Challenges in Data Governance for the Automotive Industry

The context

Data is at the heart of innovation for the automotive industry in the 21st century.

Connected and automated vehicles as well and new urban mobility generate and demand huge amounts of data. This new paradigm creates new challenges in data governance that we, part of the automotive industry, have to manage with a high degree of efficiency and effectiveness.

These challenges are related to data categories, regulations, data privacy, data ownership, data standards and data exploitation.

The players

Currently, the entire automotive industry: OEMs, new players and traditional manufacturers are converting from this traditional industry to tech & data companies, investing in technicians, development and infrastructure to meet these challenges. But not only the automotive industry, but also governments are investing large amounts of resources in these areas.

The challenges

The adoption of cloud-native and IoT solutions is helping us with these challenges.

Leading Cloud providers provide us with parallel processing, storage and analytics solutions. These solutions are key to addressing some of the challenges ahead.

But there are other points we have to face, and these points are not technical; namely, regulations, ownership and privacy.

At Applus+IDIADA, we are deal with each challenge with a different approach depending on the context and the difficulty of the user case.

Talking about Regarding vehicle data categories, we mean where the data come from: traffic, usage of the vehicle (miles, temperature…), non-anonymous data (or personal data like , phone contacts). These groups of data have different origins and different contexts. The difficulty comes from storing large amounts of data from sensors, the security and capacity of the networks, and finally, the privacy of the data.

The regulation of how and what kind of data is acquired and stored is very challenging because it depends on the vehicle market or the country. A lot of effort is invested in complying with these regulations. Data privacy and ownership are also included in that point, and not only require specific efforts in understanding and implementing the various processes of acquisition and storage, but also in analysing, anonymizing and sharing results.

When it comes to sharing information, new opportunities appear. Data standardization is mandatory in order to share information efficiently. Sharing data can be as simple as sharing a simple CSV field or exposing to consumers large streams of data with different technologies: streams, APIs, file-repositories…; but not only the way of exposing this data, but also the content is a challenge, as real-time data is very different from historical or reduced data.

In conclusion, these challenges are the tip of the iceberg. New challenges will arise as the industry grows and the adopts IoT, autonomous driving and urban mobility.