With existing business models in many different industries, e.g., automotive, telco, retail, 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 accelerating their investments, acquisitions, and partnerships with startups in order to access and take advantage of their innovations. However, they must now develop new skills to enable them to select and grow these startup-centric efforts into their next-generation core businesses.
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.
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.