Business Strategies for Data Monetization: Deriving Insights from Practice

Abstract

Although increases in available data have inspired companies’ interest in creating and extracting value from it, many lack the insight and guidance to assess the potential data offer. To address this issue, we conduct a systematic literature review to create a universe of 102 real-world cases from diverse industries with regard to the use of data. Based on an analysis of these cases, this paper provides a set of 12 generic strategies for monetizing data, ranging from sole asset sale to strategically opening data and guaranteeing control. This study supports business practice by aggregating the wide range of established approaches of data monetization from practice for operational purposes. It advances theoretical understanding of value capturing from data and suggests important avenues for future work in this emerging field of research.

Full text

15th International Conference on Wirtschaftsinformatik,
March 08-11, 2020, Potsdam, Germany
Business Strategies for Data Monetization:
Deriving Insights from Practice
Julius Baecker1,2, Martin Engert1,2, Matthias Pfaff1,2, and Helmut Krcmar2
1fortiss - Forschungsinstitut des Freistaats Bayern für softwareintensive Systeme
und Services, Munich, Germany,
2Technical University of Munich, Chair for Information Systems, Munich, Germany,
{baecker, engert, pfaff}@fortiss.org, helmut.krcmar@tum.de
Abstract. Although increases in available data have inspired companies’
interest in creating and extracting value from it, many lack the insight and
guidance to assess the potential data offer. To address this issue, we conduct a
systematic literature review to create a universe of 102 real-world cases from
diverse industries with regard to the use of data. Based on an analysis of these
cases, this paper provides a set of 12 generic strategies for monetizing data,
ranging from sole asset sale to strategically opening data and guaranteeing
control. This study supports business practice by aggregating the wide range of
established approaches of data monetization from practice for operational
purposes. It advances theoretical understanding of value capturing from data
and suggests important avenues for future work in this emerging field of
research.
Keywords: Data monetization, data-driven business models, big data, data-
driven decision making
1 Introduction
Ever-increasing quantities of available data motivate companies to leverage it for
economic benefit [1, 2]. Data is increasingly regarded as a valuable resource bought
and sold online as an asset [3]. And indeed, the use and monetization of data can be
an actual source of competitive advantage for businesses in the digital economy [4].
As an emerging phenomenon driven by current technological trends in the context of
big data, data monetization gains importance in both research and practice.
Accordingly, the term data monetization has been introduced by prior research in the
field: Najjar and Kettinger define data monetization as converting the intangible value
of data into real value [2]. Similarly, Gartner refers to data monetization as "using
data for quantifiable economic benefit" [5]. For the understanding of this work,
however, we emphasize that real and quantifiable value can occur as both a monetary
value and any other quantifiable economic benefit. For example, data can be
monetized through increased process efficiency or product improvements that rely on
data [4]. The value then becomes real through quantifiable, subsequent effects or
returns, such as reduced costs, repeated purchases and/or increased market share.
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Building on the above definitions, we understand a data monetization strategy as a
way an organization pursues to use data for quantifiable economic benefit.
Monetization of data is of growing importance, since it is a critical issue that many
industries face today [6]. Business leaders assign a higher priority to the issue of
monetizing data compared to questions of technical feasibility [7]. So far, however,
companies monetize data only to a very limited extent as shown by a McKinsey
Analytics survey in 2017 [8]. This indicates that many companies struggle to extract
economic value from their data. Indeed, a step towards data monetization can be very
challenging for organizations in practice as its adoption usually requires
organizational changes and technological upgrades [4]. In order to stay competitive,
companies need to assess and prepare their existing business models with regard to
the use of data [1]. Accordingly, for companies it is important to identify the most
promising opportunity to start their data monetization efforts [4].
In contrast to their immense importance for practice, strategies for monetizing data
are an under-investigated topic [6, 9] and data monetization best practices have yet to
be identified [2]. As Najjar/Kettinger and Günther et al. point out, reliable strategies
to support practical efforts are required [2, 10]. Investigating how companies actually
leverage available data to improve their businesses and operations can support
practitioners in overcoming barriers to utilizing data [11]. Consequently, there is a
need for research on data monetization strategies. To address this issue and to deepen
the understanding of companies successfully using data for economic benefits, we
aim at investigating the following research question (RQ): How do organizations
monetize data? In addition, we intend to examine the economic benefits that arise
from different data monetization strategies.
To answer this RQ, this study provides an in-depth analysis of case studies of
different industries, business types, and business sizes to deepen theoretical and
practical understanding of data monetization. Based on a literature review, we created
a set of 102 cases, from which we extracted a comprehensive set of generic
monetization strategies. Since we investigated monetization strategies independent of
volume, variety, and velocity of data, we use the more general term "data" instead of
"big data" in this study, although some of the case studies were presented in the
specific context of "big data". By addressing calls for additional research concerning
implications and strategies of data monetization in companies [1, 2, 10], we aim at
extending previous theoretical findings through an exploratory approach. This
contribution depicts the state of the art in this continuously evolving field of research.
Furthermore, we support practitioners’ efforts to assess and decide on appropriate
business strategies for data monetization.
2 Related Work
Research on data monetization is tied to closely related topics such as "data-based" or
"data-driven" business models (DDBM). Accordingly, this section summarizes
relevant findings from both fields that have an impact on this study.
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Academic literature has rarely discussed data monetization and DDBMs in general, as
both are emerging fields of research [6, 12]. An important part of previous work has
focused on case studies providing detailed insights into different approaches to data
monetization in enterprises. Najjar and Kettinger conduct an in-depth study in the
retail industry [2] analyzing different stages of the company’s data monetization
journey. In addition, they discuss in detail how the company benefits from this
strategy. Similarly, Alfaro et al. present insights from BBVA, a global financial
group, pursuing three different data monetization strategies [13]. Further empirical
studies with a focus on the usage of data in enterprises for economic benefit
encompass different domains such as manufacturing [14-16], online platforms [17-
21], aviation [22], start-ups [12], finance [13, 23], open data [24-28] or further (and
mixed) domains and industries [1, 11, 29-38]. They form the basis of this study (as
presented in detail in the next section).
Focusing on theoretical findings with regard to strategies on data monetization,
previous studies propose two frameworks in academic literature. Walker presents four
overarching “data strategies”, each of them instantiated by underlying approaches for
monetization purposes [39]. These monetization strategies are illustrated in detail
through comprehensive examples for firms. Further, Wixom and Ross introduce three
high-level directions for data monetization as well as corresponding examples from
the business world: improving internal processes, wrapping information around
products and services, and selling information offerings [4]. Likewise, research on
DDBMs provides some studies with relevant findings in the context of strategies for
data monetization. Hartmann et al. [12] review literature in terms of existing
dimensions of business models and derive a framework for DDBMs. They introduce a
definition of a DDBM as a "business model relying on data as a key resource" by
including the following (resulting) business model dimensions within their
framework: data sources, key activities, value proposition, customer segment, revenue
model and cost structure [12]. Schüritz and Satzger investigate five patterns of data-
infused business model innovation [1] examining the influence of data on different
dimensions of a business model. Furthermore, Zolnowski et al. identify business
model transformation patterns of data-driven innovations [36]. Further studies explore
revenue models for data-driven services used by start-ups in detail [40] or focus on
key success factors [33] and capabilities [16] for innovating business models through
digital data streams. Prior work has also suggested the use of business model patterns,
some of which focus on selected aspects of the use of data, such as Data as a Service
and Database Marketing [41]. These patterns however, only contain limited options
for businesses to monetize their data.
Although prior studies as presented above support and advance general understanding
of data monetization, academic literature does not provide a comprehensive and
empirically developed overview of strategies on data monetization so far. Therefore,
based on the initial set of data monetization strategies from Wixom and Ross [4], we
investigate this phenomenon with a method that has not been applied in this context.
For this purpose, we choose a systematic bottom-up approach, turning empirical
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results into theoretical evidence to close this gap addressing specific calls for further
research [1, 2, 10]. In answering our research question on this emerging topic, we
provide a structured overview on data monetization strategies to guide and support
decision makers in strategic planning. Furthermore, we add descriptions to each of the
resulting data monetization strategies and reference back to the original cases. Finally,
we investigate specific benefits that arise from applying each strategy in practice.
3 Research Method
To investigate strategies of data monetization, we divided our research approach in
four steps, as shown in Figure 1.
Figure 1. Four-step research approach for deriving generic data monetization strategies
First, we reviewed prior literature on empirical studies of data monetization and
DDBMs. In order to find relevant articles, we searched the scientific databases
SCOPUS, IEEE Explore and ACM Digital Library for journals and conference papers
using the term “data” in conjunction with terms such as “monetization”, “exchange”,
“value”, “fee”, “revenue” and “pricing” and their possible variations. The search
resulted in 499 hits, comprising 37 relevant articles. As selection criteria for our
review, we focused on the following: (1) we only included papers treating at least one
real-world use case. (2) From these, we filtered for papers that provided enough
information on the respective case(s) and the applied data monetization strategies
based on our definition of data monetization. This step borrows from the second step
of case survey methodology as proposed by [42].
In the second step, we created a case base comprising 142 cases described in
relevant articles. To augment the cases and ensure data triangulation [43], we
manually searched for information about the case study’s business models and data
monetization approaches. After checking all cases for sufficient information, this
resulted in 102 cases for further consideration. The resulting final case base comprises
an exhaustive overview of cases from multiple industries, such as manufacturing,
finance, tech, travel and telecommunications. Further, it covers companies of different
size, representing a diverse set of SMEs and big companies, but also companies of
different age like start-ups, new tech companies and incumbents. Additionally, the
(1) Literature Review
Search prior work for
articles related to data
monetization
Identification of 37
articles in SCOPUS,
IEEE Explore and
ACM Digital Library
(2) Case Base
Identify and document
promising use cases
from relevant literature
Case base comprising
142 use cases in the
context of data
monetization
(3) Analysis
Screen and code use
cases individually
102 cases coded
regarding the
underlying strategies
for data monetization
on an individual basis
(4) Generic Strategies
Iterativ ely find
consens us for codes of
cases
Generic data
monetization strategies
from individual codes
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cases differ in their underlying business models, ranging from classical linear value
creation to complex multi-sided platforms.
Third, the coding team of three screened all excerpts of cases in the case base and
derived a first set of codes in a bottom-up approach. While inductively analyzing
qualitative data, we borrowed from procedures of grounded theory methodology [44].
Thus, we initially associated about 180 codes to 102 cases. In the next step, we
grouped codes to categories in an axial coding step, while identifying relationships
among codes (Table 1) [44]. Using theoretical memos helped us to account for ideas
during coding [44]. During selective coding, the core categories emerged and the
relevant strategies were identified.
Table 1. Illustration of coding scheme
Excerpt from literature in
case base
Initial Codes
Generic Strategy &
Economic Benefit
“Lufthansa uses Big Data to
minimize delays:
Predict network behavior and
departure delays of aircraft
throughout the day, taking into
account
 Reactionary delays, rotation
oriented, and
 Weather and congestion at
airports. […]”
“Benefits include:
 Decreasing financial loss due
to delays, […] [22]
1. Increase Process
Efficiency
2. Decision Support
3. Data Collection
4. Optimize Process
Business Process
Improvement leading to
Cost Reduction
Finally, the individual results were consolidated and a final set of generic data
monetization strategies was derived based on a consensus of the research team.
Within this step, the team aimed at finding a good balance between individuality and
generality of the strategies. As a result, we derived each resulting strategy from at
least three different real-world business cases, corroborating the findings.
Furthermore, we assured the resulting set of strategies represents all monetization
strategies found in our case base.
4 Data Monetization Strategies
This section presents our findings from aggregating, analyzing, and consolidating
business cases of data monetization to answer the RQ posed in the Introduction. An
overview summarizing all strategies is illustrated in in Table 2.
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4.1 Asset Sale
In accordance with established findings, our case analysis shows that firms frequently
sold data solely as an asset. However, we identified further differences in the
provision of data sold to third parties. First, data was sold directly to customers,
granting them full control of the asset (Strava [17], Factual [32], Verizon Wireless
[1]). In other cases, such as LinkedIn [19], data could only be accessed and read by
(paying) customers on a query basis, keeping control of the dataset as a whole.
Finally, Gnip [35] and InfoGroup [32] sell data to customers in real-time according to
predefined criteria, mimicking a ‘data-as-a-service’ strategy.
Economic benefits comprise the creation of new revenue streams and the
extension of the customer base.
4.2 Business Process Improvement
Another promising way to monetize data is to improve or optimize existing (internal)
business processes. Based on our case base, we discovered several different
opportunities for converting data into real business value in the context of business
process improvement. Enterprises used data to increase process efficiency (Lufthansa
[22], Saarstahl AG [1], Thyssenkrupp [16], UPS [34], Suning Commerce Group [15]),
to improve process transparency (Lufthansa [22]), to support information (7-Eleven
Japan [11]]), and to monitor performance (Deutsche Bank [34]). Furthermore, data
was used to improve safety within business processes (UPS [34]).
Economic benefits comprise cost reduction, increase of sales and productivity,
detection of inconsistencies and fraud or decision support.
4.3 Product / Service Innovation
Creating completely new offerings to customers based on data is a strategy companies
apply regularly. This strategy comprises new products and services that are either fed
with data or created newly based on insights from data. For instance, IBM created a
new service around sensors in homes of elderly people to measure basic vitals and
monitor their daily operations [30]. Based on this data, anomalies can be detected and
concerned services notified, reducing assistance costs up to 30 percent [30]. Other
companies use data to create innovative services for customers extending the core
offerings (Netflix [21], DHL [33]), especially in the case of product maintenance
(Siemens [16], Thyssenkrupp [16], Pirelli [14]).
Economic benefits comprise the creation of new revenue streams and new
business segments.
4.4 Product / Service Optimization
Using data to optimize or improve existing offerings is one of the basic strategies
to create value from data. In such cases, businesses might collect data from their
products or services (Pirelli [14]), through additional efforts (Nike+ [17], Haier Group
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Corporation [15]), or source it externally, e.g. from third parties (Zara [35]). Products
and services can be improved constantly based on continuous data collection (Ford
[29]). Furthermore, existing (customer) services can be improved by augmenting
customer profile information with external data to improve interactions and decipher
customers' needs (Lufthansa [22]).
Economic benefits comprise the improvement of customer experience, reputation
and increase of sales.
4.5 Data Insights Sale
Selling information / knowledge derived through any processed step of insights
making is a strategy we observed in 17 cases. Insights from data can not only be
derived from analytics (TrendSpottr [12], Asiakastieto [26]), but also from different
ways of visualization, as we found in the cases of Sendify [12], Flo Apps [26] and
Olery [35]. Other examples of this strategy meeting a B2C context abound in
commonly used comparison portals (DealAngel [12], Car Spotter [27]).
Economic benefits comprise the creation of new revenue streams and business
segments.
4.6 Contextualization
Strategic use of certain kinds of data can create additional value to customers or
internal processes in specific contexts. Context-related data include, for example,
weather, social media, location-based and domain-specific data. In contrast to
individualization, contextualization does not rely on individual data and profiles, but
focuses on the contextual aspects rather than the distinct or personal. Examples for
contextualization of data to create economic benefits can be found in dynamic and
contextual pricing as confirmed by the cases of the Major League Baseball [29] and
Staples [1]. Walmart in contrast uses contextualized data, to recommend consumers
products that were often bought together [34].
Economic benefits comprise the optimization of prices and increase of sales.
4.7 Individualization
The individualization of certain aspects of a company’s value proposition based on
data is another source for generating additional value. This strategy is based on the
use of data, which is linked to certain customer profiles, making it possible to create
value for consumers and businesses on an individual basis. This strategy is often
applied in the area of marketing (Baixing [18], eBay [34]), and especially through
individual recommendations (Amazon [35], Netflix [21], eBay [34]) or personalized
advertisements (Runtastic [17], Facebook [19]). Another possibility is to individualize
product or service offerings to business partners based on individual customer data
(Daimler FleetBoard [1]).
Economic benefits comprise the improvement of customer experience, increase of
sales through personalized marketing or customization of products and services.
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Table 2. Data monetization strategies
Data Monetization
Strategy
Short Description
Excerpt from the Case
Base
1. ASSET SALE
Economic benefits result from additional
revenue based on provision/sale of data or
from granted access to (own) data.
Strava [17], Factual [32],
Verizon Wireless [1],
LinkedIn [19], Twitter [17]
2. BUSINESS
PROCESS
IMPROVEMENT
Value from data is created through
improvement or optimization of internal
business processes.
Lufthansa [22], Saarstahl AG
[1], Thyssenkrupp [16], UPS
[34], Deutsche Bank [34]
3. PRODUCT /
SERVICE
INNOVATION
Extending the existing range of offerings to
customers with new products or services
based on data.
IBM [30], Rolls Royce
Aircraft [1], DHL [33],
Netflix [29], Siemens [16]
4. PRODUCT /
SERVICE
OPTIMIZATION
Optimizing existing products or services by
utilizing data.
Ford [29], Lufthansa [22],
Thyssenkrupp [16], Zara
[35], Pirelli [14]
5. DATA INSIGHTS
SALE
Selling information/knowledge derived
through any processed step of insights
making (analytics, visualization, etc.) based
on data.
Olery [35], Sendify[12],
DealAngel [12], Asiakastieto
[26]
6. CONTEXTUA-
LIZATION
Using context-based data to generate
economic benefits.
Major League Baseball [29],
Staples [1], Walmart [34]
7. INDIVIDUA-
LIZATION
Customer linked data is used to
individualize certain aspects of a
company’s value proposition on an
individual basis.
eBay [34], Daimler
FleetBoard [1], Netflix [29]
8. BUILD &
STRENGTHEN
CUSTOMER
RELATIONSHIP
Data is leveraged to create and maintain
lasting relationships with customers.
Rolls Royce Aircraft [1],
Wells Fargo [34], NBC
Universal [11], DHL[33]
9. STRATEGICALLY
OPENING DATA
Opening (internal) data to business partners
or 3rd parties for value co-creation,
increased visibility or other advantages.
DrugCo (anonymized) [2],
APIbank (anonymized) [23],
Helsingin Sanomat [26]
10. DATA
ENRICHMENT
Data enrichment means any aggregation of
internal or external data sources as well as
further processes of transformation or
cleaning of data for economic benefits.
InfoGroup [32], Gnip [35],
Zara [35], Walmart [34],
DealAngel [12]
11. DATA
BARTERING
Data bartering occurs when (own) data is
exchanged in return for valuable assets
such as tools, services or data.
Asiakastieto [26], Factual
[32], retailers (anonymized)
[11]
12. DATA PRIVACY
AND CONTROL
GUARANTEE
Data that is derived from the interaction
with customers is monetized by
guaranteeing of not using them or
providing control over the data to the
customer.
DuckDuckGo [38], Cozy
[38], Meeco [38], Gigya [20]
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4.8 Build and Strengthen Customer Relationship
Firms can leverage data to create and maintain lasting relationships with customers.
For this purpose, firms regularly focus on leveraging data to obtain customer behavior
and customer needs (NBC Universal [11], Lufthansa [22]). Moreover, in many cases
data is used to infuse innovative services and create repeat purchases. For example,
companies bind customers through additional data-based services like efficiency
monitoring (Rolls Royce Aircraft [1]) or ensuring customers the lowest prices by
monitoring competitors (Walmart [34]). If specific or unique data, insights or data-
driven services become essential to a customer's business model, a data -induced
‘vendor lock-in’ effect may occur.
Economic benefits comprise the optimization of customer acquisition and
retention strategies, increased customer trust and confidence, enhancement of
customer loyalty and satisfaction and creation of recurring revenue.
4.9 Strategically Opening Data
Strategically opening data to business partners is a promising source of economic
benefits that companies intuitively hesitate to pursue. Companies use this strategy for
granting third-parties and suppliers access to specific parts of their business data
landscape via APIs [2, 23, 26]. Third parties use this data to build additional products
and services or to align their processes to extend value propositions and create
ecosystems and networks.
Economic benefits comprise leveraging business partner capabilities,
enhancement of value co-creation, new partnerships, cost-sharing and increase of
visibility.
4.10 Data Enrichment
Data enrichment is the aggregation of internal or external data sources as well as any
subsequent processes of transformation or cleansing of data. Aggregating or
transforming data sources already may provide economic value, and represents the
predominant value proposition of several case companies (InfoGroup [32], Gnip
[35]). Consolidating the data landscape is especially useful among large companies by
making important internal data and information directly available to other
departments of the company [2].
In most cases, however, this strategy constitutes an essential and preliminary stage
of further purposes and is therefore frequently combined with other strategies in the
analyzed data set (See 5.1 Combination and hierarchies of data monetization
strategies for a more detailed discussion of this aspect).
Economic benefits comprise the improvement of (internal) value creation,
increase in the availability of data / information or extension and verification of
datasets.
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4.11 Data Bartering
Data bartering occurs when a company exchanges data in return for valuable assets.
Hence, data is used for quantifiable benefits through exchanging them for other data,
insights, tools and services or special deals [11].
Examples include providers of data and analytics that individually grant discounts
based on contributions to the companies database (Factual [32]) or by directly
exchanging data with business partners in finance (Asiakastieto [26]). Furthermore,
data bartering frequently occurs in the retail industry, when point-of-sales data is
bartered, for example, for demographic information or analytics software [11].
Economic benefits comprise the exchanged value in the form of, for example,
tools, services or data.
4.12 Data Privacy and Control Guarantee
From our findings, we derived an emerging strategy for monetizing data that
constitutes a contemporary “anti-pattern” to existing strategies. Businesses that collect
data from interactions with their customers (frequently B2C) can retrieve economic
benefits from this data by not using it or by granting customers full control over their
data.
Therefore, through guaranteeing privacy and control, recorded customer data is
converted into valuable benefits such as increased customer loyalty or market share.
Cases can be found predominantly within web-based services such as search engines
(DuckDuckGo [38]), private data management tools (Meeco [38]), or customer
identity management platforms (Gigya [20]).
Economic benefits comprise the increase of market share, customer loyalty and
better image and reputation.
5 Discussion
This section discusses further insights from our research and analysis of the case base.
Additional findings include the combination of data monetization strategies and
aspects of giving away data for monetization, which, according to our case base,
happens only in platform ecosystem settings.
5.1 Combination and Hierarchies of Data Monetization Strategies
The monetization strategies presented in Section 4 are not mutually exclusive, but
occurred in different combinations throughout the use cases. A majority of cases
showed a combination of different strategies. Data Enrichment was the strategy we
were able to identify in 46 of the cases. Further, the strategy has been used in
combination with other strategies most often. In many cases, data is enriched initially,
combining different data types, usually from different (i.e., internal and external)
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sources. Therefore, building capabilities to collect, store, combine and handle data can
be labelled as a key to monetizing data.
Another aspect we encountered in cases where we identified the combination of
monetization strategies, is a certain hierarchy within the monetization strategies. One
strategy often became the basis for other strategies to be combined with. For further
advancement of the field, future studies may uncover certain archetypes, dominant
combinations or a comprehensive taxonomy of data monetization strategies.
5.2 Openness of Data in Platform Ecosystems
Monetizing data by giving it away for the use by third parties is one strategy we
identified from our case base. We found this strategy mainly in the context of digital
platforms, which use data as a means for value-co-creation and managing platform
complementors. When sharing data with partners, thus opening their digital platforms,
owners seek to stimulate growth in their ecosystems[45, 46]. In this context, data is
also considered a boundary resource, which can enable value generation and capture
within an ecosystem [47]. Further, governance of data within an ecosystem is an issue
that arises when opening proprietary data to third parties. Lee et al. (2018) present a
contingency-based approach for data governance in platform ecosystems, giving
proof that the governance and openness of data relies on a multitude of contingencies
[48]. Platform owners need to consider these contingencies when opening up their
data for future monetization.
6 Future Research
Our findings suggest further studies of data monetization and the use of data to create
and capture additional value. We will outline these avenues in the following.
As mentioned in Section 5.1, the initial set of data monetization strategies found in
this study could be the basis for further theoretical contributions. First, we propose the
development of a well-founded taxonomy for data monetization in companies in order
to structure multiple ways of data monetization. Furthermore, future research could
work on additional factors concerning data monetization strategies such as success or
degree of complexity to support practical adoption on data monetization.
In the case that data is treated as an asset, our analysis showed that there exists a
wide range of different approaches for releasing data to customers and capturing
value. For example, different ways for freemium revenue models occurred, where
mostly the "premium" part differed in multiple ways (e.g., parts of the data or stale
data was free). Future research could tackle this issue by investigating revenue models
on data as an asset in detail.
The issue of data privacy has gained huge attention in recent years. Regulators are
introducing measures to oppose recurring numbers of privacy violations. Specifically,
the European Union and the United States Congress have begun to address these
issues. From the perspective of this work, we expect strategies for data monetization
to focus even more on issues around privacy, ownership and control of data. First,
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future work may assess possibilities for new, innovative strategies to monetize data in
the context of data privacy. Second, based on these possibilities, conceptualizing
monetization strategies is an important task for future work.
Due to the case base underlying our study, this paper described data monetization
from a business perspective. Nonetheless, considering the view of private users is also
worthwhile, since some of our derived strategies already touch on this perspective
(e.g., bartering private data for using apps and services or selling private data as an
asset). Future research may investigate the user-centric perspective to derive further
insights as well as interrelations with the company perspective. This research avenue
could further educate users on their possibilities and encourage them to handle and
use their data more purposefully, strengthening users’ data literacy.
The context of monetizing data by Strategically Opening Data opens promising
paths for future research. Touching on aspects of value co-creation, boundary
resources, and data governance, future work may focus on strategic management of
open data and its implementation. Giving away valuable assets to third parties while
generating additional value creates a natural tension that warrants examination in
more detail. We further encourage researchers to investigate this strategy in the
context of platform growth and the role of opening data to increase participation of
third parties in platform ecosystems. Generally, giving away intellectual property,
such as data within software ecosystems for purposes of increasing adoption yields
abundant opportunities for further investigation.
7 Conclusion
The goal of this paper was to derive generic data monetization strategies that have
proven effective in practice. Drawing upon 102 business cases, we therefore
examined recent approaches and strategies to convert business-related data into
economic benefits, with the goal of creating a reliable representation of the state of
the art in data monetization. Our findings have implications for theory and practice:
First, they support theoretical understanding in this rapidly changing field of research
by providing an empirically developed foundation for data monetization strategies
and their economic benefits. Accordingly, we address recent calls for extending prior
work on the field and propose several starting points for further investigation of the
topic. Second, this study bundles various possibilities to engage in the important issue
of utilizing available data, providing managers an overview of strategic options. In
conclusion, this work offers decision-makers appropriate guidance to take a step
towards monetizing their data resources.
8 Acknowledgments
This research has been supported by the Bavarian Ministry of Economic Affairs,
Regional Development and Energy (grant. No. IUK547-001 - BayernCloud).
https://doi.org/10.30844/wi_2020_j3-baecker

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