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.
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.
I want to take a quick breather from writing about corporate innovation and return to another topic of this blog: big data and insight as a service. Host Analytics, one of my portfolio companies, recently completed a $25M financing round. Host Analytics offers a cloud-based Enterprise Performance Management (EPM) Suite that streamlines a corporation’s planning, close, consolidation and reporting processes. But it is what they are enabling for the enterprise that is important to write about. Host Analytics has moved from being an EPM company, to being an insight generation company.
On July 15 IBM and Apple announced an exclusive partnership. There are several components to this partnership that have been addressed elsewhere (here and here) but of most interest was the commitment to develop 100 industry-specific mobile analytic applications for the enterprise. As I had written, the broad adoption of smartphones and tablets by employees, customers and partners, combined with a BYOD strategy, is driving corporations to rethink their enterprise application strategies. They are starting to mobilize existing applications and embrace a mobile-first approach for the new applications they are licensing or developing internally. Analytics-based insight-generation applications represent a major category of these new applications. Recognizing this trend, I and many other venture investors, have been aggressively funding startups that develop mobile enterprise applications.
The physical world (from goods to equipment) is becoming digitally connected through a multitude of sensors. Sensors can be found today in most industrial equipment, from metal presses to airplane engines, shipping containers (RFID), and automobiles (telematics devices). Consumer mobile devices are essentially sensor platforms. These connected devices can automatically provide status updates, performance updates, maintenance requirements, and machine-to-machine (M2M) interaction updates. They can also be described in terms of their characteristics, their location, etc. Until recently these sensors have been interconnected using proprietary protocols. More recently, however, sensors are starting to be connected via IP, to form the Internet of Things, and by 2020 50B devices will be connected in this way. The connected physical world is becoming a source of immense amount of low-level, structured and semi-structured data, e.g., big data.
Collecting and utilizing sensor data is not new. For example, GE uses data from sensors to monitor the performance of industrial equipment, locomotives, jet engines and health care equipment. United Airlines uses sensors to monitor the performance of its planes on each flight. And government organizations, such as the TSA, collect data from the various scanners they use at airports. The key applications that have emerged through these earlier efforts are remote service and predictive maintenance.
While our ability to collect the data from these interconnected devices is increasing, our ability to effectively, securely and economically store, manage, clean and, in general, prepare the data for exploration, analysis, simulation, and visualization is not keeping pace. Today we seem to be pre-occupied with the goal of trying to put all of data we collect into a single database. Even in this task we are not doing a particularly good job. The existing database management systems are proving inadequate for this task. They may be able to process the time series data collected by sensors, but they cannot correlate it. The effectiveness of newer database management systems (NoSQL), e.g., Hadoop, MongoDB, Cassandra, is also proving inconsistent and depends largely on the type of application accessing the database and operating on the collected data.
The new generation of applications that will exploit the big data collected by sensors must take a ground up approach to the problem they are trying to address, not unlike that taken by Splunk. In Splunk’s case, the application developers considered the ways the sensor data being collected from data centers must be cleaned, the other data sets with which it must be integrated/fused, the approach to interact with the resulting data sets, etc. Splunk’s developers were able to accomplish this and deliver a very effective application because they understood the problem, the spectrum of data that must be used to address the problem, and the role the low-level data is playing in this spectrum. They also appear to have understood the importance of providing effective analyses of the low-level data as well of the higher-level data sets that resulted when several different data sources are fused.
The Internet of Things necessitates the creation of two types of systems with data implications. First, a new type of ERP system (the system of record) that will enable organizations to manage their infrastructure (IT infrastructure, human infrastructure, manufacturing infrastructure, field infrastructure, transportation infrastructure, etc.) in the same way that the current generation of ERP systems allow corporations to manage their critical business processes. Second, a new analytic system that will enable organizations to organize, clean, fuse, explore and experiment, simulate and mine the data that is being stored to create predictive patterns and insights. Today our ability to analyze the collected data is inadequate because:
- The sensor data we collect is too low-level; it needs to be integrated with data from other sensors, as well as higher-level data, e.g., weather data, supply chain logistics data, to create information-richer data sets. Data integration is important because a) high-velocity sensor data must be brought together and b) low-granularity sensor data needs to be integrated with other higher-granularity data. Today integration of sensor data is still done manually on a case-by-case basis. Standards-based ways to integrate such data, e.g., RESTful APIs, other types of web services, have not yet adopted broadly in the Internet of Things world and they need to. We need to start thinking of sensor data APIs in the same way we have been thinking about APIs for higher-level data. And once we start defining these standards-based APIs we also need to start thinking about API management.
- We don’t yet know the range of complex analyses to perform on the collected sensor data because we don’t know yet what enterprise and government problems we can solve through this data.
- Even for the analyses we perform, we often lack the ability to translate any analysis results to specific actions.
Finally, along with these two types of systems we will need to effectively manage the IP addresses of all devices that are being connected in these sensor networks. IPV6 gives us the ability to connect the billions of sensors using IP. We need better ways to manage these connected devices. Most organizations today manage them on spreadsheets.
The big data generated by the Internet of Things is opening up great opportunities for a new generation of operational and analytic applications. Creating these applications will require taking a ground-up approach from the basic sensor technology and the data sensors can generate to the ways sensors and managed and data is integrated, to the actions that can be taken as a result of the analyzed data.