Evangelos Simoudis is the Founder and Managing Director at Synapse Partners in Silicon Valley. His book The Big Data Opportunity in Our Driverless Future was published last month. He is on Securing America’s Future Energy’s (SAFE) Autonomous Vehicle Task Force. He spoke to The Fuse about driverless cars, job opportunities in the autonomous space, and the challenges addressed by exploiting Big Data.
What is your intended audience for your book? What do you hope readers will see as key takeaways?
The main audience is corporate executives. Many corporations, including automotive industry incumbents, have come to Silicon Valley to try to understand how start-ups can help them innovate. The emergence of ACE (autonomous, connected and electrified) vehicles and mobility services provide an opportunity to better articulate how corporations can use start-ups in next-generation mobility. Big Data is itself a major lever for both corporations and private companies to innovate and disrupt in next-generation mobility. The book provides a road map to take advantage of the opportunity afforded by Big Data in the context of next-generation mobility.
Big Data is itself a major lever for both corporations and private companies to innovate and disrupt in next-generation mobility.
One of the book’s big takeaways is that next-generation mobility is not only the result of technology changes; we have to see it in the context of larger macro trends. These trends include the growth of mega-cities along with the rising traffic congestion and pollution in our everyday lives. The aging population in certain developed economies will need to be accommodated. These macro issues and the technology that enables the development of ACE vehicles will foster the next generation of mobility.
The book posits that consumers are shifting from a car ownership-centric model to a hybrid model that mixes car ownership with vehicle access. This shift will continue for the next 10-15 years. Perhaps in 20 years, we will be able to shift to a completely driverless environment. At such a time, we may forego car ownership, at least in some urban settings. With these changes, corporations in the auto industry need to move from a model that has them designing manufacturing and distributing automobiles to one that has them providing transportation solutions. In order to do that, automakers will need to start generating customer insights.
Finally, companies that offer mobility services such as ride-hailing and ride-sharing will want to capitalize on ACE vehicles as a means of improving their economics. But in order to do this they need to become fleet operating companies. Making this transition will provide them with control over a set of ACE vehicles and the user experience offered through those vehicles. Today, when you use a ride-hailing services such as Uber or Lyft, there’s very little control over the experience you have while in the vehicle. ACE vehicles, however, will enable the mobility services companies to exercise better control and enhance the user’s experience by improving the condition of the vehicle, the driving behavior, and the vehicle’s personalized environment (the type of infotainment, the type of connectivity, the cabin’s amenities).
What kinds of jobs will be supported by the shift to Big Data and autonomy?
With the transition to on-demand mobility, we will start seeing the emergence of a new value chain. This value chain will take a page out of the playbook of the airline industry and could even follow the trajectory of how that playbook has evolved. For example, you can imagine the value chain consisting of fleet operators and fleet maintenance companies. Today, the owner of the car driving for a ride-hailing service takes care of the vehicle, but if a ride-sharing company operates the fleet, someone will have to clean the interior of the car, perform maintenance and repairs on it, and recharge it or refuel it to ensure its continued use. That is similar to what today occurs with airlines. There are companies that take care of their planes in between flights, cleaning them, refueling them, and repairing them. Creating this on-demand mobility value chain will require labor and therefore produce jobs.
Creating an on-demand mobility value chain will require labor and therefore produce jobs.
The bigger opportunity, however, is around Big Data. The companies participating in this new value chain will need to analyze a variety of Big Data such as real-time performance data from the autonomous vehicle, as well as historical vehicle performance data, to determine, for example, the optimal times for recharging each vehicle or performing preventive maintenance on the vehicle. You can also analyze passenger data regarding destination preferences (business vs social activities), as well as infotainment preferences while in the vehicle. In order to do these types of analyses, you need a variety of data points. That is opportunity of big data analytics in the course of enhancing the overall transportation experience. As we start to blend urban on-demand mobility with urban logistics for small packages or food, these companies will also need to be in the position to determine how to deliver such items as efficiently as possible. For example, is it possible to deliver a package to a destination in after dropping off a passenger and before picking up another ride? These applications will all be driven by the right data analysis. And that provides job opportunities.
Finally, labor opportunities in small-batch manufacturing will emerge. Take, for example, the partnerships of GM-Lyft or Daimler-Uber. You can envision the ride-sharing companies going to the automotive OEMs, using the data they have on certain markets, and asking for ACE vehicles with certain specifications, such as a small car carrying 1-2 passengers in urban environments with narrow streets. The vehicles’ specifications would be the result of very sophisticated data analysis. If you combine that type of analysis with additive manufacturing, automakers will be able to profitably produce vehicles in small batches instead of requiring very large production runs in order to benefit from economies of scale.
Intel recently said that “data is the new oil,” suggesting that cars will be as reliant on data as they have been on physical oil. Do you agree?
I do agree that next-generation vehicles will generate and rely on data a lot more than any prior generation of vehicles. That is in fact the core subject of my book. It’s not only the data that is generated by the vehicle that is important; there is a lot of other data generated outside of the vehicle that needs to be used and analyzed in order to improve the entire transportation experience. A lot of data has to be brought together—everything from weather data, to traffic data, to personal calendar data is needed in addition to what data is produced inside the vehicle in order to get a complete picture and provide the right transportation experience. If companies want to develop such customer-centric experience, they need to bring together all this data. I’m not naïve to think that all this data will reside in a single repository or that there will be a single owner of all this data. In fact, one of the biggest challenges for the companies that want to participate in this new value chain is the ability to develop a data-sharing culture. That’s just as important as the data itself.
What disruption risks exist with data beyond the predictable cyber security issues?
OEMs have to become more cognizant of the data they collect, how they safeguard it, how they distribute it, and how they secure it.
One challenge that needs to be dealt with is how this data is governed. Who has control over this data? We’re seeing a backlash against Internet companies collecting data on consumers because the consumers don’t always understand or are able to appreciate what data is being collected, who has access to it, even beyond the collector, and how it is being used. There are issues of privacy and security as we hear about hacks that result in data theft and even inadvertent data leaks on a daily basis. Companies participating in this value chain will need to understand and appreciate that they are in the data business. For automotive OEMs, it will be extremely important to understand that they must shift from simply being manufacturers to being in the customer insights business. They have to become more cognizant of the data they collect, how they safeguard it, how they distribute it, and how they secure it.
Can you give a sense of the scope of data created by autonomous vehicles?
Fully autonomous vehicles can produce about a gigabyte per second of operation, but not all of the data that is produced every second is useful or should be kept.
Intel recently said that an autonomous vehicle produces something on the order of 4 terabytes of data per day. My estimate is that fully autonomous vehicles produce about a gigabyte per second of operation. Not all of the data that is produced every second is useful or should be kept. But the overall data quantity obviously depends on how much the vehicle is used. Vehicles that are in fleets offering on-demand mobility services will need to operate as much as possible during a 24-hour period, so these vehicles will produce a lot more data than those that are used privately and therefore less frequently. But in understanding the data being generated and consumed, one will also have to consider data coming from other sources such as infotainment. Streaming content into your vehicle will produce quite a bit of data, much like streaming content into your home does today. On top of that, you have data generated through the communication with all the other vehicles operating in the vicinity and the data generated through the communication between the ACE vehicle and transportation infrastructure, such as roads, traffic lights, bridges, etc. Weather and maps, especially high-definition ones that need to be updated in near real-time, are very heavy data generators.
What are the biggest obstacles to autonomous vehicles? What might hinder a transportation revolution?
I see very two broad obstacles, and a smaller one related to data. The first broad obstacle is regulation. Even with this hybrid model of car ownership and access, only a small number of autonomous vehicles will be on the road in the next 10 years. Regulating how transportation and traffic will incorporate these cars will take time. The second is infrastructure. Not only does the right infrastructure have to be built, but today we don’t know yet who will pay for it. With regards to capitalizing on the opportunity provided by Big Data, again, we need to find the specialized personnel to prepare, manage, and analyze the data that is collected and can be collected in order to generate the insights that will enable us to exploit the opportunity leading to a driverless future.