The amount of data vehicles generate is set to explode. This brings a lot of opportunities, but also great responsibilities for those who want to use car data.
When establishing new products and services, the most important aspect is that customers see their benefits right away. In a 2018 report by McKinsey, the vast majority of industry leaders indicated that communucating the value of Car Data Monetization is important, and a staggering 84 percent called it their "biggest chalenge" and "highly relevant".
On the one hand, this might be due to the lack of blueprints. Most car data monetization projects are entering completely new areas. Vehicle manufacturers have always considered enhancements of already established and proven business models as more secure than developing something from scratch.
Furthermore, it is extremely important for companies to have a uniform understanding of the customers and their needs in order to derive a strong value proposition. Last but not least, one needs to answer the question of how much is the data actually worth. The value of data heavily depends on the use case. No one knows yet what a fair price is and a lot of monetization projects are still experimenting to find the right balance.
When it comes to the development of connected services, stronger capabilities around internal and external collaboration are needed, along with the agility of faster product iterations, such as rapid prototyping. Some of these skills collide with prevailing attitudes that have been cultivated over decades in the automotive industry, such as a strong risk aversion.
Companies have to design teams with a product-first mindset instead of being attached to multi-year IT projects. Since teams work better in small formations (for agile teams a size of 7 +/-2 is recommended), it is difficult to staff each team with the same amount of functional and domain experts. Due to the technical complexity of the projects, there is a shortage of specialists that can effectively integrate, manage and develop data and connected services.
There are plenty of issues and potential sources of error around data quality, connectivity and data availability. Your data isn't where it needs to be. Your data is there, but it's late. There is no clear common understanding of your data. Challenges like these are particularly strong in the automotive, transportation and logistics sectors.
As an example: A car manufacturer implements around 1000 unique data points around a vehicle and a connected car collects around 200 to 300 different data attributes. Some of the data is important for CRM systems, some for manufacturing, others for product development and testing.
Since data is streaming in from so many internal and external sources and in different formats, it’s getting increasingly complex to standardize and harmonize data. In another Xapix blog post, we delve deeper into the topic of business data standardization and present some viable solutions.
According to the McKinsey study, the development of car-data-enabled features requires skills and resources that no single company has. To span this gap, collaboration will be key—unfortunately, managing external partnerships is a science in itself.
“No single player can succeed on a stand-alone basis in establishing the digital ecosystem around the car, and multiple stakeholders need to work together.”
– A senior executive at a premium OEM
McKinsey names other challenges like data sharing regulations that constrain collaborations, different innovation speeds of partners, the inability to create a shared vision of the source of value and limited managerial commitment to explore unproven business models amongst others.
Data privacy and compliance is deemed critical in order to enable new features and services from car data. Apart from the unresolved question of data ownership, there are several other pitfalls. This is partly due to the fact that some legislation is unclear and the implementation of newer, more precise laws is very slow.
The introduction of the GDPR brought some clarity, but it needs to be further developed and understood around specific industry use cases. The complexity is ever increasing as individual jurisdictions are coming up with their own regulations. GDPR is still considered to be the gold standard and many other laws are modeled after. But for example, California’s CCPA differs heavily in the way it designates user and business rights. Global companies need to take these differences into account and need the ability to adapt these into their systems.
Another point is that there are dangerous misconceptions that put whole projects at risk, for example around the concept of data anonymization. Since data quality suffers during anonymization, data is often simply pseudonymized in projects. This is a huge difference, since only true anonymized data is exempt from GDPR, and can thus freely shared and monetized with third parties, whereas pseudonymized data still has to be treated and protected like personal data.
The market around car data monetization is highly competitive and in constant motion. Someone somewhere is always building the next big thing. Here at Xapix, we think that it is important to tackle the challenges named above head on and work with what is possible now. Don’t become too paralyzed in the face of complexity. Especially around challenges with data quality, data governance and standardization or freeing up developer resources through low-code data integration - we can help if you tell us more about your situation. So don’t hesitate and get in touch with us!