Global Automotive Data Management and Cloud Platform Strategies, 2019

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.