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Software and data management play a vital role in innovation, efficiency, and safety in the fastchanging automotive industry. To adapt their products to strong regulatory requirements and complex customer expectations, automotive companies have become data and software companies. Also, AI comes to disrupt the entire business with new technologies and techniques from technological companies like Google, OpenAI and AWS to accelerate product development as a software or digital product.

There are key aspects to consider when talking about software and data management.

Data Obtained From Testing

Testing is the foundation of automotive development, ensuring that vehicles meet performance, safety, quality, and regulatory requirements, as well as customers’ expectations. At this stage, data obtained from product development (simulation, real-world driving and crash tests) provides invaluable insights into vehicle behaviour, performance and potential improvements.

Testing Data Management and Governance

Data management and governance are essential for the effectiveness of the development processes that ensure compliance with industry regulations.

Data management facilitates the storing and analysis of large amounts of data generated in the testing phases. Governance ensures data integrity, accessibility and traceability enabling experts to take decisions based on valuable information.

Security and Data Privacy

Digital vehicles are here now, evolving how we understand cybersecurity while creating an ecosystem of software and data in a new automotive paradigm. Protecting vehicles from cyber threats is necessary to provide physical safety to the driver and the road environment. Privacy is crucial as vehicles and road infrastructure collect large amounts of sensitive information that must be compliant with regulations.

GDPR and Regulatory Compliance

The European GDPR regulation defines rules on the collection, processing and storage of personal data. Automotive actors must be committed to GDPR requirements when handling customer data, including obtaining explicit consent for data processing, implementing security measures, and providing mechanisms for data access and erasure.

Data Lakes and Data Repositories

Data repositories or data lakes are the base storage for large amounts of data created during product development and during the lifecycle of the vehicle on the public road infrastructure. Datalakes offers a scalable and cost-effective storage solution for handling structured and unstructured data. Data repositories provide structured storage for specific types of data, such as vehicle telemetry and diagnostics, as well as customer profiles. These repositories enable data analysis, predictive modelling, and machine learning applications, driving continuous improvement and innovation in automotive technology.

Machine Learning and AI

Machine learning and AI technologies are revolutionising the automotive business and their software and data products: predictive maintenance, autonomous driving, and personalised user experiences.

Secure development practices are essential when deploying machine learning models in automotive products to mitigate cyber and non-cyber security risks. AI algorithms are here to ensure the safety of AI-driven features.

In conclusion, automotive software, data management, ML, and AI technologies for automotive actors must be a priority for their product development teams. Bringing safety, performance and customers’ personalised experiences always under the GDPR and taking security risks seriously.