Global Automotive Data Management and Cloud Platform Strategies, 2019

Global Automotive Data Management and Cloud Platform Strategies, 2019

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

RELEASE DATE
28-Feb-2020
REGION
North America
Research Code: K399-01-00-00-00
SKU: AU01970-NA-MR_24204

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Description

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. Frost & Sullivan 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. Frost & Sullivan 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.

RESEARCH: INFOGRAPHIC

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Table of Contents

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

Research Scope

Key Questions this Study will Answer

Research Background

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

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

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

Automotive Cloud Growth Opportunities

Strategic Imperatives for Success and Growth—Automotive Cloud Platform

The Last Word—3 Big Predictions

Legal Disclaimer

List of Exhibits

List of Exhibits (continued)

The Frost & Sullivan Story

Value Proposition: Future of Your Company & Career

Global Perspective

Industry Convergence

360º Research Perspective

Implementation Excellence

Our Blue Ocean Strategy

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. Frost & Sullivan 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. Frost & Sullivan 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.
More Information
No Index No
Podcast No
Author Dorothy Amy
Industries Automotive
WIP Number K399-01-00-00-00
Is Prebook No
GPS Codes 9800-A6,9807-A6,9967-A6,9813-A6,9A70-A6,9966-A6,9AF6-A6