The autonomous car has been a subject that has taken center stage for several years now. Automatic piloting, obstacle detection, route programming… So many innovations which are largely based on the collection, analysis and storage of thousands of data. Technologically, industries are ready. But concretely, there are still many brakes before being put on the market. The main challenges of the automotive sector are those of data quality and security. By extension, the question of responsibility in the event of an incident arises.
Can data position itself as a trusted third party?
In the event of an accident caused by an autonomous car, who is responsible? Is it the car manufacturer, the manufacturer of the detection software, the metadata expert, or even an outside phenomenon? The answer is far from obvious. This is the reason why insurance companies remain very cautious, which slows down the deployment of self-driving cars. Today’s vehicles rely more and more on data to function: this is true not only in the context of their use, but also throughout their production. The data collected over the life of a car tells us about its behavior, and it will therefore serve as a trusted third party, because insurance and risk analysis contracts are based on this reading of the data. Data collection and analysis standards are therefore necessary to regulate the automotive industry. What are the criteria that come into play in this data-driven standardization of the sector? We will distinguish two main ones: quality and safety!
Big data must rhyme with big quality
Every microsecond, from the start of a car’s manufacture to its end of life, volumetric data is collected via various sensors and connected objects. All stages of the production process are monitored for extremely fine quality control, mainly in order to avoid recalls and obtain certifications for the vehicles. This very large quantity of data is stored in lakes (data lakes), most often in the Cloud. In the automotive industry, the data landscape is heterogeneous, volumetric and technological. However, to be able to analyze the data, it must be standardized and made intelligible. This is what gives them qualitative value, and allows them to be understood and used by data experts and analysts.
Today, companies in the automotive industry have a double heritage of data: those from Product Life Management tools stored in ERP, and those from sensors and connected objects stored in the Cloud. For the latter, data catalogs provide invaluable help in sorting, understanding, correlating, qualifying and enriching company data. This allows teams to use them in an optimization and performance process.
Data security: can we trust hosts?
Having qualitative data is a key step, which cannot however be freed from the security aspect. Because if data and its quality are now the main competitive advantages of companies, protecting them becomes all the more essential. However, few companies have sufficient infrastructure to store them internally. In ERP, they are ultra secure. But in cloud-hosted data lakes, how can you be sure security is 100% reliable? This is one of the major issues today, and it is not specific to the automotive sector. The whole issue is based on the tamper-proofing of the data. Companies must be able to rely on a partner who cannot alter them, because if the data belongs to the car manufacturer, it is often hosted on platforms over which it has no control. There are therefore many questions around the security, ownership, location and certification of data. So many challenges to structure the industry of tomorrow!
The automotive industry is currently experiencing profound upheavals, both technologically and in terms of its business model, which is gradually shifting from the purchase to the rental of services. The ability of automobile manufacturers to use mature, qualitative and secure data is one of the keys to their competitiveness. Data catalogs play an important role here: that of giving strategic teams access to a growing wealth of data, in order to optimize their use.. Data is then positioned as a full-fledged player and a trusted third party. All that remains is to guarantee its security and compliance. Will we therefore soon see the appearance of certification bodies for the veracity of these data? We can only hope so, to soon come across autonomous cars on our roads!