Unlocking value from connected-car data

Recall the last time you were behind the wheel of a vehicle. Let’s assume the best-case scenario: the road and weather conditions were smooth. Maybe your journey revealed ocean-coast vistas on a sunny day. 

Still, another factor invariably loomed: unpredictability. 

Because it is annoyingly impossible to know what’s literally around the corner, car manufacturers are focusing on developing tools that preempt and safeguard against the unforeseeable. And what makes your safety on the road possible given incomplete knowledge regarding a patch of black ice or a truck running a red light? Data.  

Whether your vehicle is conventional, connected, or autonomous, a great deal of road-tested data is needed to make your travels safer. The more your vehicle can sense and anticipate, the safer the ride. Data is the fuel. 

The world runs on data: successful companies recognize this. Not enough of them though: McKinsey cautions that to date “most players have overlooked opportunities to monetize data from these vehicles—a significant oversight, considering how companies in other industries are aggressively generating value from data.”

The good news for car manufacturers—and all industries for that matter—is this: Any data that boosts human flourishing has the potential to increase business value. 

Connected cars and autonomous vehicles (AVs) offer the promise of being easier on the environment, safer, and more open to innovation. Those of us behind the wheel—consumers—already have interest in increasingly connected cars and AVs. A 2020 survey by McKinsey found that 37% of respondents would switch car brands to achieve improvements in connectivity. And 39% consumers were interested in unlocking additional digital features after purchasing a vehicle. 

According to a study from Fortune Business Insights, the global connected car market is projected to grow from $59.70 billion in 2021 to $191.83 billion in 2028. That’s a CAGR of 18.1% between now and 2028. 

Ultimately, the success of AVs depends on how we deal with data. Designing better connectivity and getting get vehicles on the road with levels 2 through 5 of autonomy is a learning process that requires tons more data. Devices like cameras and lidar sensors capture massive unstructured data sets.

In order to be useful, the data in an AV research case must travel where its insights can be gleaned: field data must get to the cloud where its driving lessons are released. 

Keep in mind that the movement of this data happens in a complicated, crowded space. 

Huge data sets form an increasingly sprawling landscape—from endpoints like AV cameras to the cloud. Most data amasses at two locations—the multicloud and the edge. Far-flung and close-by alike, data is experiencing unprecedented growth. This year alone, enterprise data is growing at the average annual rate of 42% globally. Only 32% of data available to enterprises is put to use. IDC found that the more data enterprises take advantage of measures like data operations in order to leverage their data, the higher their revenues and customer satisfaction. Massive data means massive opportunity. 

To take advantage of this opportunity, data collections must migrate quickly to where data can be securely put to best use, in proximity to applications. 

In the case of research vehicles used by car manufacturers to fine-tune the road-readiness of future solutions, data makes or breaks everything. An average research vehicle often records 30 to 50TB of data, but can record up to 150TB. At the end of the day, a fleet of 10 to 20 advanced driver-assistance system (ADAS) research vehicles can gather up around 1.5PB. The data will need to be sent to where it can be processed, often with AL/ML tools, in a public cloud. But how do we get it there? 

According to the recent Mass Data on the Go report, bandwidth alone is too constricted and slow to move tons of data. That 1.5PB of data that research vehicles delivered to the garage at the end of the day? It can take up to 150 days to transfer it over an enterprise-class connection. 

To banish latency headaches, companies are increasingly turning to a much faster, reliable solution: data shuttles and arrays. Research vehicles save the data on drives in the trunk that later easily detach and travel by cargo air to the cloud where insights are extracted. 

The road ahead is clear: how companies will enable the secure movement of their data will determine the quality of the ride. 

Jeff Fochtman is a Senior Vice President of Business and Marketing at Seagate Technology.