In the previous post I described a new value chain that will connect companies providing on-demand mobility and three emerging models for the realization of this value chain. This value chain is the result of the consumer shift from a car ownership-centric transportation model to a hybrid model that blends car ownership with mobility services, and the stated intent by the providers of certain of these services to adopt of Autonomous Connected Electrified (ACE) vehicles. Various acquisitions, partnerships, and investments by automotive industry incumbents and by companies that already provide on-demand mobility services, or intend to do so, including the recently announced partnerships between Waymo and Avis, and Apple and Hertz, indicate that this value chain, which I call ACE mobility services value chain, is starting to quickly materialize. In this post I provide a deeper analysis of the emerging value chain in the process exploring investment opportunities in tomorrow’s leading businesses.
This post first appeared on 4/27/17 in O’Reilly’s site.
In my book, The Big Data Opportunity in Our Driverless Future, I make two arguments: 1) that societal and urban challenges are accelerating the adoption of on-demand mobility, and 2) technology advances, including big data and machine intelligence, are making Autonomous Connected and Electrified (ACE) vehicles a reality. ACE vehicles and on-demand 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 this post, I examine what is causing these shifts, the value chain that is emerging as a result of these shifts, big data’s key role in the value chain, and the models being created around this value chain.
With existing business models in many different industries reaching maturity and providing little or no growth, and startups disrupting them with their new solutions, corporations find themselves more than ever in need for creating new businesses. But few corporations are able to consistently create from scratch new, big businesses that use innovative technologies and employ novel business models. For reasons explained here, it is slowly becoming apparent to corporations that the innovation model that is based solely on the efforts of corporate R&D organizations is no longer sufficient for addressing the long-term growth goals they need to achieve. To address these issues, achieve their growth goals, and avoid being disrupted corporations are starting to tap on the innovations of startup ecosystems. However, they must now learn how to select and grow these startup-centric efforts into their next-generation core businesses.
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.
Professor Ikhlaq Sidhu and I recently started talking about how the interest of corporations in the innovations created by startups is leading to changes in corporate R&D models, an area he has been studying for some time. As we continued our conversations we felt that it will be important to start publishing some of our thoughts. This is the first of what we hope to be a series on posts on how startup innovation is impacting corporate R&D models. Please also see.
The World of Innovation Has Changed
A great deal has changed in corporate innovation since the days of Bell Labs and Xerox PARC. While these models of advanced work led to so many innovations and created tremendous broad economic value, though not always to the lab’s corporate owner, it is clear that large scale, insulated corporate research is no longer the most common model for entering new markets or developing technologies of the future. Even Alphabet is re-evaluating the mission of Google X.
What has changed? For most companies, open innovation and new venture acquisitions have become extensions of the firm’s advanced R&D portfolio. At the same time entrepreneurs and their investors have become much more effective and skilled at efficiently creating new startups and bringing technology and business model innovations to market. And finally, a significant fraction of University lab work has now evolved from the traditional “publish or perish” model to one that is today closer to demonstration, design-oriented, and more applied than ever before.
All of these changes are now converge towards a new model for creating and managing portfolios of innovation.
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.