Cloud-Native Data Governance for Next-Generation Automotive Manufacturing: Securing, Managing, and Optimizing Big Data in AI-Driven Production Systems

Abstract

The ongoing digital transformation of automotive manufacturing is unleashing an unprecedented wealth of data. To leverage the added value of this amount of data, it needs to be secured, managed, and optimized. Asset-intensive businesses produce a large amount of data from PLCs, network sensors, SCADA systems, historians, programming cards, image processing, and video systems. Utilizing stacks of artificial intelligence (AI)-driven analyses can help to optimize this data while observing controls supports resilience against the consequences of cybersecurity incidents. Problems include data security, data integrity, data privacy, data protection, connectivity, latency, unreliable data, insufficient data quality, and a lack of understanding to foster data-based business and optimization opportunities. Benefiting from opportunities through time-to-market reductions and enhanced automotive products.
It is the vision of next-generation automotive production systems to lower the data infrastructure stack of manufacturers to profit from cross-company efforts so they can focus on their competitive intelligence within their compartments, as chaperoned by services. Currently, the on-premise paradigm of introducing this stack is turning into a hybrid-cloud approach towards complete cloud-native managed services. Managed services help to hide the underlying technology stacks to uplift overall agility. Industry-accepted rules of practice need to be embedded via platforms and industry services in such a way that automation and decreasing transaction costs enable marketplace power. The text discusses the current challenges in automotive manufacturing and its digital transformation, outlines the opportunities, and opens governing questions that will follow in this course.