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Global Automotive Data Management and Cloud Platform Strategies, 2019

  • Report

  • 65 Pages
  • February 2020
  • Region: Global
  • Frost & Sullivan
  • ID: 5004020

Prioritising Critical Datasets from Non-critical Ones Will Determine Cloud-related Partnerships for OEMs by 2025

The aim of this research study is to give an overview of automotive cloud platforms and the key cloud applications adopted in the automotive market. The study focuses on the different cloud platform strategies adopted by original equipment manufacturers (OEMs), key business models used, key cloud vendors, and their core features. The publisher believes that automotive cloud and data management platforms will form the backbone of digitization initiatives in the industry. However, OEMs are challenged with technical skills in-house and, hence, are dependent on technology partnerships for building automotive cloud platforms.

Automakers understand the importance of managing data and deriving value from them. Cloud platforms are critical to move, store, secure, and index massive volumes of data generated from connected vehicles. However, only purpose-built cloud platforms exist today. Connected services deployment has one and smart manufacturing service has another. Data from connected and autonomous vehicles are collected and processed separately. This situation gives rise to data scalability issues and accommodation of evolving technological changes. Therefore, automakers are seeking technological partners who will not just provide cloud solutions, but have the capabilities to build a unified data management platform with Artificial Intelligence (AI)/Machine Learning (ML) capabilities.

OEMs store connected data on private cloud, fearing security and privacy issues; this will be an expensive option in the long run. Hybrid cloud architecture will be the future for mass automakers. Less critical services will be on the public cloud and the sensitive use cases (such as OEM-specific vehicle testing and development) will be mostly on private cloud. The hybrid cloud approach will be ideal once OEMs build the capabilities that are required to segment the critical datasets from non-critical ones. The publisher believes that 80–85% of the OEMs will transition to hybrid cloud architecture, owing to scale and cost benefits.

Automotive companies should lay down aggressive roadmaps for the development of futuristic data management strategies - including what level of data needs to be collected, how data labeling will happen, and what the level of scaling in future will be - and accordingly set up an ecosystem for storage, processing, and service delivery. Cognitive capabilities should not be used for any specific application, but should be embedded throughout the cloud platforms. Automotive firms can, thereby, leverage deeper insights from generated data and create compelling use cases for connected and autonomous vehicles.

Table of Contents

1. Executive Summary
  • Key Findings
  • Key Trends Transforming the Autonomous and Connected Landscape
  • Growth in Connected Vehicles and Data
  • Different Layers of Automotive IoT Platform - Overview
  • Data Management Strategies Adopted in the Market
  • OEM - Technology Partnerships for Cloud
  • OEMs With In-house Strategies
  • VW Future Strategy - In-house Software Development
  • Automotive Cloud Platform - Future Opportunities
  • Automotive Cloud - Key Vendor Highlights
  • Automotive Cloud and Data Management - Current Vs. Future Outlook

2. Research Scope, Objectives, and Background
  • Research Scope
  • Key Questions this Study will Answer
  • Research Background

3. OEM Cloud Platform - Market Trends
  • Different Layers of Automotive IoT Platform - Overview
  • Global Automotive IoT Ecosystem - Participants
  • Key Applications of Automotive Cloud
  • OEM Approach Towards Cloud and Data Management
  • OEM Analysis - Connectivity and Cloud Partnerships
  • OEM Cloud Platform Analysis
  • Automotive Cloud - Key Vendor Highlights
  • Service Delivery Partners - Key Vendor Highlights
  • Future Opportunities for Automotive Cloud Platform

4. Connected Services
  • Global Connected Cars Forecast
  • Connected Services Through Cloud
  • Rise in Connected Vehicle Services
  • OEM Data Management Strategies - Connected Services
  • Deployment Model Analysis for Connected Services
  • Case Study: BMW Connected Services Architecture
  • Case Study: VW Automotive Cloud
  • Case Study: Daimler’s eXtollo Cloud Platform

5. Autonomous Vehicles - Testing and Development
  • Market Volume of Automated Vehicles (L3/4) by 2030
  • AV Development Through Cloud
  • Autonomous Driving Services of the Future
  • OEM Data Management Strategies - AV Development
  • Automotive Cloud Platforms used for AV Development
  • Case Study: Toyota Research Institute and AWS IoT
  • Case Study: Audi’s AV Development

6. Growth Opportunities and Companies to Action
  • Automotive Cloud Growth Opportunities
  • Strategic Imperatives for Success and Growth - Automotive Cloud Platform

7. The Last Word
  • The Last Word - 3 Big Predictions
  • Legal Disclaimer

8. Appendix
  • List of Exhibits

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • Audi
  • BMW
  • Daimler
  • Toyota
  • VW