Data-Driven and Cloud-Native Ecosystems in the Automotive Industry

Overview

Journey of BMW Group IT's Cloud Data Hub:

Exploring data platform archetypes, cloud adoption motivations, architecture decisions, and core AI use cases in the automotive industry.

Syllabus

• Introduction to Data-Driven and Cloud-Native Ecosystems
Overview of the Automotive Industry's Digital Transformation
Importance of Data-Driven Strategies in Automotive
• BMW Group IT's Cloud Data Hub
Historical Context and Evolution
Key Strategic Objectives
• Cloud Adoption in Automotive
Motivations for Cloud Transition
Benefits of Cloud Technologies for the Automotive Sector
Challenges and Mitigation Strategies
• Data Platform Archetypes
Understanding Data Platforms: Types and Functions
Examination of Cloud Data Hub's Architecture
Comparative Analysis with Other Industry Platforms
• Architectural Decisions in the Cloud
Design Principles for Scalable Data Solutions
Integration with Existing IT Infrastructure
Considerations for Security and Compliance
• Core AI Use Cases in Automotive
Predictive Maintenance and Vehicle Health Monitoring
Autonomous Driving Systems: Data and AI
Personalized Customer Experience and Telematics
• Ecosystem Collaboration and Innovation
Partnerships and Ecosystem Involvement
Innovation Ecosystem in the Cloud Context
• Case Studies and Practical Applications
Real-World Implementations of AI in Automotive
Analysis of BMW's Cloud Data Hub Use Cases
• Future Trends and Innovations in Cloud and AI
Emerging Technologies and Trends
The Future of Cloud-Native and AI in Automotive
• Conclusion and Key Takeaways
Recap of Key Learning Points
Future Outlook and Industry Impact
• Additional Resources
Suggested Readings and References
Online Forums and Community Engagement

Subjects

Conference Talks