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.
In the not too distant future, automakers won’t be evaluated just on the physical, safety and performance characteristics of their vehicles. Instead incumbent and next-generation automakers will be evaluated based on the completeness of their solution along five dimensions: Electric, Autonomous, Connected, Mobility Services (EAC+MS), and Information. We read about the progress automakers and their suppliers are making along the first four dimensions. There is much less conversation about the fifth dimension. In this two-part series, we will discuss the big data challenge facing the automotive industry. The pieces are the result of my work in the industry helping corporations with their innovation and big data strategies. The first post provides the why and makes two points:
- Automakers must be in the information business. To be effective in the information business, automakers must change their perspective and start thinking about an overall process for big data in and around the car.
- Information in EAC+MS implies big data and incumbents in the automotive ecosystem must become serious about big data. Newcomers to the automotive industry such as Google, Tesla, Faraday Future, and likely Apple, but also Uber, and Lyft, realize this imperative.
The second piece will provide the how to try to address this challenge.
In a previous post I wrote about the disruptive innovations that have been introduced by Tesla Motors (Tesla) and Uber and presented the steps the automotive industry should be taking in order to address the startup-driven disruption. In this post I want to make three points:
- It is hard for startups to break into and succeed in the automotive industry. The industry requires high investment and ability to scale while maintaining low risk. The Car Use value chain has lower barriers to entry but they result in many competitors that have difficulty differentiating their solutions.
- Startups must realize that they cannot disrupt the entire automotive industry. Instead they must focus in the right areas, and collaborate with innovation-minded incumbents in order to become part of the appropriate supply and value chains as quickly as possible.
- The incumbents must structure their organizations, operations and culture in a way that enable startup-driven innovation to meaningfully impact their business.
Companies in the automotive value chain are faced with a challenging future. While reporting record quarterly sales, they are also witnessing two alarming trends. Because of problems such as pollution, climate change and loss of productivity due to long commute times, consumer attitudes towards car ownership and use are changing. In the medium and long term, i.e., the next 5-30 years, these changes have a high probability to negatively impact automakers, their suppliers and their dealers, along with insurance companies, finance companies, and many other industries that are part of the automotive value chain. In addition, there is a growing consumer interest in electric cars (to address the pollution and climate change problems) and in self-driving, or autonomous, cars (to address the productivity problem, as well as a slew of other issues such reduced accidents and mobility for the elderly and handicapped). The success of Tesla Motors, Zipcar and Uber, the growing consumer anticipation of Google’s self-driving cars entering broader service, as well as Apple’s anticipated entry in the car business are exerting additional pressure on the automotive value chain to change the way it innovates. In this blog I explore what the automotive industry has been doing to address the potential disruption, analyze the effects of these initial steps, and provide recommendations on what corporations could be doing better.