Faced with unprecedented disruption, the automotive industry is betting on artificial intelligence (AI) as a driver for change. According to one recent report, the global automotive AI market will grow from $2.99 billion in 2022 to $14.92 billion by the end of the decade.
The rapid uptick in expenditure suggests there’s no lack of confidence in the ability of AI and associated technologies to help automakers thrive in the age of electrification, autonomous driving, connectivity, and novel approaches to personal mobility. But the scale and speed of these investments also raises some fundamental questions. Does the automotive industry have access to the data necessary to fuel AI-powered optimization and prediction? Where are data-driven solutions best applied in the business chain? How can enterprises translate the potential of generative AI technologies into products that deliver real value? Do automotive companies possess the requisite data literacy for projects to succeed and scale?
Anant Agarwal is well placed to provide authoritative answers. As a data science manager for the Nissan Motor Corporation, one of the world’s most prominent automotive manufacturers, Agarwal is at the heart of the new data analytics-driven automotive revolution.
Data Insight from the Heart of Nissan
Working at Nissan’s Global Digital Hub, Agarwal is charged with delivering products and solutions for customers throughout the company’s worldwide business. His background, while unconventional, has set him up for success. Agarwal completed a degree in geology and geophysics at the prestigious Indian Institute of Technology (IIT) in Kharagpur, earned a master’s degree in computational science at the University of Minnesota, and obtained an MBA from the Indian School of Business (ISB). His varied experience makes him a perfect guest for Future Says.
In this episode, Agarwal explains that data science has the potential to deliver improvement in nearly every corner of Nissan’s business. That means data and data literacy can improve not just design and production, but also supply chains, sales, marketing, after sales operations, and the customer experience. Some of the many possibilities he highlights include the use of digital twins and Internet of Things (IoT) technology to map out logistics and optimize inventories. In manufacturing, examples include integrating AI with computerized visual inspection systems to bolster quality control.
Putting Data Science to Work
Regardless of the business function involved, the principles and foundations on which successful data analytics products are built remain the same. It’s no surprise to find that loads of high-quality data and enterprise-wide data literacy is Agarwal’s starting point. Beyond that, the key question is how a solution created by Nissan’s Global Digital Hub will be used. Is the product scalable or generalizable? What is the MLOps vision?
Against the backdrop of surging AI investment throughout the automotive industry, Agarwal makes it clear that data science is no quick fix. Successful products and solutions are built around a thorough planning and development process that typically lasts up to a year.
At Nissan, the journey starts by understanding the business context and identifying the data needed. Here, Agarwal extols the virtues of data scientists and business teams forging close working relationships. “As data scientists, we are mathematicians and statisticians. We are not necessarily the best at understanding the business.”
The development process continues by exploring relationships within the data, identifying outliers, and cleaning data so it’s ready for analysis. Agarwal stresses the need for meticulous methodologies. The modeling phase is therefore likely to involve numerous iterations before the best option is identified.
Promoting the Benefits of Data Literacy
Agarwal sees model deployment as the most challenging stage of the data process. Here again, collaboration is invaluable: data science needs to engage with the business, and vice versa. If business teams can develop their data literacy – and by extension their understanding of how Nissan’s Global Digital Hub’s products work – this will ease the twin challenges of adoption and deployment. Nissan encourages this cross-fertilization and promotion of general data literacy by embedding data scientists in business teams for the long term, not just the life of a particular project. Agarwal says that accessible low- and no-code solutions such as Altair® RapidMiner® are powerful agents for accelerating the spread of data literacy.
Assessing the Impact of Generative AI
On top of this, generative AI technologies such as ChatGPT are adding yet more momentum to the rise of the citizen data scientist. Within Nissan, Agarwal believes this new wave of tools demonstrates exciting potential in sales and marketing. For example, generative AI can help the business identify which marketing channels are having the biggest impact on consumer behavior.
Alongside generative AI’s benefits, however, Agarwal agrees with the commonly held view that the democratization of AI raises important ethical questions. What’s more, he believes the spread of generative AI will raise new cybersecurity concerns. In the search for answers, the conversation again returns to data literacy. So just as Agarwal highlights the merits of non-specialists familiarizing themselves with data science within the business environment, he believes that tackling ethical issues requires a better understanding of the technologies involved.
Perhaps reflecting the fact he migrated to computational science from geology and geophysics, he doesn’t think that the learning curve needs to be daunting. To start, he says there’s plenty of good reading material out there. For those looking to dig a little deeper, programming is more accessible than ever. Put simply, data and AI democratization is very much a fact of life. Whether in business or wider society, the priority now should be on applying the right principles and making the most of the opportunities the world currently offers.
Future Says Season 4 is proudly sponsored by Oracle. Oracle offers integrated suites of applications plus secure, autonomous infrastructure in the Oracle Cloud. For more information about Oracle (NYSE: ORCL), please visit https://www.oracle.com/.