'Ocean of big data' to disrupt auto business: Barclays
As cars become increasingly connected to the internet, it will be a huge challenge to manage the data produced by smart vehicles, says Barclays analyst Brian Johnson. A single autonomous vehicle could generate as much as 100 GB of data every second, and Johnson notes that it would require roughly a mile of Amazon's refrigerated articulated data trucks to manage the data produced by autonomous cars. He argues that, to thrive, automobile companies will have to offer more vehicle-related services, such as identifying warranty issues and predicting vehicle maintenance needs. 

The cameras on a pilot model of an Uber self-driving car are displayed at the Uber Advanced Technologies Center.

As cars increasingly become another "thing" in the internet of things, an ocean of data will inundate companies, pose engineering challenges, and provide opportunities for manufacturers.

A single autonomous car, with all of its sensors, cameras, and LiDAR, could generate as much as 100 gigbatyes of data every second, said Barclays analyst Brian Johnson, in a note published Wednesday.

Extrapolating this, Johnson said:

Assuming the entire US fleet of vehicles (260mn vehicles) has a similar data generation, it would create an ocean of data. To put it in context, one hour's worth of raw data across the entire US fleet would be ~5,800 exabytes in size.

To visualize this, Johnson noted that Amazon recently launched a service where refrigerated semi trucks carry 100 petabytes of data from clients to Amazon storage centers.

Or on a daily basis, there would be enough raw data to fill 1.4mn Amazon AWS "Snowmobile" data tractor trailer trucks (which each hold 100 petabytes) – at 45 feet per truck, a convoy of trucks would be 11,000 miles long. Even with data compression of 10,000x, that would still be a one mile convoy!

The sheer volume of this data will create new challenges for storage, management, and analysis, he noted. Even with 5G wireless technology, companies will need strategies for extracting important or useful information from the total amount of data collected. Johnson noted in particular that "edge analytics," where information is analyzed close to the sensors themselves (rather than sent through the cloud) may end up being important tools for managing this massive volume.