Creating a Taxonomy of Business Models for Data Marketplaces

Abstract

Data marketplaces can fulfil a key role in realizing the data economy by enabling the commercial trading of data between organizations. Although data marketplace research is a quickly evolving domain, there is a lack of understanding about data marketplace business models. As data marketplaces are vastly different, a taxonomy of data marketplace business models is developed in this study. A standard taxonomy development method is followed to develop the taxonomy. The final taxonomy comprises of 4 meta-dimensions, 17 business model dimensions and 59 business model characteristics. The taxonomy can be used to classify data marketplace business models and sheds light on how data marketplaces are a unique type of digital platforms. The results of this research provide a basis for theorizing in this rapidly evolving domain that is quickly becoming important.

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Creating a Taxonomy of Business Models for Data Marketplaces
Conference Paper · June 2021
DOI: 10.18690/978-961-286-385-9.23
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DOI https://doi.org/10.18690/978-961-286-385-9.23
ISBN 978-961-286-385-9
CREATING A TAXONOMY OF BUSINESS
MODELS FOR DATA MARKETPLACES
Keywords:
data
marketplace,
business
model,
data
trading,
taxonomy,
dimensions
M
ONTIJN VAN DE
V
EN
,1
A
NTRAGAMA
E
WA
A
BBAS
,2
ZENLIN KWEE2 & MARK DE REUVER2
1 Eindhoven University of Technology, Department of Industrial Engineering and
Innovation Sciences, The Netherlands; e-mail: m.r.v.d.ven@tue.nl
2 Delft University of Technology, Faculty of Technology, Policy and Management, The
Netherlands; e-mail: a.e.abbas@tudelft.nl, z.roosenboom-kwee@tudelft.nl,
g.a.dereuver@tudelft.nl
Abstract Data marketplaces can fulfil a key role in realizing the
data economy by enabling the commercial trading of data
between organizations. Although data marketplace research is a
quickly evolving domain, there is a lack of understanding about
data marketplace business models. As data marketplaces are
vastly different, a taxonomy of data marketplace business models
is developed in this study. A standard taxonomy development
method
is followed to develop the taxonomy. The final
taxonomy comprises of 4 meta-dimensions, 17 business model
dimensions and 59 business model characteristics. The
taxonomy can be used to classify data marketplace business
models and sheds light on how data marketplaces are a unique
type of digital platforms. The results of this research provide a
basis for theorizing in this rapidly evolving domain that is quickly
becoming important.

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1 Introduction
As an organization may not always possess the required data to carry out or improve
their processes and services, they may wish to purchase these data from other
organizations. A data marketplace can enable data purchase by providing a digital
platform through which individuals and organizations can exchange data (Stahl et
al., 2016; Schomm et al., 2013). In contrast to most other platforms, where data is
utilized to improve services or manage customer relationships, on data marketplaces
data is actually the product itself (Spiekermann et al., 2018).
Despite the potential benefits of data marketplaces, in practice, very little data is
shared or traded via these platforms (Koutroumpis et al., 2020). In general, little
research has been conducted on data marketplaces (Thomas & Leiponen, 2016) and
data marketplace business models in particular (Fruhwirth et al., 2020; Spiekermann,
2019). As a foundation for research on a novel and diverse phenomenon, a first step
is developing a taxonomy, because it can be used to classify data marketplace
business models (Lambert, 2015). Two taxonomies of data marketplace business
models are currently available in the literature, i.e. those proposed by Fruhwirth et
al. (2020) and Spiekermann (2019) respectively.
The existing taxonomies (Fruhwirth et al., 2020; Spiekermann, 2019), however,
overlook two main areas which this study aims to address. Firstly, the two studies
mostly focus on the classification of multilateral data marketplaces, while in practice
data trading often happens via bilaterally negotiated contracts (Koutroumpis et al.,
2017). Secondly, the studies view data marketplace business models from a single
firm perspective. However, data marketplaces take part in a network of stakeholders
involving data analysts, application vendors, algorithm developers, data providers,
consultants, licensing entities, and platform providers (Muschalle et al., 2012;
Thomas & Leiponen, 2016).
To address the above two under-researched areas, this study develops a taxonomy
from a multi-stakeholder perspective on business models. We define a business
model as the way a network of stakeholders creates and captures value (Bouwman
et al., 2008). This multi-stakeholder perspective allows us to understand the business
model for the data ecosystem as a whole. Moreover, we define a data marketplace
as a digital system where data is traded as an economic good, that connects data

M. van de Ven, A. E. Abbas, Z. Kwee & M. de Reuver:
Creating a Taxonomy of Business Models for Data Marketplaces
315
buyers and data sellers, and facilitates data exchange and financial transactions
(Koutroumpis et al., 2020; Stahl et al., 2016). In this way, the term data marketplace
is broadly interpreted, to go beyond the already studied multilateral data
marketplaces.
The remainder of this paper is structured as follows: in Section 2, the taxonomy
development process is described. Subsequently, Section 3 presents the developed
taxonomy on the basis of the identified business model dimensions. Lastly, Section
4 provides a conclusion of the research and discusses the scientific contribution,
practical relevance and limitations of this study.
2 Taxonomy development process
To develop the taxonomy, we follow the taxonomy development method by
Nickerson et al (2013). Meta-characteristics of the taxonomy are defined first. Next,
the thirteen ending conditions suggested by Nickerson et al. (2013) were employed.
After that, multiple iterations are conducted to refine the taxonomy.
2.1 Meta-characteristics
Meta-characteristics function as overarching characteristics of the object of interest
(Nickerson et al., 2013). We use the four business model domains of the STOF
ontology (i.e. Service, Technology, Organization and Finance domains as in
Bouwman et al. (2008)) as the meta-characteristics of the taxonomy, as the STOF
approach takes service as a unit of analysis and employs a multi-stakeholder
perspective on business models (Bouwman et al., 2008). This perspective is well-
suited for data marketplaces because a network of business actors are involved in
and around data marketplaces (Muschalle et al., 2012; Thomas & Leiponen, 2016).
2.2 Literature search
We collected dimensions and characteristics from existing literature. A literature
search was conducted to discover existing knowledge about the object of interest
(Webster & Watson, 2002). Google Scholar was consulted to find relevant academic
sources, using the search string “Data marketplaces” AND (“Business models” OR
“Digital platform” OR “Digital marketplace” OR “Data trading” OR “Data

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economy”). This string resulted in a total of 359 articles. The articles were scanned
based on their title, abstract and relevance, which resulted in a preliminary selection
of 17 articles. After making this pre-selection of articles, the full text of the articles
was read, which resulted in the exclusion of seven articles that did not explicitly
discuss dimensions or characteristics of data marketplace business models. Based on
cross-reference of the selected articles, we added four additional articles that
presented topic-relevant business model taxonomies to the list. The literature review
resulted in a final set of 14 articles as presented in Table 1.
Table 1: Overview of classifications relevant to data marketplace business models
Author(s) (Year) Type
Citations
(dated 14
April
2020)
Schomm et al. (2013)
Dimensions of data providers and data
marketplaces
73
Stahl et al. (2014a)
14
Stahl et al. (2014b) 16
Stahl et al. (2017)
12
Stahl et al. (2016)
Classification of electronic marketplaces
30
Koutroumpis et al. (2017)
Market designs for data marketplaces
19
Muschalle et al. (2012)
Pricing models for data marketplaces
74
Fricker and Maksimov
(2017) 8
Spiekermann (2019)
Taxonomy of data marketplace business models
9
Fruhwirth et al. (2020)
1
Bock and Wiener (2017)
Taxonomy of digital business models
22
Täuscher (2016)
Taxonomy of marketplace business models
6
Täuscher and Laudien (2018)
153
Hartmann et al. (2014)
Taxonomy of data-driven business models
131
2.3 Selection of empirical cases
To account for the practical relevance of the taxonomy, we conducted desk research
between May and July 2020 to build a database of empirical cases of data
marketplaces. Sixty-five websites of data marketplaces that were mentioned in
existing studies of data marketplaces were included in the database (Carnelley et al.,

M. van de Ven, A. E. Abbas, Z. Kwee & M. de Reuver:
Creating a Taxonomy of Business Models for Data Marketplaces
317
2016; Koutroumpis et al., 2020, 2017; Prlja, 2019; Spiekermann, 2019; Stahl et al.,
2016). The data discovery platform datarade.ai, a website that provides an overview
of over 1,800 data providers and 200 data platforms, was consulted. In total, the
search in the repository of datarade.ai resulted in the discovery of an additional set
of 187 data marketplaces. To complement the database with cases that were not
considered in the existing studies or part of the datarade.ai database, we used the
search engine Google to further conduct a desk research. The keywords “data
marketplace”, “data market” and “data trading platform” were applied during the
search. From this search, fifteen data marketplaces were added to the database.
Four criteria were applied to the companies that resulted from the desk research to
ensure the relevance of the empirical cases. Firstly, data marketplaces that turned out
to be shut down, after inspecting the website, were excluded from the database.
Secondly, the companies that did not fit this study's definition of a data marketplace
were excluded. This implied that data marketplaces that only provided open data,
such as governmental organizations and NGOs, were excluded as these platforms
adopt non-commercial business models (Carnelley et al., 2016). Thirdly, data
marketplaces that did not have an English version of their website were excluded.
Lastly, data marketplaces that were still in the construction phase were excluded.
The application of these four criteria to the cases led to the exclusion of 89 cases.
Therefore, the final database consisted of 178 cases of data marketplaces.
To analyse the business models of existing data marketplaces, a sample was taken
from the database of cases. The empiricist philosophy of classification prescribes to
build a taxonomy based on the consideration of many characteristics (Lambert,
2015). Therefore, the cases of data marketplaces in the database were first segmented
into groups based on the similarity of their characteristics. The website of datarade.ai
categorized data marketplaces based on the type of data traded on the platform. This
variable was selected as the leading sampling variable to explore the variation
between cases in the database. Based on the available information on datarade.ai and
an inspection of the case's website, 138 cases could be labelled by type of data traded
on the platform. The remaining 40 cases in the database were labelled based on the
classification of the cases in the existing studies (Fruhwirth et al., 2020; Spiekermann,
2019) and through the manual inspection of the companies’ website.

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The segmentation of data marketplaces by type of data traded on the platform
reveals that some segments of data marketplaces in the database were
overrepresented. This was especially the case for audience data marketplaces, that
constituted over 60% of the cases (N=112). Audience data is combined data about
a certain target group of customers, which is much sought after by marketeers. To
compensate for the overrepresentation, instead of random sampling, a
disproportionate stratified sample of N=40 cases was taken from the database
(Daniels, 2011). The final sample of 40 data marketplaces consisted of ten data
marketplaces on which any type of data is traded (25% of the sample), four financial
and alternative data marketplaces (10%), nine audience data marketplaces (22.5%),
six sensor and mobility data marketplaces (15%), four geo data marketplaces (10%)
and seven health and personal data marketplaces (17.5%) (available here:
https://doi.org/10.4121/14679564.v1).
2.4 Design iterations
Our design phase started with a conceptual-to-empirical approach (Nickerson et al.,
2013). In these design iterations, the concepts derived from the literature were
compared to the sample of empirical cases. Information on the business models of
the cases was collected from publicly available sources such as company websites
and news articles. The discovered information fragments were coded using the
dimensions and characteristics from the literature review as a guideline (See Table
2). After each case, newly identified characteristics were added to the dimensions of
the taxonomy. After two conceptual-to-empirical design iterations, two empirical-
to-conceptual iterations were conducted, which resulted in the addition of two
dimensions to the taxonomy: enterprise data marketplace and data processing and analytics
tools. After every design iteration, the ending conditions were checked. After two
conceptual-to-empirical iterations and two empirical-to-conceptual iterations, both
the objective and subjective ending conditions were met. Finally, to test the
usefulness of the taxonomy, three mini-case studies were conducted on empirical
cases of data marketplaces that were not part of the sample, i.e. Wibson, QueXopa
and Advaneo respectively. The taxonomy was found to be useful, as the business
models of the cases could be classified based on public information about the cases.

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Table 2: Coding examples for the value proposition dimension
Characteristic
Case
Quote
Easy data
access and/or
tooling
Open:Factset
Marketplace
“FactSet creates data and technology solutions for investment
professionals around the world, providing instant access to
financial data and analytics that investors use to make crucial
decisions.”
Knoema
“Knoema is a cloud-based data technology platform that makes
data accessible and delivers intelligent data tools to enable data
access and discovery.“
Secure data
sharing
DAWEX
“With Dawex Global Data Marketplace providers can
highlight the value of their data while retaining full control over
the distribution and configuration of usage rights.”
Snowflake
“Unlike other data marketplaces, Snowflake Data Marketplace
leverages Snowflake's Secure Data Sharing technology, which
means no data transfer and no need to squeeze data through
APIs or use cloud storage.”
High quality
and unique
data
Amazon
DSP
“Use exclusive Amazon audiences to reach your ideal audience
on and off Amazon.”
Datax
“Quality business data for better sales leads – Any campaign is
only as good as the data it’s built on – so make sure yours is the
best.
3 Taxonomy of Data Marketplace Business Models
The final taxonomy consists of 4 meta-dimensions, 17 dimensions and 59
characteristics and is presented in Table 3. In the following sections, the data
marketplace business model dimensions are discussed per meta-dimension (STOF).
Table 3: Taxonomy of data marketplace business models
Dimension
Characteristics
Service domain
Value
proposition
Easy data
access and/or
tooling
Secure data
sharing
High quality and
unique data
All services
in a single
platform
Enterprise data
marketplace
Yes No

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Data
processing and
analytics tools
Yes No
Marketplace
participants
B2B C2B Any
Industry
domain
Any
data
Geo
data
Financial &
Alternative
data
Health &
Personal
data
Audience
data
Sensor &
Mobility
data
Geographic
scope
Global Regional Local
Time frame
Static
Up-to-date
(Near) real-time
Multiple
Technology domain
Platform
architecture
Centralized Decentralized
Data access API Download Specialized software
Multiple
options
Data source
Self-
generated
Customer
provided data
Acquired data
Multiple
sources
Organizatio
n domain
Matching
mechanism
One-to-one One-to-many Many-to-one
Many-to-
Many
Platform
sponsor
Private Consortium Independent
Finance domain
Revenue
model
Commissions Subscriptions Usage fees Asset sales
Pricing model Freemium Pay-per-use Flat fee
tariff
Package
based
pricing
Multiple
Price discovery Set by buyers Negotiation Set by marketplace
provider
Set by
external
sellers
Smart contract
Yes
No
Payment
currency
Fiat money Cryptocurrency

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3.1 Service domain
The value proposition is a statement that indicates the proposed value that an
enterprise intends to deliver to the customer (Bouwman et al., 2008). It often
describes how customers can benefit from using the service and how the enterprise
aims to set itself apart from the competition. Some data marketplaces offer an
enterprise data marketplace as an additional service. An enterprise data
marketplace functions as a private data marketplace that enables organizations to
share data within the company or with external partners, such as suppliers and
customers, that are invited by the focal organization. The data processing and
analytics tools characteristic indicates whether a data marketplace offers additional
tooling on top of the data, to perform analytics activities on proprietary data or data
bought via the platform. The marketplace participants dimension describes the
type of participants that are allowed to register and exchange data on the
marketplace. While most data marketplaces allow the exchange of any type of data
on their marketplace, some data marketplaces focus their data offering towards a
specific industry domain. The geographic scope describes the regions in which
the data marketplace is operating and available to users (Täuscher & Laudien, 2018;
Täuscher, 2016). The time frame dimension describes whether or not the data needs
frequent updates to maintain the relevancy of the data (Schomm et al., 2013).
3.2 Technology domain
Data marketplaces may adopt two types of platform architectures: centralized or
decentralized (Koutroumpis et al., 2017). In the centralized approach, data providers
offer their data products via a predefined centralized location on the platform, such
as a cloud repository. In decentralized platforms, the data products remain at the
data provider and the data is traded using distributed ledger technologies such as
blockchain. Platform providers may provide access to the data in a number of
different ways (Schomm et al, 2013). The data source dimension describes the
origin where the data was gathered or collected by the data marketplace platform
(Hartmann et al., 2014).

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3.3 Organization domain
The matching mechanism determines the number of parties on each side of the
platform (Koutroumpis et al., 2017). Besides multilateral data marketplaces, three
more types of data marketplaces exist: bilateral data marketplaces (one-to-one
matching), dispersal data marketplaces (one-to-many matching), and harvest data
marketplaces (many-to-one matching). The platform sponsor can be a private
individual or a group, a consortium of buyers or sellers, or an individual or a group
that is independent of other market players (Stahl et al., 2017, 2016).
3.4 Finance domain
The revenue dimension describes the main source of revenue for a data marketplace
(Spiekermann, 2019; Täuscher & Laudien, 2018; Täuscher, 2016). The pricing
model specifies how the final price for the data good or service is composed
(Fruhwirth et al., 2020; Schomm et al., 2013; Spiekermann, 2019; Täuscher &
Laudien, 2018; Täuscher, 2016). A price discovery function allows buyers and
sellers on the marketplace to determine a transaction price which they both agree on
(Bakos, 1998). Data marketplaces may implement smart contracts to enhance
transparency and to enforce trust among marketplace participants (Fruhwirth et al.,
2020). The payment currency dimension explicates which currencies are accepted
for the payments made by marketplace participants (Fruhwirth et al., 2020).
4 Discussion and Conclusion
The developed taxonomy of data marketplace business model has two key scientific
contributions. First, the results of the study contribute to the scarce knowledge
about data marketplaces and their respective business models (Thomas & Leiponen,
2016). This study adopts a multi-stakeholder perspective on data marketplace
business models by emphasizing the roles in the data ecosystem. The taxonomy
provides an overview of contemporary knowledge about data marketplace business
models and exposes new business model alterations that have emerged in practice.
A second contribution made by this study is related to the interpretation of a data
marketplace. Existing taxonomies (Fruhwirth et al., 2020; Spiekermann, 2019) focus
on studying one type of data marketplaces: multilateral data marketplaces
(Koutroumpis et al., 2017). In our study, data marketplaces are more broadly

M. van de Ven, A. E. Abbas, Z. Kwee & M. de Reuver:
Creating a Taxonomy of Business Models for Data Marketplaces
323
interpreted as digital systems for trading data as an economic good, that connect
buyers and sellers, and facilitate data exchange and financial transactions. This allows
us to identify additional business model dimensions, which are not part of existing
taxonomies: enterprise data marketplace, data processing and analytics tools,
geographic scope, matching mechanism and platform sponsor. By eliciting how data
marketplace business models differ, we provide a basis for fine-grained theory
development, which is often lacking in platform studies (De Reuver et al., 2018).
The developed taxonomy can guide decision-makers who are exploring the options
of setting up a data marketplace or to join an existing data marketplace. An improved
understanding about data marketplace business models may help to achieve
commercialization, that will make data more accessible and exploitable to
individuals, businesses and authorities.
Although we took a systematic approach, subjectivity in assessing the cases may pose
a limitation. We dealt with this by conducting multiple iterations and re-
interpretations of the data. Further, not all data marketplace companies disclose
sufficient information about all of their business model characteristics. Therefore,
not all empirical cases could be classified into all of the conceptually derived
dimensions. This was especially the case for financially related dimensions such as
revenue partners and cost categories (Täuscher & Laudien, 2018). Lastly, as in any
taxonomy development study, our study is limited to the current set of phenomena
that exist in practice. Hence, future research may update our taxonomy in light of
fundamentally new data marketplace types.
Data marketplaces pose a foundation for the data economy: they enable firms to
access external data to drive their business and to profit from selling their own data.
The EU is investing heavily in data marketplaces in the years to come (European
Commission, 2020). At the same time, ambiguity pertains over what constitutes a
viable data marketplace business model. Our taxonomy takes a broad and multi-
stakeholder perspective to data marketplaces, going beyond the single-firm
multilateral perspective of extant taxonomies. We argue that such a broad conceptual
basis is needed to advance scholarly understanding of ecosystems in the data
economy and to unlock the potential of trading data for a functioning data economy.

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Acknowledgments
The research leading to these results has received funding from the European Union’s
Horizon 2020 Program, under grant agreement 871481 – Trusted Secure Data Sharing Space
(TRUSTS).
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