Commercializing Data Assets

Data aggregation is one method of commercializing data assets. Companies may combine internal and external data to create unique, high-value offerings, then sell data extracts to select customers. Data may be sourced from internal operations, customer engagement, external data sources, and synthetic data. This model for monetizing data assets presents few barriers to entry with low costs associated with de-identifying data and making it available to the marketplace. Health care companies, innovators, and researchers are often eager to acquire this data (and willing to pay top dollar), so the data marketplace may provide a significant return on investment.

Market opportunities are varied and wide-ranging. Customers in separate, non-competitive industries value organized and representative data connected to other valuable external data to help drive their insights and analytics development. More prominent market players may offer their data and tools to equip smaller players or new entrants. At the consumer level, personal health data—such as steps per day, heart rate, oxygen level, and other metrics—are an important aspect of an increasing focus on personal health and wellness.

Strategically, organizations must consider the uniqueness of their data, identify potential buyers and competitors, and determine whether selling their data compromises a unique market position or an ethical position necessary for individual protections. Operationally, a data aggregator business model has a comparatively high likelihood of success. The investment necessary is relatively low, and the capabilities required are relatively affordable and accessible in a short period. Data collection and wrangling, curation, interoperability, and light analytics tools may be required.

Organizations can also take data from their core services, operations, and customer interactions to build an insights on-demand offering that serves existing customers or delivers value to new ones. This model builds on existing data assets and newly acquired data to create actionable insights for customers who can use the information to improve care or inform treatments. For example, many national and international organizations do not have access to diverse data sets and the talent of data scientists. Sharing data and resources can help the industry overcome these challenges on a global scale.

Insights generation presents fewer strategic risks of “giving away” data to current and future competitors since all data assets are not shared. However, organizations may face a higher barrier to delivering value to customers. Some physicians or health organizations may be biased toward using their own data or an artificial intelligence and data analytics model that uses their own data. Insights alone may offer less flexibility and be less compelling to some potential users.

In terms of execution, the recruitment of talent and the insight development process can be more complex than in the data aggregation and delivery process. Organizations may need to invest in data mining, AI and machine learning, insight delivery, and predictive and prescriptive modeling capabilities or some combination. As a result, organizations should expect more investment and a longer duration to realizing value and potentially higher returns.

Using a combination of their internal data and external data, companies may be able to launch products to serve existing or new customers. Some organizations may be able to make incremental investments to monetize existing algorithms and increase revenues significantly. These insight-driven products build on existing data and analytics infrastructure and offer holistic solutions to defined client needs. If additional capabilities or skills are needed, they can accelerate product development through an external partnership.

For example, a health care company develops an app using conversational AI to help people with diabetes monitor their blood sugar levels. The application leverages health data from patients, diagnostic insights, and advanced technologies from specialized partners. Once developed, the company can adapt the application and make it available to other health care organizations for people with diabetes.

AI solution design likely presents few risks to existing business lines but will require careful market selection. Organizations will need to carefully vet potential solutions and product ideas, especially those with established competitors. The key capabilities required for success include user research, AI and machine learning capabilities, product development, and product marketing. Additionally, organizations must stand up dedicated, experienced product teams and leadership or identify a partner with complementary capabilities via a joint venture relationship.

The fourth proposed model for monetizing data is a full platform based on data aggregation or insights on-demand offerings. Allowing other developers or partners to host data assets on the platform can help attract more customers and broaden your reach. By bringing together internal and external products and applications, an organization can reap the mutually beneficial outcomes of network effects. Examples of solution platforms are the various app stores and marketplaces that exist today to bring third-party solutions together.

A solution platform business model can significantly enhance existing lines of business by increasing the frequency and breadth of existing customer touchpoints and bringing in new customers who may engage with additional offerings. Platforms require both sellers and buyers, so they benefit from network effects. These can lend significant momentum to a platform once it is up and running. However, a considerable investment may be required until sufficient customer and partner engagement is achieved. Ongoing requirements include platform maintenance, rules and standards definition, algorithm development, and audience development and marketing.