Data monetization: The missed gold rush of the software-defined vehicle

Ya, right - data is the new gold. Monetization of vehicle data was destined to be the next gold mine. Except, so far this has not come true. McKinsey's prediction from 2016 did not materialize. 5 years later a new report looked at it again with three hyptotheses why this is so. The short summary:

• The offered services were not interesting enough to the customer because user experience was tedious or the same services are available for free in the smartphone world making pricey subscriptions unattractive.
• The organizations have not been rebuild to reflect possible value streams via data. Organization are still structured along vehicle function silos. Cross-functional data monetization is close to impossible.
• Solutions are one-offs. Pay-per-use insurance between one OEM and one insurance company; cooperation with a single gas station brand. Scaling across partners or better across OEMs is not established leading to fragmented solutions.

At the same time, Wired recently reported what kind of data popular vehicle brands in the US already collect today - and it is a lot.

It seems like OEMs collect a lot of data but cannot really make use of it today.

Let's drill down on this a bit more. There are essentially three different categories for data monetization in a vehicle

1. Optimization of vehicle development or sales - Using collected data on usage of features to steer development and sales. In this case the OEM is the data customer. We do see these use-cases e.g. in the development of autonomous driving use-cases to collect training data.
2. Establishing services around a vehicle. This is the broadest field of use-cases. Using the vehicle as a data collection device and to either offer this data to third parties for sale (e.g. using a vehicle as a micro-weather station) or to serve as a platform between end customers and other service providers (e.g. pay-per-use insurance) as a two-sided business model. In either case the data customer is a third party company.
3. Managing commercial vehicle fleets - Owners and operators of commercial vehicle fleets can obtain significant cost advantages out of collecting data of the vehicles, e.g. for predictive maintenance or optimized planning. In this case the owner of the vehicle is the data customer.

Interestingly, there are almost no use-cases listed where the driver (or a private end customer) is the data customer.

Looking at these categories for monetization it becomes obvious why OEMs are seemingly failing on data monetization.

In first the category some companies are succeeding. However, it is not monetization in terms of revenue but in terms of gained efficiency. It barely becomes visible.

The second category is essentially trying to build a market place for vehicle data. The use of OEM-specific APIs and data formats creates fragmentation in this space. On top, as McKinsey pointed out OEMs are facing additional fragmentation due to siloed organization structures. Fragmentation is the primary issue when attempting to build any ecosystem. The best job in the mobility sector probably is again ... *drumm roll* ... Google. They aggregate traffic information via movement of smartphones. No OEM-induced data market fragmentation. On top of this, no OEM has build an organization that is actually able to create meaninful revenues out of this data. Selling data services requires a different sales and distribution channels than vehicles. OEMs have failed to establish working partnerships with relevant players int his field.

The protagonist for the third category is not the OEM but a fleet operator. Think about fleets of trucks or construction machinery or school busses or rental cars. If you can increase the utilization of your vehicles, you can significantly save cost. The problem is that in many cases these fleets are consisting of vehicles from a single OEM. Therefore, fragmentation of the data ecosystem again kills your use-case. No wonder the market of fleet management is still dominated by add-on solutions. As the OEMs do not manage to normalize data and offer data over standard APIs with a standard pattern of access permission, fleet operators have to plug in another box.

In the end it is fragmentation that kills the market. Of course some OEMs may argue they are big enough, I am convinced though that open standards - or better open implementations - are the only way to overcome the fragmentation and leverage the potential of the collected data.