Few question the premise that combining data with statistical models can provide value for both the automotive industry and consumers. The idea is that machine learning will lead to insights that allow automakers to know more about their customers, resulting in win/win outcomes for manufacturers and consumers alike.
Thus, the prospect of autonomous vehicles generating 4TB of data per day by 2020 is causing high levels of excitement, and numerous related use cases show the potential of new revenue streams for automotive OEMs and Tier-1s.
Examples include an improved driving experience, targeted special offers, and preventive maintenance – all ‘driven’ by a plethora of IoT generated data. The number of documented data-driven use cases in the public domain is high. So far, so good – at least in principle.
However, there is still an open question as to who actually owns such collected data and whether consumers will have a say on potentially declining and/or rescinding their generated data to OEMs. Essentially, we need to ask who owns the data, and how will it be used?
This suggests that the viability of a data-driven future is dependent on ethical considerations, clarity, and transparency. How will the current lack of industry standards affect the risk/reward properties of data collection initiatives?
Differences in legislation across countries could also throw a spanner in the works. Imagine a scenario where data ownership is determined by location. What happens when you drive across a border between two countries with differing data laws, restrictions and protocols?
The automotive industry and consumers would stand to benefit from a global, industry-wide initiative that addresses consumers’ data privacy concerns, while also rewarding OEMs and Tier-1s for providing new services.
Resolution of such pivotal questions will determine whether the selling of driver data to third parties can be successfully married with the commercial viability of pursuing personalized data-driven mobility solutions.