Data Science Will Drive Auto Industry In Future, Tata Consultancy Services Says

If you identify the key building block of the auto industry during its first 130 years, you would have to point to engineering. It was engineering that made automobiles, our personal transportation devices, possible in the first place. And it is engineering that kept the line of progress moving forward decade after decade. Engineering made cars safer, more fuel-efficient, more comfortable, more durable, and more useful. It is responsible for the immense progress the auto industry has enjoyed and for the incredible benefits that personal transportation has delivered to the people of the globe. But going forward, engineering won’t serve as the prime driver of the auto industry, according to Sreenivasa Chakravarti, vice president – manufacturing, Tata Consultancy Services. Instead, he asserts, the key driver will be data science.

Electrification, autonomous operation, vehicle-sharing all appear to have the potential to change the auto industry in a fundamental way. Asked which of those technologies will cast the biggest shadow on the industry, Chakravarti made it clear that while all three technologies are important, the discipline that will tie them together in the context of the auto industry is data.

“There is a need to view this question from two lenses — the automobile itself and the end-user,” Chakravarti told forbes.com. “When you look at it from the lens of automotive engineering, very clearly autonomous has a larger potential for fundamentally altering the ground reality, and in many ways electrification will plug into it. When you see through the lens of the end-user, it does appear to be seamless connectivity leading to frictionless transport and a personalized end-user experience. Very interestingly, both these thrive on the rapidly emerging space of AI. So it is data science that holds the key to this potential.”

Chakravarti believes that the new technologies will have profound effects on the industry as it is currently structured. In fact, he refers to connectivity, electrification, autonomy and sharing as “forces of nature” in the emerging scenario. He noted that sharing is essentially business modeling around connected technologies. The leaders in vehicle sharing are investing in autonomy, and to be compliant with the very near-future regulation, electrification is a must. The flow of progress suggests that autonomous vehicle operation is another piece of the puzzle, but the barriers might make that the last of these “forces of nature” to find widespread adoption.

“The evolving landscape very clearly brings out this trend of autonomous features built around electric vehicle capabilities,” Chakravarti said. “The current cost makes widespread adoption difficult, and hence the shared format of transport is logically first off the blocks. The fusion of vehicle, ecosystem, infrastructure, customer and other data is helping intelligent, self-learning and optimizing ecosystem to take shape.”

What the new technologies require is a connective tissue, a common thread, that can tie them all together in a coherent manner that can be analyzed and optimized. The emerging business models are leveraging cutting-edge technology to create market value through connected services and shared mobility.

“In many ways, we are seeing the emergence of a new RNA and DNA for this industry, at the heart of which is data, harvested by data science,” he said.

Based on TCS research, Chakravarti doesn’t believe that any of these technologies will fall the wayside given the pace at which they are emerging. However, if commercial success and mass adoption are the criteria by which success is judged, “full autonomy” or what is often referred as “Level 5 autonomy” has a lot of distance to go in terms of both infrastructure readiness and human behavior change management, he said.

“Taking a long term view, all the technologies have a play,” he said. “The sequence of adoption will be driven by the benefits they deliver to the end-user at the right price point. As the larger connected-vehicle industry scales, it will provide the base for other technologies to be adopted.”

This could imply that non-automotive tech companies — Google, Apple, Uber, Lyft and others — will challenge the traditional automakers in the auto manufacturing and marketing arenas. But Chakravarti doesn’t see it that way. Instead, he believes the tech companies and the traditional auto manufacturers will work in concert, if not necessarily in harmony, in using their strengths while minimizing their inherent weaknesses.

“Apart from traditional engineering, there is completely new space of data science-led innovation — artificial intelligence, including machine learning, machine vision, which is determining the trajectory of vehicle feature development,” he said. “The huge volume of data, long data lifecycle and the ever-increasing appetite for scenario synthesis need complex, cloud-enabled systems to be operated. Many of these capabilities come naturally to technology companies. On the other hand sourcing and manufacturing are not part of their native capability set, as also large-scale field operations and dealer networks.”

In a way that traditional car companies have not, Chakravarti noted the technology companies are used to rapidly changing technology adoption cycles and have innovated with new business models to create economic wealth. The challenge to traditional car manufacturers is certainly real, he said, but a middle path is bound to emerge. That’s the reason why we are seeing extensive ecosystem play, with collaborations between traditional and new-age companies.