McKinsey estimates that today’s car has the computing power of 20 personal computers, features about 100 million lines of programming code, and processes up to 25 gigabytes of data an hour. While that might feel like a lot of data, it does not even compare with autonomous cars that will generate 4 terabytes of data an hour. Our self-driving future will involve many petabytes of data exhaust every year.
Types of data generated by cars
Connected cars generate 3 types of data:
(1) Technical Data: This data is generated by sensors, cameras, lidar, and includes information about acceleration, braking, road conditions, signs, and signals.
(2) Crowdsourced Data: This data is obtained from external sources like traffic information, weather conditions, gas pumps, charging stations, local broadcast news, and events.
(3) Personal Data: This data is availed from the driver and passengers, and includes geolocation data, favorite destinations, places, routes, and personal preferences
The saying goes "The road to revenue is paved with good data". For automakers, the question (or challenge) would be how to turn that into a reality.
Automakers are forming their Data Cartels
Telematics diagnostics providers like OnStar (General Motors), Snapshot (Progressive Insurance) and BlueLink (Hyundai) have been collecting car data for the past decade. But silo-ed data offers limited value and presents a distorted picture. When data is aggregated across all entities in the ecosystem, the insights are comprehensive and everyone benefits. To that end, the automotive industry has begun to consolidate their data efforts.
BMW Group recently launched BMW CarData, a platform which enables customized service options for BMW drivers based on real-time data from the vehicle.
The Apollo consortium headed by Baidu, Nvidia, and Microsoft is creating a car data platform for the Chinese automotive market. While Toyota, Ericsson, and Intel are putting together a separate platform for Japan.
Daimler AG and Delphi are experimenting with Otonomo, an online marketplace for car data. Amazon invested in Mojio while SiriusXM acquired Automatic. Uber, Lyft, Grab, and other rideshare services have created walled gardens around their car data. Bosch has partnered with TomTom to launch a proprietary platform.
Just Google "connected car data" and you'll see that every major company in the automotive space has either partnered or are devising their strategies to tap into what may be the biggest potential source of new revenue. And of course, Alphabet has substantial car data through Waymo, Waze, Android Auto, Google Assistant and Google Maps.
The Race to Data Monetization
Data indeed is the new oil for the car industry.
Cars generate data about how they are used, where they are, and who's behind the wheel. But how can industry players in the evolving automotive ecosystem turn data into valuable products and services with new business models?
(1) Essential Services
Visualizing and predicting the need for essential products, features, and services and offering the right moment can result in a direct path to monetization. Examples include:
- White-glove maintenance
- Roadside assistance
- In-car shopping
- OTA software apps
(2) Tailored Personalization
Leveraging car data and mobile phone data to understand people behavior, preference, and intent can result in predictive personalization. Examples include:
- Favorite destinations
- Trip anticipation
- Concierge services
- Location-based offers
(3) Insights Marketplace
Cloud data collected from millions of cars can be mined, sorted and packaged into insights for consumption. Frost & Sullivan identified over 50 different organizational types that want to profit from your vehicle, including your driving behavior and location data. This marketplace for automotive data is slated to grow to $30 Billion USD by 2025. Examples include:
- Insurance providers
- Marketers & Advertisers
- Financial services
- Energy & Utility services
- Logistics companies
- Parking operators
- City planners
- City IoT grids
What's in store for the end user (or driver)
An average person spends 400 hours behind the wheel of their connected car every year.
Personalization can create delightful experiences for people, as they actively drive the cars of today, or lean back in the self-driven cars of tomorrow. Just ask the lead characters Mike and Annie in our automotive fiction. They'll tell you all about it.
Mike and Annie were all set for their weekend trip to Tahoe in their shiny new luxury SUV to teach their twin first graders to ski. The morning of the trip, Mike mapped the route on his mobile phone. His SUV sync-ed with the phone and picked up the destination. Sensing the intent to drive through the Sierra mountains in icy and snowy conditions, Mike's SUV conducted a self-diagnostic systems check and determined the need for snow tires and engine coolant for the long drive. Based on Annie's preferences, the SUV dialed the on-the-go white-glove tire replacement service. The SUV also updated the apps on the dashboard, identified the cafes and restaurants based on the family's eating habits, mapped the gas station pit stops from projected fuel consumption, and refreshed the list of local radio stations along the route to match Annie's music choices (smart move!). As they stepped out the door to leave, the eager skiers were pleasantly surprised to find the smiling tire service technician waiting right outside the garage door.
What's next and where can we start?
Collecting data is only one part of the story, the challenge is making sense from all this data, finding real-world applications, uncovering deep insights and creating recurring revenue streams from data-driven services.
LotaData's People Intelligence platform for apps, cities, and businesses has the unique ability to transform location signals and automotive geo-data into meaningful currency. Data signals are cleansed, processed and analyzed to develop behavioral profiles that tell us about the places, restaurants, malls, brands, activities, events (& more) that people are interested in. LotaData does this with the highest regard for data protection and end-user privacy.
As we witness the explosion of car data, the key questions that will drive progress are: (1) What services and features provide real benefits for consumers? (2) What is the value exchange? and (3) Are we being sensitive, respectful and privacy compliant in our approach?