated in Table 4 below to foster a more comprehensive outline promoting scientific transparency and replicability [89].

Table 4 Details regarding the systematic literature transparency
Full size table

Furthermore, the data obtained from the aforementioned databases were analyzed and visualized using commonly accessible Microsoft Office software, such as Excel and PowerPoint. The relevant scholarly references were automatically curated, standardized, as well as managed, employing the Mendeley Reference Manager version 2.95.0. Finally, the premium version of the software Grammarly (version: v1.0.40.855) from the corresponding manufacturer in combination with ChatGPT was employed to ensure orthographic accuracy only.

Lastly, no research funds and cooperations influencing the research are to be declared. Further, no competing interests are directly or indirectly related to the paper.