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Google thinks its cloud computing technologies provide an ideal platform for collecting and analyzing data from connected cars for all kinds of new use cases in the future.
In a just released solution guide the company described how its cloud services could be harnessed to power new automotive applications such as usage-based insurance, predictive maintenance and new in-vehicle experiences using data collected from connected vehicles.
Modern vehicles can generate up to 560 GB of data per day, Google solution architect Charles Baer said a blog this week. While this data can be incredibly useful, there are multiple challenges that need to be overcome first in order to get there, he said.
For example, connecting any device, including those in connected cars, to any data collection and analysis platform requires authentication and authorization capabilities. It also requires the ability to do things like pushing out software updates and configuration tools to those devices.
Similarly, there is a need for reliable mechanisms for ingesting data from connected vehicles, processing the data and storing it. In order to derive business value from the data, organizations then need to have capabilities for running various types of sophisticated analytics on the collected data.
Business-level logic needs to be developed and integrated with data from internal sources and often with data acquired from a third-party or from off-premise locations. Finally predictive models are required, so analysts can predict outcomes based on the data, Baer said.
Google Cloud Platform (GCP) services have the capabilities to address such requirements he said. For example, on the device management front, Google’s Cloud IoT Core technology already allows organizations to connect globally distributed devices to GCP, he said.
Cloud IoT is a service that Google launched in May to help enterprises better manage sensors and other IP-enabled devices connected to networks. The device management features built into the Cloud IoT service provide the kind of device authentication and authorization that is required in a connected car environments, Baer said.
Google’s Cloud Sub/Pub message oriented middleware technology similarly can be used as a data ingestion point for the various different kinds of data that connected cars generate. Google has described Cloud Pub/Sub as middleware that lets distributed applications and services communicate using a real-time messaging capability.
Baer listed several other Google cloud technologies that he said would work well for enabling new applications from connected car data. Examples included Google Cloud Dataflow for processing data gathered from multiple sources and Google Compute Engine, Container Engine and App Engine.
Google’s range of predictive technologies such as TensorFlow and Cloud Machine Learning engine also can be very useful in extracting insights and value from IoT data, he said.
“Vehicles are becoming sophisticated IoT devices with built-in mobile technology platforms to which third parties can connect and offer advanced services,” Baer wrote in his blog. Google’s cloud technologies provide a scalable and reliable platform for enabling this sort of use case, he noted.