How OEMs Can Profit from Automotive Data Monetization

We live in a connected world of impregnated data. And this data holds the key to tomorrow’s digital transformation. This even includes the data, from connected autonomous vehicles having hundreds of data sensors containing information on geo-location, vehicle performance, driver behavior, biometrics, and others. So, the previous four-wheeled buggy, which evolved into a central intelligence-driven machine has information from various data points that can be put to good use. Yet, less than 20 percent of these use cases are monetized. While the data monetization of vehicle data continues to be a hyped buzzword, yet not everyone is aware of this possibility nor feels the same about it. For instance, in a 2019 study (2019 Global Automotive Consumer Study) by Deloitte, 79 percent of consumers in China believe increased connectivity will be beneficial, only 35 percent of German consumers feel the same. Furthermore, some experts highlight that participants in any car data market should have the authority to be able to sell and buy car data and not just the OEM leaders. And with particular attention on how this value can be circulated in the market to ensure stakeholders receive their fair share of the profit and data transparency.

According to a report titled ‘Accelerating the car data monetization journey’ from McKinsey, the overall revenue pool from car data monetization at a global scale might add up to US$ 450 - US$ 750 billion by 2030. This growth depends on the ability of market players to use the data generated by cars, drivers, and mobility systems to develop products that create revenue, reduce costs, and enhance safety and security. Meanwhile, Frost & Sullivan believe data monetization opportunities will rise to US$ 33 billion by 2025. Because of such promising aspects, companies, both automotive and non-automotive, are getting drawn to the financial benefit of the available automotive data pool. Though the automotive industry is beginning to capitalize on this newly discovered resource, some industries are already reaping feasible business models built on it.

In the same report, Deloitte states that in a connected vehicle value chain, everyone has a crucial role to play. The generators make end products capable of capturing data; the transmitters help in the safe delivery of the data to a central repository where manipulators collect and combine data from different sources into a usable format. Then the developers, design end-user offerings that leverage the data while providers market the service offerings to both B2C and B2B audiences. Despite the simplicity of this chain process, the problem arises when OEMs have primary access to data and try to control every point in the value chain— even though they may not be well-positioned to do so.

Fortunately, OEMs cannot extract business value from connected vehicle data without applying a successful machine learning model. This approach is compulsory when attempting to leverage asynchronous and highly heterogeneous time-series data at scale to generate predictive insights. In addition, unlike smartphones and computers, automobiles are typically designed as closed systems. This limits accessibility to the data generated or used within it. Hence OEMs have to grant more extensive access to their vehicle data for better machine learning model design. Also, to grasp potential revenue sources and develop according to business models for the automotive context, OEMs need to build partnerships with service providers for interoperability and better assessment of data and cut the costs that may occur if they develop their own machine learning models by hiring best minds. E.g., Ericsson’s Connected Vehicle Marketplace, Delphi’s investment in Otonomo are providing white-label services that OEMs can utilize in case they don’t want to build and manage their own.

Similarly, FordPass by Ford has built platforms and interfaces that service providers can plug into. Even BMW collaborated with IBM and created a white-label service business model where 8.5 million BMW vehicles have built-in telematics systems that operate using the open-source BNW CarData Platform. Once the collected raw information is anonymized, it is then passed into IBM’s BlueMix cloud platform. After that, IBM Watson’s IoT system analyzes the data, draws insights, and makes it available to suitable third-party providers.

Before embarking the data monetization journey, OEMs and other participating companies must take an objective look at where they stand today concerning the benefits of communications, organizational considerations, and partnership challenges that lie ahead. They must build new business models and partnerships focusing on connectivity, autonomy, electrification, and mobility with priority on structured and classified data. For a successful automotive data monetization, the focus should be on customer experience as value-added services become the norm and therefore mine large quantity and unique, high-quality data sets. OEMs should not neglect privacy and right-to-repair legislation and monitoring. They should also ensure interoperability and scalability and enable compatibility with a wide variety of 3rd parties. As new and non-traditional participants enter the ecosystem, this can open up vast avenues of the market and return profits for everyone, resulting in a win-win situation.