The use of data-driven business models has become an increasingly trending activity in business, and is widely recognized in the field of digital transformation. Well-known cases of Google’s People Analytics Department, Amazon’s customer recommendations and the growth of Southwest Airlines’ customer loyalty after embracing a data-driven culture has inspired managers to engage in data-driven business models. On Thursday the 5th of April we have a workshop on Data Driven Business Model Innovation that will be given by Professor Frans Feldberg.
The principle behind Data-driven-thinking sounds straight-forward; Making decisions about the problem at hand based on the data that is available. Data-driven thinking has found its way into organizations as an aid in the decision-making process, and more and more organizations are recognizing the potential value of data-driven business models. It requires organizations to establish proper leadership to embrace a data-driven revolution, adaptive talent management that is aimed at attracting the best data scientist, and to change the culture of “What do we think?” to “What do we know?” Behind the scenes, infrastructure and technical knowledge is necessary to collect and manage the captured data. These data then need to be visualized in user-friendly dashboards, in order to transform massive amounts of unstructured data into actionable insights. The biggest challenge in making the translation from data to (actionable) insights is to balance data and intuition, and implement the right business processes that can turn data into a valuable asset.
The Data Behind Wearables
One way in which organizations can tap into the potential of data-driven business models is by looking at the data from wearable technologies. While the idea of “wearable” technologies dates back to the 1980s, when Seiko developed watches that were able to interchange data with a computer, the recent years have shown an enormous increase in the use of wearable technologies. Nowadays, wearables can be described as smart electronic devices that can be worn on the body as implants or accessories, such as the Apple Watch, Google Glasses, and the Fitbit.
These technologies not only allow us to things such as texting or calling, they also collect data about for example heart rates, blood pressure, the number of steps, or the number of calories burned. Monitoring and storing these data happens continuously. Analysis of these datasets can generate insights and assumptions about someone’s health or lifestyle. Opportunities to connect these wearables to decision-making processes, for example to alert an individual on high blood pressure are being explored in a variety of industries. For example, in healthcare these datasets could help doctors monitor patients’ cardiovascular conditions.
Data-Driven Health Insurance
Another industry that is exploring the possibilities of using data from wearables for new business models, is the insurance industry. Car insurance companies have already introduced devices that can be installed in the car of an individual to monitor driving behavior, in exchange for a reduction on the insurance premium. ANWB, one of the largest car insurance companies in the Netherlands, has started in 2016 with collecting data on the driving behavior of its customers. Based on their driving behavior, customers were rewarded up to 30% discount on their insurance premium. Via an app, they were able to receive feedback on heir driving behavior based on the collected data. This same idea can be applied to wearables. If you wear an Apple Watch or a Fitbit, it can be rewarded with a discount on your health insurance premium. But why would an insurance company provide a discount in exchange for wearing a Fitbit?
The answer lies in the insurance premium. Traditionally, the insurance premium is the result of a calculation that takes into account factors such as BMI, (non-)smoking, and age. With the help of wearable data, insurance companies have the opportunity to assess for each individual the risk of illness, which can then be reflected in the insurance premium. It this way, insurance companies reason, each individual will receive a premium that is fair in accordance with the possibility of someone needing medical care. Obviously, there are several ethical considerations to be made before we as a society start to accept such practices. But besides fair pricing, insurance companies can also create the opportunity to reduce the number of claims by helping people take better care of their health. When insurance companies have developed the ability to assess the risk of individual health, a possible next step is actively helping individuals reduce their risk of medical problems.
Digital Transformation, Business Model Innovation, and Ethical Considerations
Embracing the opportunities of utilizing wearable data however, organizations have to be structured in a way that they embrace data-driven business models. This means that the insurance company has to be transformed as to what is described by Hartmann et al. (2014) as a ‘data generation and analysis’ company. This type of company is involved with the analysis of data that they themselves generate. In this case, they generate the data by supplying their customers with wearables that can generate the data. The analyses performed on these datasets will generate feedback, which will be reflected in the premium that is set for the customer, and the advice he or she receives on their medical status. In order for this model to be implemented, it requires the insurance companies to adopt a mindset that focuses on effective data generation, analysis and use. This requires not only technical skills and insights, but more importantly the adoption a mindset within the organization that is focused on decision-making based on data. Without it, it is impossible to successfully implement a data-driven business model in an organization.
Next to the potential positive implications, there are other factors to take into consideration. Fair pricing will imply that those people who form the smallest risk, will pay the smallest premium. For those who do form a large risk, this will result in a higher premium. This will eventually create a model of inequality in the insurance premium pricing, which will not be beneficial to society at large. Since the use of wearables is merely starting to develop, we should consider how we deal with these ways of using wearables. When is the use of personal data desirable, and when is it a violation of people’s privacy? What are long term consequences? A society where our health is constantly monitored through wearables? And do these wearables even create a thorough and holistic representation of our health? To what extent do these wearables influence the way we see our own health? And in what ways will people find unexpected ways of using (or influencing) these technologies and the data they collect?
Concluding, while the application of wearables in the insurance industry is just one example of a data-driven business model, it illustrates the potential that adopting such business models can have for organizations and society. It is important to take into account the impact a data-driven business model can have on society and to adopt a critical point of view towards digital innovations. In our upcoming four-day program on digital innovation, we will dive deeper into the ways in which digital innovations may be developed and used, and we will reflect on the (un)expected consequences for people, work, and organizations.