Navigating the Data Privacy Landscape for Autonomous and Connected Vehicles: Balancing Consumer Privacy and Monetization
Wednesday, May 25, 2022

This is the last part in a series of advisories on data privacy best practices for autonomous and connected vehicles. To read previous advisories in this series, please visit: Best PracticesDocumenting Data CollectionDefining Data Privacy Principles and Implementing Effective Data Security.

The data collected from connected and autonomous vehicles is a treasure trove of information that can be used to continuously improve the customer experience, provide valuable and potentially cost-saving conveniences, and improve user and vehicle safety.

Today, much of the focus for data monetization seems to be on consumer-related data. Global management consultant McKinsey estimates the financial opportunity for AV companies to monetize this data at $250-$400 billion within a decade. Some examples of this monetization, noted in their new report on Unlocking the Full Life-Cycle Value From Connected-Car Data, include “insurers… tailor[ing] insurance rates to driving styles, for instance, and certain cities [using] sensory data to identify potholes. A few media agencies have also increased their advertising reach through new touch points inside and outside of vehicles.”

This industry-within-an-industry is possible because the collected data from connected and autonomous vehicles can allow third party businesses to deduce driver demographics, as well as coveted psychographic and behavioral factors such as personal interests, recreational activities and other preferences. More importantly, effective use of this data can dramatically increase driver safety while reducing traffic and road congestion.

In this dynamic landscape, in-house counsel should create privacy practices for connected and autonomous vehicles that meet or even exceed consumers’ privacy expectations while maximizing the data’s benefits. Strategies to accomplish this include highly transparent and accessible disclosures, meaningful consumer choice and effective data deidentification.

 

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