Companies have always collected data, and these data sources might turn out to be data treasures. However, the advantage of collecting huge amounts of data over decades comes with the disadvantage of prevailing organizational structures that hinder value realization from data. This article describes four approaches established companies can take to monetizing their data. The approaches are drawn from research into the experiences of early-adopting companies and highlight how the environment for data monetization was built.

Direct or Indirect?

When aiming to realize value from data, companies need to mobilize analytical as well as technical capabilities. The former is required to understand the data that are central to the data monetization initiative, and comprises human capabilities including mathematical and business analytical knowledge as well as the ability to use technology. The latter comprises technological components that are needed for data processing, as well as data infrastructure capabilities including hardware, software, and network capabilities. Companies take either direct or indirect approaches to mobilizing the required capabilities.

With indirect approaches, companies start building either analytical (business-centric approach) or technical capabilities (technology-centric approach) first. These companies have the goal of realizing new data-driven business models but begin with projects that gradually develop capabilities (see What are Data-Driven Business Models?). As an explanation for taking the indirect approach, the interviewees from our research emphasized that “improving the existing services, products, and operations is more obvious to the business, easier to grasp, and bears less risk.” Established companies have differential approach options based upon their prior actions. These prior actions may limit or enhance a company’s ability to act directly and indirectly and may drive its ability to focus on analytical or technical matters.

Taking a direct approach usually implies a clear vision and the willingness to invest in data monetization initiatives immediately. Companies taking a direct approach tend to have a greater digital maturity, which allows them to grasp data monetization opportunities. The projects are typically sponsored by the chief executive officer (CEO) and financed by funds for new business opportunities. The new data-driven business model tends to be integrated into the existing organizational structure, or a new company based on a new data-driven business model is established (i.e., a start-up). Our cross-industry research with early adopters has led us to suggest the below four approaches that come out of a broader direct or indirect approach.

Business-Centric Approach

Beginning with analytical capabilities, companies start developing an increased understanding of their data within business units, which usually fund these initiatives. These companies have typically done some previous big data analytics groundwork that enables a business unit to analyze existing data assets. In our research, the use cases for the gradual enhancement of the traditional business model tended to be very detailed, but this allowed the potential data-driven business models to be described at a higher level. The business-centric approach delivered promising results, as the business was involved early on. A business-centric approach was followed by 26% of the cases analyzed in this research.

Technology-Centric Approach

Companies that start building technical capabilities have usually decided to invest in big data analytics platforms as part of their digital initiatives. For them, having the platforms in place is the first step toward the realization of value from data. These companies typically have not done any prior big data analytics groundwork. Our research found that selecting the most sophisticated solution to provide best-in-class technical capabilities was a common strategy. As it turned out for some companies, the implemented solutions were very advanced and not required for the developed use cases. One interviewee highlighted that “the investment was not justified”. A technology-centric approach was identified in 37% of the cases.

Integration Approach

Transforming an organization to integrate a new data-driven business model into existing structures typically requires a clear business opportunity, a common vision, and CEO sponsorship. A large-scale transformation is usually initiated to mobilize required capabilities for data monetization. The initiative typically begins with a data-driven business model design supported by external consultants infusing the ideation process with relevant industry and cross-industry data-driven business model cases. This process often results in a populated business model comprising the relevant facts of the identified business opportunity. Based on this design, a minimum viable product is initiated, presenting early tangible results. Once the minimum viable product reaches a certain maturity, it enters the implementation stage, where the developed product is scaled for commercialization. The integration approach was followed by 16% of the cases.

Start-up Approach

In contrast, the establishment of a data-driven business model through a new company requires a different approach. However, a clear business opportunity and CEO sponsorship are vital here as well. Having a clear understanding of the data and the monetization opportunities they afford, paired with a willingness to invest, allow new revenue streams to be harvested. This boldness leads to the decision to set up a new company. Capabilities are built from scratch. Ideally, the new subsidiary remains completely separate, both conceptually and spatially. Access to the data is usually granted through APIs. The team typically works in a start-up fashion with end-to-end responsibilities, from design to realization of the data monetization initiative. The start-up approach by 5% of the cases.

Companies that fail to take one of the four approaches remain in a pending state with an unclear strategy. These companies invest in use case development within the business units and conduct software selection projects but do not take the next step toward a data monetization initiative. An unclear strategy stage was identified by 16% of the cases analyzed in this study. Decisive factors for the approach taken are the sponsor of the initiative, the funding source, and the motivation to embark on the journey.

Successfully deploying data monetization initiatives requires capability building and sourcing. The cultural transformation aspects are the most challenging (see The Challenge of Becoming a Data-Driven Company). Agile collaboration modes between business and IT are required, using lean start-up methods. Senior sponsorship is vital here, also. Having a clear understanding of the data and their capitalization opportunities, paired with a willingness to invest, allows new revenue streams to be harvested. This boldness leads to the decision to embark on a data monetization journey (see also Mobilizing Capabilities of Data-Driven Culture).

Contact us to learn how these 19 companies adopted data monetization initiatives and how Assefy can be a catalyst for your success.