In my book and previous posts I build a broad case for the key role big data and AI play in next-generation mobility, and provide several examples from transportation and logistics. Next-generation mobility is about intelligent, connected vehicles that utilize some form of electrified propulsion, and on-demand shared transport services of people and goods that will be offered through such vehicles. Many of these vehicles will be capable of autonomous movement. Next-generation mobility will help us address some of our biggest challenges, such as pollution and climate change, urbanization and congestion, aging population, and traffic fatalities, while enabling us to maintain economic prosperity by operating highly optimized supply chains that span the globe. It will give rise to a new value chain where big data and AI will play a key role. It is therefore important to identify the new monetization opportunities enabled by big data and AI in the context of this value chain.
This post first appeared on 4/27/17 in O’Reilly’s site. It has been revised since it first appeared.
In my book The Big Data Opportunity in Our Driverless Future I make two arguments. First, societal and urban challenges are accelerating the adoption of on-demand personal mobility services. Second, technology advances, including big data and AI, are making next-generation vehicles, and specifically Autonomous Connected and Electrified (ACE) vehicles a reality. I define Next-Generation Mobility as the movement of people and goods using a combination of ACE vehicles, and of transport services such as ride-hailing, car sharing, ridesharing, and others that are offered on a short-time, on-demand or as-needed basis. Next-Generation Mobility will cause three major shifts that can lead to the disruption of the automotive and transportation industries: a consumer shift, an automotive industry shift, and a mobility services shift.
In a series of posts, starting with this one, I examine what is causing these shifts, one of the value chains that is emerging as a result of these shifts, big data’s and AI’s key roles in the value chain, and the models being created around this value chain.
The Consumer Electronics Show (CES) is starting later this week and will be followed by the Detroit Auto Show (DAS). Both shows will serve as venues for the automotive industry to showcase Autonomous Connected Electrified (ACE) vehicles and new Mobility Services. ACE vehicles combined with Mobility Services such as ridesharing, car sharing and multimodal transportation options will give rise to a new personal mobility model that combines car ownership with car access. These innovations and the emerging model are creating two challenges for the automotive industry.
By extensively utilizing data, and paying attention to detail Tesla has changed the conversation on the type of personalized experience car owners (drivers and passengers) should expect from an automaker. In the process, it is building strong loyalty with the owners of its cars who appear willing to support it through thick and thin. Tesla has taken a lesson from Apple, Google, Facebook and Amazon, four companies that obsess about connecting pieces of data and using it to better understand their consumers and tailor their services to provide the right experience. It is this personalized experience that Tesla offers that has allowed it to build a brand that delights its customers. The exploitation of big data that is generated by vehicles, consumers and companies across the entire automotive value chain must become a key competence of all automakers. But as I discussed in previous posts of this series, with the possible exception of GM through its OnStar service, (and here) only recently have started to collect and utilize these types of big data (and here). As a result, they don’t capture data of sufficient scale and they are not best in class yet at exploiting big data. In this post I argue that automakers should accelerate their partnerships with companies that have strong data collection and exploitation DNA as Tesla has already demonstrated is possible. As mobility services are starting to play an increasingly important role in transportation solutions, companies that offer such services become ideal partners to automakers. By partnering with them, automakers will be able to better understand their customers in far greater detail than they do today, as well as mobility services, which threaten to disrupt them. Ridesharing and carsharing companies represent the best initial candidates for such partnerships because these companies a) are collecting and utilizing consumer big data with the same attention and rigor as Apple, Google, Facebook, and Amazon and b) have already collected impressive data sets due to the scale they have achieved. Apple’s just announced investment in Didi Chuxing (and here), in addition to the broad implications to Apple’s services in China, e.g., ApplePay, is a further indication that data partnerships even among companies that are some of the best in class, can be essential for developing next-generation transportation solutions, including autonomous vehicles.
In this first part of this two-part series, I discussed why the automotive industry, particularly the incumbent OEMs, is facing a big data challenge. This challenge is becoming extremely acute as a result of the increasing adoption of EAC vehicles combined with Mobility Services (EAC+MS) and the torrent of data that will be generated as a result of this adoption.
In this post, I present how the incumbent OEMs can address this challenge. To do so, automakers must:
- Think strategically and own the big data strategy. They must then drive the execution of this strategy instead of relying on their suppliers for partial solutions
- Revamp the vehicle’s computer system architecture to create a unified computing and big data architecture.
- Establish and enforce data ownership rights among the appropriate constituencies.
- Create a data-sharing culture.