Data will define Mobility

Big data expert and Silicon Valley investor Evangelos Simoudis explains why carmakers need to embrace data as the biggest opportunity for the driverless future.

Dr. Simoudis, the landscape to develop autonomous vehicles is hyper-competitive. What’s the gist of it all?

Evangelos Simoudis: The race to a driverless future is not only about vehicle technology, but about something bigger: next generation mobility. Many people will immediately think about driverless cars, but vehicles are only a part of the driverless future. I’m talking about a model where car ownership is augmented with on-demand car access. Over the next 15-20 years we will transition to a hybrid model that combines ownership with access. Beyond that, you can start thinking more and more about mobility as a service.

The race to a driverless future is not only about vehicle technology, but about something bigger: next generation mobility.

Evangelos Simoudis

Can you describe how collecting and analyzing the data comes into play for autonomous navigation and the user experience or tomorrow?

Simoudis: In autonomous navigation, the vehicle has to understand its surroundings and what action to take next. It needs to take data from its environment, which might be the infrastructure in which it operates, other vehicles around it, and its own performance characteristics. Then there is a wide variety of data we can gather from the passengers: who they are, what mood they’re in, what they’ve done before, what they’d like to do on a routine basis for work and entertainment. When we talk about trips, we need to start thinking what happens even before I get into the car and what happens after I get out.

When we talk about trips, we need to start thinking what happens even before I get into the car and what happens after I get out.

Let’s look at a concrete example from your daily life.

Simoudis: Today, I took the train to San Francisco because there is a baseball game, which means traffic and parking will be extremely heavy. I didn’t know that, but I happened to see it in the morning in the news. There’s no reason why my automaker couldn’t have looked at my calendar and said: You have a meeting in San Francisco at 1, and because there is a game that starts at 1:30, we recommend you take the 11 o’clock train. We will have a ride hailing service pick you up from your office at 10:45 and we will have another service waiting at the train station to take you to your first appointment. If they told me you can drive to San Francisco, they should recommend a specific parking lot and give me a voucher for it. And if it was raining, they would recommend a particular parking garage because it’s closer to my destination compared to the one where I typically park.

Sounds promising. What data streams are involved to make this a reality?

Simoudis: You will need my calendar data. My trip preference data, meaning you should be able to know that I’m OK taking the train as opposed to always driving. When I am in the city, I prefer to both walk and ride, so take into account those kinds of preferences. You need traffic data, event data and foot traffic around the event. Parking data, including real-time availability of each relevant parking lot. Then you need ride-sharing data for waiting times and price differences. It may be multi-modal, so you need public transit data. You want weather data because rain triggers a different set of decisions. Then there are personal preferences: knowing who is in the vehicle and customizing what music I listen to or what content is served to me through a mobile app.

Once I’m liberated from the chore of driving, it gives me a lot of options as far as the transportation experience is concerned. What are the opportunities to bring in data and monetize it?

Simoudis: If I’m using a mobility service that takes me from, let’s say, San Francisco to Lake Tahoe, that’s a three or four hour drive during which all types of entertainment become an option: streaming video, music, audio or e-books. Startups have realized the opportunity to build applications and services for this. I’m considering an investment in a company right now that uses cameras to infer the mood of the passenger, using artificial intelligence. If they can detect my mood, they should be able to personalize my entertainment even further.

How much data is involved to power this driverless future?

Simoudis: A massive amount. Barclays thinks it could be as much as 100 gigabytes per second of operation of an autonomous vehicle, and Intel recently published a statistic that they expect four terabytes per day per vehicle. My assumption as I state in my book is about 1 gigabyte per second of operation, which is closer to Intel’s assumption. Take mapping: high-definition maps are voracious in terms of data. We’re talking about HD maps with centimeter-level resolution down to a single lane. And whereas today’s maps are on average updated every six months, the maps of tomorrow require daily updates that have to be fused with input from other vehicles and the infrastructure around the vehicle.

Should carmakers develop that capability in-house or partner with a company that’s already very good at handling user data?

Simoudis: Automakers will need to develop a broad set of partnerships around data. If consumers are shifting from car ownership to ownership plus vehicle access, manufacturers have to make a second shift — from being designers and manufacturers of cars to being providers of transportation experiences. For this to occur they have to get into the insight-generation business, which requires not only data collection, but also the expertise to analyze data and create additional value for their customers through insight generation.

If there won’t be one single entity that has all the data, the future will be all about new partnerships?

The providers that are able to take that data and turn it into a personalized transportation experience will win.

Simoudis: Correct. The incumbent automotive industry will need to develop a data sharing culture, which means partnerships based on a value exchange for using all this information. The winners will be the companies who have something to offer. If a carmaker goes to a ride-hailing company and wants to form a partnership just to get some of their data, they have very little leverage. On the other hand, if the OEM says here’s the data I’m prepared to put into this partnership and here’s how I expect we’ll both benefit from it — that’s where the real exchange of value and differentiation will emerge from such a partnership.

What role can the individual play in this new world?

Simoudis: As long as there is value to be received, consumers will be more willing to opt in and share data they generate. The providers that are able to take that data and turn it into a personalized transportation experience will win.

Dr. Evangelos Simoudis is a recognized expert on big data strategies and corporate innovation. He has worked in Silicon Valley for 25 years as a venture investor, entrepreneur and corporate executive. He graduated with a degree in electrical engineering from Caltech, worked at various venture capital firms and was VP of business intelligence at IBM. Simoudis currently is the founder and managing director at Synapse Partners. His new book is entitled “The Big Data Opportunity in our Driverless Future.”

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