Business Strategies for Data Monetization: Deriving Insights from Practice

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.

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.

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.