An Overview of Existing Approaches and Challenges
Pauline van Rüth and Andreas Hamann
Often titled the “new oil” of today’s economy, data has become an important financial and strategic resource for almost any company in the world. To remain competitive in the digital economy, companies are increasingly seeking to turn their intangible data into real corporate value. The resulting data market is estimated to be at more than €80bn in Europe alone by 2025 (The European Commission 2018; 2020). Yet, there is currently still a lot of uncertainty within companies of how to exactly move forward and make the most use of their respective data. In this article, we therefore provide a review of different approaches that companies can take to monetize their data. We also discuss several core challenges that still exist in that regard and provide managerial recommendations based on our results.
Data Monetization Approaches
Internal Data Monetization
In general, a company can either create value from its data by leveraging it internally or externally. Internal data monetization is usually preferable to start with as it is under the full corporate control and is likely to have an immediate financial impact. A prominent internal use case for data is to track, analyze, and improve business processes. In the manufacturing industry, for instance, equipment sensors can be used to train a machine learning model that allows for more precise and efficient automatic manufacturing than a human could achieve. Similarly, companies can create value by developing completely new goods and services to consumers based on data. These are either fed by data or produced from scratch based on data insights. The continuous collection of data also allows existing products and services to be improved and customer services to be optimized. In a related fashion, data can also help customize on the individual level, e.g., by using online customer profiles for recommendation systems. This may in the end lead to a better customer experience, higher customer loyalty, and increased sales. Finally, internal data can also be monetized by offering additional data-based services or react to identified customer needs, thereby creating the potential to achieve higher customer retention rates and generally improved customer relationships that ultimately also increase corporate sales in the long-run.
Despite the use of internal data has greatly benefited businesses in the past, a recent MIT survey indicates that managers fear that their existing IoT solutions are replicable by other companies within only three years (Wixom 2019). Therefore, sustainable differentiation by only internally monetizing data is likely not possible. To achieve a long-term competitive advantage, moving from internal to additional external data monetization opportunities becomes necessary.
External Data Monetization
Externally monetizing data means moving beyond internal data usage to make the data available to consumers as well as public or private institutions. In essence, data becomes a product on its own and can be either directly or indirectly monetized.
Data selling is the most direct and intuitive of all data monetization approaches, i.e., offering raw aggregated and anonymized data for a nonzero price. Raw data monetization is particularly suitable in sectors with dispersed information and poses no harm to the basic business concept. Possible buyers for raw data are aggregators and brokers like Acxiom or Experian, who gather and analyze data from many sources. However, the vast amount of data that more and more companies nowadays collect also offers possibilities to directly sell data to other companies. This allows the data providers to keep more control over the data they sell and their underlying data product and pricing strategies. Data marketplaces like Dawex and Datarade can facilitate this process and bring buyers and sellers together.
Contrarily, data wrapping, data packaging, and data bartering are monetization options where a data provider offers data as a supplementary product. For instance, to generate additional customer value, corporations can wrap around certain digital information to an existing tangible core product or service. For instance, UPS provides valuable tracking data about each package delivery to the consumer. Data and additional services can also be offered in certain packages to account for different consumer preferences and willingness to pay. Finally, data bartering refers to trading data for valuable assets. This results in data being exchanged for other information, views, software tools, or special agreements for quantifiable advantages.
Next to these direct monetization methods, companies may also share data with other external parties and indirectly generate value from the results of the joint data analysis process. Exchanging data through platforms such asopening data to other firms or third parties strategically allows corporates to develop more value co-creation and increased corporate visibility. For instance, Airbus launched its own open aviation data platform Skywise which provides aircraft performance data to airlines, suppliers, and other stakeholders. Similarly, companies can also provide paid access to platforms for customers as either a core or supplemental service in a Software as a Service (SaaS) model. For instance, a company may monetize its data by providing data-based insights as a service.In doing so, data from multiple sources are combined and internally analyzed to derive relevant suggestions for customers, e.g., in the form of a decision support system. Compared to acquiring raw data, customers can directly use the provided input and are therefore willing to pay a higher price for this kind of data product. Finally, an even more extended analytics-enabled platform can be developed and monetized. In this approach, a company can provide real-time services to customers via the cloud based on internally analyzed data.
Major Data Monetization Challenges
While these approaches may seem promising for any organization to leverage its intangible data into real financial value, there are also several hurdles to be overcome for a successful data monetization strategy and implementation. In the following, we will discuss two major hurdles in more detail. For additional insights into further data monetization issues, please refer to Mirbagherimarvili, Ghanbari, and Rossi (2022).
Privacy and Legal Aspects
Legislations often require companies to keep their data securely and confidential. Some industry-specific legislations even partly ban data from being directly monetized. Thus, it depends largely on the legislative environment to which extent data monetization is possible. This both refers to internal data usage and processing as well as externally monetizing data products. The European General Data Protection Regulation (GDPR), for instance, provides companies that are in direct contact with their customers with certain advantages compared to third-party advertisers. Yet it also still requires those corporations to create internal information governance structures and obtain active consent during data collection to align with its regulatory demands. If confidential data is shared with the permission of the individual in question, the data sharing process must be safeguarded legally in a properly protected manner. In contrast, the United States deal with around 50 independent legislations for data privacy laws, turning the privacy ensuring process for data monetization even more complicated. The heterogeneity in the definition of data and data privacy also further affects public perception of data ownership. It is, thus, common for laws to apply only if the data is in that region whereas the regulation does not apply if an individual transmits sensitive data to a party based outside of that region. Especially with cloud computing and Platform as a Service (PaaS) models, data monetization operates in a field with an increasingly complicated legislative environment.
Data Accessibility and Quality
Since successful data monetization of any kind depends largely on the internal accessibility and quality of the input data,it is one of the greatest challenges faced by corporates. According to a study by Wixom and Ross (2017), only roughly a quarter of all businesses provide employees and consumers with quick access to the data systems they require. This reinforces the need for a well-established data gathering and processing infrastructure in businesses. Otherwise, data may simply not be monetizable if it is not available to the relevant internal stakeholders, e.g., in new product development. However, even if the data is accessible, it further needs to be of reasonable quality to be useful. According to Gartner, poor quality data creates an average financial loss of $15 million per year for an organization (Moore 2018). Bad data quality also negatively affects both internal and external monetization possibilities. In the case of external monetization, for instance, if consumers or business partners realize that the provided data is not useful to them, they will not buy it any longer or already recognize the poor data quality prior purchase. Therefore, to succeed in monetization efforts, at least a part of the data that should be internally processed or externally sold should be of reasonable quality for the involved stakeholders to work with.
Data Value Chains as a Guide to Extract Value from Data
Based on our reviewed monetization possibilities and existing challenges, multiple managerial implications can be derived. First, data value chains can support companies in the internal data value creation process. For instance, the so-called Big Data Value Chain by Faroukhi et al. (2020a) covers the stages of generating, acquiring, pre-processing, storing, analyzing, visualizing, and exposing data. As such, this framework includes repeatable procedures for extracting data’s value throughout its entire lifecycle. This is particularly important for generally understanding the value of the available data sources and lays a base for internal improvements or the design of external data products or services. To this end, note that successful data monetization also often necessitates organizational adjustments and technology improvements.For instance, when a company chooses between adjusting its existing business model with supplementary data service or establishing an entirely new data-based business model, its internal processes, technological setup, and data privacy and governance structures need to be changed accordingly.
Adopt a Product Management Mindset for External Data Monetization
As for other types of products, managers should start thinking strategically of how data can be monetized. This includes strategically thinking about the target group of the provided data and their needs to subsequently develop use cases and tailored data-based products. Especially in markets with competing data sources, managers should particularly clarify where the value of buying their data would be. Simply selling data to other parties may otherwise fail.
Focus on Strong Leadership towards Data Monetization
Finally, data monetization also demands committed leaders who can implement technological and organizational changes to create a significant new value proposition based on data. This is also necessary to avoid a lack of unaccountability with respect to data monetization as it often touches multiple departments. It is in any company’s best interest to have managers that implement best practices based on capturing the correct data and teaching staff how to utilize such to accomplish goals that outperform current objectives.
Data is more and more being viewed as a valuable resource that can be a significant source of competitive advantage and profit. Our review shows that there are various ways in which data can be used to create real financial value. Especially the external data monetization market is expected to grow significantly in the upcoming years and allows companies to access further revenue sources. However, major obstacles like an unclear and shattered legal environment as well as company internal data accessibility and quality issues hinder the current development. Managers can use data value chain models like the one developed by Faroukhi et al. (2020a) as a guideline to move from internally available data to insight and value to be monetized. Furthermore, managers should adopt a product management mindset for data and focus on strong leadership to manage to change to a more data-driven business model.