Is rise of data and AI regulations a challenge or an opportunity?

Right To Repair and EU Data Act as a step towards data monetization.

Legislators try to shape the future

In recent years the automotive market has witnessed a growing amount of laws and regulations protecting customers across various markets. At the forefront of such legislation is the European Union, where the most significant disruption for modern software-defined vehicles come from the EU Data Act and EU AI Act. The legislation aims to control the use of AI and to make sure that the equipment/vehicle owner is also the owner of the data generated by using the device. The vehicle owner can decide to share the data with any 3rd party he wants, effectively opening the data market for repair shops, custom applications, usage-based insurance or fleet management.

Across the Atlantic, in the United States, there is a strong movement called “Right to Repair”, which effectively tries to open the market of 3rd party repair of all customer devices and appliances. This also includes access to the data generated by the vehicle. While the federal legislation is not there, there are two states that that stand out in terms of their approach to Right to Repair in the automotive industry – Massachusetts and Maine.

Both states have a very different approach, with Maine leaning towards an independent entity and platform for sharing information (which as of now does not exist) and Massachusets towards OEMs creating their own platforms. With numerous active litigations, including lawsuits OEMs vs State, it’s hard to judge what will be the final enforceable version of the legislation.

The current situation

Both pieces of legislation impose a penalty when it’s not fulfilled – severe in the case of EDA (while not final, the fines are expected to be substantial, potentially reaching up to €20 million or 4% of total worldwide annual turnover!), and slightly lower for state Right to Repair (for civil law suits it may be around $1000 per VIN per day, or in Massachusets $10.000 per violation).

The approach taken by the OEMs to tackle this fact varies greatly. In the EU most of the OEMs either reused existing software or build/procured new systems to fulfill the new regulation. In the USA, because of the smaller impact, there are two approaches: Subaru and Kia in 2022 decided to just disable their connected services (Starlink and Kia Connect respectively) in states with strict legislation. Others decided to either take part in litigation, or just ignore the law and wait. Lately federal judges decided in favor of the state, making the situation of OEMs even harder.

Data is a crucial asset in today’s world

Digital services, telematics, and in general data are extremely important assets. This has been true for years in e-commerce, where we have seen years of tracking, cookies and other means to identify customers behavior. The same applies to telemetry data from the vehicle. Telemetry data is used to repair vehicles, to design better features and services offering for existing and new models, identify market trends, support upselling, lay out and optimize charging network, train AI models, and more. The list never ends.

Data is collected everywhere. And in a lot of cases stored everywhere. The sales department has its own CRM, telemetry data is stored in a data lake, the mobile app has its own database. Data is siloed and dispersed, making it difficult to locate and use effectively.

Data platform importance

To solve the problem with both mentioned legislations you need a data sharing platform. The platform is required to manage the data owner consent, enable collection of data in single place and sharing with either data owner, or 3rd party. While allowing to be compliant with upcoming legislation, it also helps with identifying the location of different data points, describing it and making available in single place – allowing to have a better use of existing datasets.

A data platform like Grape Up Databoostr helps you quickly become compliant, while our experienced team can help you find, analyze, prepare and integrate various data sources into the systems, and at the same time navigate the legal and business requirements of the system.

Cost of becoming compliant

Building a data streaming platform comes at the cost. Although not terribly expensive, platform requires investment which does not immediately seem useful from a business perspective. Let’s then now explore the possibilities of recouping the investment.

• You can use the same data sharing platform to sell the data, even reusing the mechanism used to get user consent for sharing the data. For B2B use cases, the mechanism is not required.
• Legislation mainly mandates to share data “as is”, which means raw, unprocessed data. Any derived data, like predictive maintenance calculation from AI algorithms, proprietary incident detection systems, or any data that is processed by OEM. This allows not just to put a price tag on data point, but also to charge more due to additional work required to build analytics models.
• You can share the anonymized datasets, which then can be used to train AI models, identify EVs charging patterns, or plan marketing campaigns.
• And lastly, EU Data Act allows to charge fair amount for sharing the data, to recoup the cost of building and maintaining the platform. The allowed price depends on the requestor, where enterprises can be charged with a margin, and the data owner should be able to get data for free.

We can see that there are numerous ways to recoup the cost of building the platform. This is especially important as the platform might be required to fulfill certain regulations, and procuring the system is required, not optional.

The power of scale in data monetization

As we now know, building a data streaming platform is more of a necessity, than an option, but there is a way to change the problem into an opportunity. Let’s see if the opportunity is worth the struggle.

We can begin with dividing the data into two types – raw and derived. And let’s put a price tag on both to make the calculation easier. To further make our case easier to calculate and visualize, I went to high-mobility and checked current pricing for various brands, and took the average of lower prices.

The raw data in our example will be $3 per VIN per month, and derived data will be $5 per VIN per month. In reality the prices can be higher and associated with selected data package (the data from powertrain will be different from chassis data).

Now let’s assume we start the first year with a very small fleet, like the one purchased for sales representatives by two or three enterprises – 30k of vehicles. Next year we will add a leasing company which will increase the number to 80k of vehicles, and in 5 years we will have 200k VINs/month with subscription.

	Year 1 / 30 000 VINs	Year 2 / 80 000 VINs	Year 3 / 100 000 VINs	Year 5 / 200 000 VINs
Raw data ($3) (monthly)	$ 90,000.00	$ 240,000.00	$ 300,000.00	$ 600,000.00
Derived data ($5) (monthly)	$ 150,000.00	$ 400,000.00	$ 500,000.00	$ 1,000,000.00
Raw data ($3) (yearly)	$ 1,080,000.00	$ 2,880,000.00	$ 3,600,000.00	$ 7,200,000.00
Derived data ($5) (yearly)	$ 1,800,000.00	$ 4,800,000.00	$ 6,000,000.00	$ 12,000,000.00
Total revenue (monthly)	$ 240,000.00	$ 640,000.00	$ 800,000.00	$ 1,600,000.00
Total revenue (yearly)	$ 2,880,000.00	$ 7,680,000.00	$ 9,600,000.00	$ 19,200,000.00

Of course, this represents just a conservative projection, which assumes rather small usage of the system and slow growth, and exclusive subscription to VIN (in reality the same VIN data can be shared to an insurance company, leasing company, and rental company).

This is constant additional revenue stream, which can be created along the way of fulfilling the data privacy and sharing regulations.

Factors influencing the value

$3 per VIN per month may initially appear modest. Of course with the effect of scale we have seen before, it becomes significant, but what are the factors which influence the price tag you can put on your data?

• Data quality and veracity – the better quality of data you have, the less data engineering is required on the customer side to integrate it into their systems.
• Data availability (real-time versus historical datasets) – in most cases real-time data will be more valuable – especially when the location of the vehicle is important.
• Data variety – more variety of data can be a factor influencing the value, but more importantly is to have the core data (like location and lock state). Missing core data will reduce the value greatly.
• Legality and ethics – the data can only be made available with the owner consent. That’s why consent management systems like the ones required by EDA are important. 

What is required

To monetize the data you need a platform, like Grape Up’s Databoostr. This platform should be integrated into various data sources in the company, making sure that data is streamed in a close to real-time way. This aspect is important, as quite a lot of modern use cases (like Fleet Management System) requires data to be fresh.

Next step is to create pricing strategy and identify customers, who are willing to pay for the data. It is a good start to ask the business development department if there are customers who already asked for data access, or even required to have this feature before they invest in bigger fleet.

The final step would be to identify the opportunities to further increase revenue, by adding additional data points for which customers are willing to pay extra.

Summary

Ultimately, data is no longer a byproduct of connected vehicles – it is a strategic asset. By adopting platforms like Grape Up’s Databoostr, OEMs can not only meet regulatory requirements but also position themselves to capitalize on the growing market for automotive data. With the right strategy, what begins as a compliance necessity can evolve into a long-term competitive advantage.