What will the car of the future look like? Will it be an electric vehicle, or hydrogen fuel cell? Will it be something you own, or something you borrow? Fully autonomous, or hybrid? These are all interesting questions, but tangential to the existential question: how will auto companies use data to meet the demands of the future? How will auto companies become data companies?
KPMG’s 2018 global automotive executive survey found that “data is both the greatest opportunity and hardest challenge for asset-based auto companies.” In fact, “85% of all executives and 3 out of 4 consumers are convinced that data & cyber security is the number one prerequisite for future purchasing decisions.”
Auto manufacturers need to understand this changing market in order to tailor their offerings to their customers and potential customers on an ongoing basis. To survive this seismic shift in the automotive industry, manufacturers need to rethink their data strategies. Their focus should be on three key categories:
- Continuous Upgrades
- Tailored Experience
Car purchases today are not just about physical assets, but are instead lifestyle purchases whose technology must integrate into everything else a person does. To gain competitive advantage, manufacturers need to continue to use data to improve and upgrade the vehicle even after it’s rolled off the lot.
Roland Berger and Lazard’s 2018 Global Automotive Supplier Study found that the “emergence of software as key differentiator will make many existing competencies obsolete and create more intensive competition from new tech players.” On the other side of the coin, software differentiation becomes a significant opportunity to deliver upgraded technology and services to the consumer on an on-going basis, keeping them satisfied with their purchase–and therefore, the brand–long after the original software has become outmoded.
Consumers will no longer face in-car maps missing new roads or sending them to closed businesses. Maps, along with security features, voice controls, smartphone integrations, and, most significantly, autonomous capabilities, will receive regular updates over the lifespan of the car.
The same upgraded technology and services that benefit the consumer also become new revenue sources for the automaker. “To remain competitive and capture a fair share of value in the field of automotive electronics, it is crucial to analyze which features add real value to the future architecture and therefore can be monetized,” according to McKinsey. “Subsequently, players need to derive new business models for the sale of software and electronics systems, be it as a product, a service, or something completely new.”
Customers aren’t buying your product; they’re buying their preferences. The car that uses data to personalize everything from the buying experience to the day-to-day will appeal most.
In this ideal world, you should be able to walk up to your car, and automatically the doors unlock, as the car responds to the proximity of the familiar smartphone in your pocket. At the same time, the seats, temperature, and playlist default to your preferences. The telematics system suggests a route home that incorporates data it’s collected about the stop you make at your son’s school on Wednesdays, the gas station where you go slightly out of the way to get the best price, and the real-time traffic situation, currently impacted by construction and a blocked lane where a bus broke down.
These are the features that are meaningful to the consumer of today and tomorrow. Precision engineering, competitive pricing, and size all take a backseat to software upgrades tailored to personal preferences.
Data is core to automation. The self-driving car itself takes in massive quantities of complex data in real-time in order to navigate roads, traffic, and hazards successfully. And the data it collects can then be used by the manufacturer to continuously improve and grow the repertoire of autonomous features.
Google, the company that democratized access to information and became a verb on data, has perhaps unsurprisingly jumped head-first into the self-driving car race. After putting in “3.5 million real-world miles on public roads,” and “8 years self-driving in more than 20 US cities,” according to CB Insights, “Waymo (spun out as a formal Alphabet company as of December 2016) began truly driverless testing last year and has now ordered thousands of new Chrysler Pacifica minivans ahead of its robotaxi service launch.” They have partnerships lined up with Lyft, Avis, and Intel, among others. Car companies, from mainstays like GM to newcomers like Tesla, are heavily invested as well, along with ride-sharing services like Uber and Lyft.
The data’s on the wall: it’s not a matter of whether but when these companies will be able to ingest, process, manage, and analyze highly complex data from unpredictable sources in real-time, in order to roll out a safe and reliable fleet of self-driving cars. We’re almost there.
McKinsey finds that “new smart sensors and applications will create a “data explosion” in the vehicle that players need to handle by processing and analyzing the data efficiently if they hope to remain competitive….The advent of highly automated driving capabilities will require functionality convergence, superior computing power, and a high degree of integration….As the volumes of data grow, data analytics will become critically important for processing the information and turning it into actionable insights. The effectiveness of using data in such a way to enable autonomous driving and other digital innovations will depend on data sharing among multiple players.”
Manufacturers need to prepare by investing in technology designed for the Extreme Data Economy. Instant insight engines accelerated by GPU databases deliver the millisecond speed of data analysis, efficient, parallelized, brute-force computing power, and real-time, even automated, analysis that car companies need to compete in a data-driven market that demands continuous upgrades, tailored experiences, and increasing levels of automation.
Need a primer on a GPU database? Read a quick overview here.
These technologies will lay a foundation for developing new business models centered around data. BCG puts it well: “this data might create far more commercial value for vehicle manufacturers than those companies can gain by using the data merely as a source of information for better designing and servicing their products.” As the automotive industry shifts its focus, it becomes clear that, across the board, data is the fuel of the future.
This article was originally published on Forbes on 5/10.
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