How Big Data Is Shaping the Future of the Automotive Industry

September 28, 2022 2:17 PM

The Impact of Big Data on the Automotive Industry

Big data and advanced analytics are reshaping the entire automotive value chain from design, production and marketing, to operations, maintenance, resale and eventually disposal, and recycling. Connected cars, which utilize bi-directional data communication, are now generating over 1TB of data for every day they’re driven. That’s enough to fill over 30 iPhone 7s every day!

Vehicle sensors gather structured, unstructured, and semi-structured data. Then it’s the job of data scientists and business intelligence analysts to write algorithms which turn that data into useful information. Even now, we’re only scratching the surface of what is possible today and will be possible over the next decade.

The Use of Big Data in Vehicle Fleet Management

The primary use cases we see for big data in fleet management relate to safety, optimization of logistics, tracking of driver performance, and predictive maintenance.

Sensors on the vehicles can alert fleet managers to potential safety issues or incidents instantly. Accidents can be reported immediately and fleet managers can take action without waiting for a driver’s phone call or incident report.

Also, through analyzing GPS and movement tracking data, optimal routes and dispatching decisions can be planned. Amazon are masters of this kind of big data analysis and even offer versions of their own software running on quantum compute power via AWS.

Finally, being able to instantly record and track vehicle condition is critical for optimization of maintenance and repair plans. Making sure repair networks have work orders and the right parts available ahead of time means less waiting for repairs and higher utilization rates across fleets.  

Big Data in the Auto Insurance Industry

Auto insurance has already embraced big data in managing risk, underwriting and assessing the driving performance of policy holders. Many drivers in the US are familiar with the usage-based insurance offered by providers such as Progressive’s Snapshot program.

By analyzing customer characteristics, auto insurers are also better at pricing insurance coverage so it accurately reflects the level of risk a customer presents.

One very promising area is taking unstructured data such as images or videos of damage and converting these data into structured insights, to simplify the underwriting and claims processes. Auto insurers can achieve the ‘holy grail’ of straight-through-processing, where claims can be settled without the need for human intervention and manual processing.

How Ravin AI Leverages Big Data for Vehicle Inspections 

Ravin AI collects data about both the external and internal condition of vehicles to provide fleet managers and auto insurance carriers with valuable insights that deliver business value.

For example, Ravin’s Inspect mobile app allows drivers to conduct a simple 360° walkaround scan of the exterior and collects data points of the interior and mechanical condition of their vehicles. By doing this at any handover points (check-in or check-out for example), fleet managers get an accurate view of the condition of their fleet, which combines visual evidence with structured data.

This allows fleet managers to proactively monitor the condition of the fleet so they can make sure their vehicles are safe to drive and any maintenance or repairs can be planned and scheduled. It’s a critical tool, especially for managers of distributed fleets who can’t have an inspector reviewing each vehicle before and after each drive.

Ravin AI’s fixed Autoscan system can be installed at centralized sites and automatically gathers data about every vehicle driven in and out. These data are presented to fleet managers and operations teams so they can remotely track the condition of their vehicles.

In auto insurance, Ravin’s tools produce and analyze data to support the underwriting and claims / FNOL (first notice of loss) process. For example, we’re working with a carrier in Europe who offers their customers a fully digital claims experience. Claimants are able to take a short 30 second video scan of their damaged vehicle and Ravin’s big data analysis and AI technology will recommend repair options to the insurer, allowing small claims to be automatically processed and larger claims to be triaged to the optimal repair partner. 

Conclusion: Big Data in the Automotive Industry

It’s a cliche to say that big data and AI are having a seismic effect on many industries. The automotive industry is uniquely positioned to capitalize on these advanced technologies as vehicles are producing and distributing an enormous amount of data today and this will only grow in the coming years.

Smart fleet managers and insurance companies are already implementing advanced analytics and AI-powered products into their business workflows but there is still a huge amount of opportunity for forward-thinking companies to gain competitive advantages and optimize their business operations.

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