The final 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 scientific 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
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 in order to determine a data value [16]. Specifically, 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 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, 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 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, 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 final 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 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 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 define three exclusive characteristics in the dimension data valuation tooling in our taxonomy.
The first 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 defined 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
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 defined 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 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, 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 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 [79, 84].