The automotive industry has undergone a seismic transformation over the last several decades. In 2005, most vehicles were mechanically sophisticated but had limited digital capabilities and integrations. Today, the automobile is a computer on wheels: software-defined, cloud-connected, increasingly autonomous and continuously evolving through over-the-air (OTA) updates. 

Advancements in technology have fueled this acceleration at an incredible pace. Just take, for example, electric cars: They accounted for 18% of sales in 2023, up from just 2% in 2018. Inexpensive sensors, high-speed wireless networks and the cloud have enabled a complete transformation — with data, analytics and AI now central to the entire lifecycle of the automobile.

To help automotive companies keep pace with this incredible rate of change and speed toward further innovations, Snowflake is bolstering its AI Data Cloud by offering Automotive Solutions especially tailored to the automotive industry. 

Here, Tim Long, Snowflake’s Global Industry GTM Lead for Manufacturing, and Pugal Janakiraman, Snowflake’s Field Chief Technology Officer for Manufacturing, outline some of the biggest challenges and opportunities that auto companies face today.

Data plays a critical role in all the innovations happening in and around the auto industry. What are the challenges that this industry faces in harnessing their data and putting it to full use?

The data required to drive the industry forward has traditionally been very challenging to conquer for a number of reasons:  

• Siloed data: Data is siloed across all automotive design, production and aftermarket systems, making it almost impossible to analyze end to end across the entire vehicle lifecycle.

• Data volume: The sheer amount of data has increased dramatically with the rapid rise of sensors in manufacturing processes and connected vehicles. A single car, for instance, can generate more than 20 GB of data per hour — and that’s on the conservative end. With around 100 million cars produced a year globally, that’s a seriously big number.

• Legacy infrastructure: Legacy infrastructure has limited scalability, which is needed to develop autonomous driving solutions or “digital twins” used in automotive design.

• Difficult cross-enterprise collaboration: Productive cooperation between the automotive OEMs and their thousands of suppliers, partners and customers is cumbersome and complicated.

Over the last few decades, the auto industry has largely focused on driving efficiency within a particular process area or a siloed system, such as product lifecycle management, enterprise relationship planning (ERP) and manufacturing execution systems. But now, with unlimited compute available in the cloud, along with big data analytics tools maturing with AI, organizations can build efficiencies that go far beyond the silos of a department or system. 

That said, this cloudification journey presents new challenges, given the sheer volume and velocity of data generated from “connected” cars. What’s more, the fear of vendor lock-in has pushed many auto companies to adopt multi-cloud strategies, and that has ultimately led to persisting data silos — evolving beyond on-premises systems and into cloud infrastructure as well.

What are the key capabilities Snowflake offers to address these challenges?

Snowflake’s Automotive Solutions include our powerful data sharing and AI capabilities, combined with specialized partner solutions, to enable the entire auto ecosystem of suppliers, original equipment manufacturers (OEMs), distribution, sales and service providers to seamlessly collaborate on vehicle development, optimize production processes, and leverage real-time data insights across the entire automotive value chain. With Snowflake, the automotive industry can expect a data-driven transformation to be:

• Easy: With Snowflake, companies can unify enterprise, shop floor (IT/OT) and vehicle data — whether structured or unstructured — all in one place, and they can leverage Snowflake’s analytics capabilities to analyze it all without moving the data around. 

• Connected: Snowflake enables seamless end-to-end visibility throughout the value chain, from R&D to the after-sales domain.

• Trusted: With built-in security, governance and high-availability architecture, Snowflake’s managed service for AI and data workloads provides a solid foundation for building automotive solutions with confidence and speed.

Given the collaborative nature of the industry, how can automotive companies benefit from Snowflake’s strong ecosystem of partners and providers?

Snowflake’s partner ecosystem has proven success at delivering solutions for the most challenging automotive use cases. Snowflake’s system integrator partners, including Accenture, Deloitte and EY, help automotive manufacturers accelerate digital transformation with the Snowflake AI Data Cloud for Manufacturing. Together with AWS, Snowflake enables a unified data and AI strategy for automotive manufacturers, helping organizations ingest, transform and share data at scale and powering critical workloads across analytics, data engineering, AI and app development. With fully managed infrastructure, businesses gain simplicity, scalability and governance while leveraging integrated AI services, and real-time collaboration capabilities. Organizations do more with their data and reach AI-driven insights faster with Snowflake and AWS.

Additionally, we partnered with leading technology companies to develop solutions for our joint customers across three key use cases:

Product development: Speed up time to market and enable smarter vehicle innovation.

• Concept Reply: Leverage a structured approach to improving manufacturing processes by combining advanced connectivity, real-time insights and generative AI with its Overall Equipment Effectiveness (OEE) blueprint.

• Siemens Digital Industries Software: Accelerate software-defined vehicle (SDV) innovation by enabling digital twin simulations and AI-driven virtual validation to enhance automotive system development, including advanced driver-assistance systems (ADAS) development.

• Mendix: Enable simpler integration, advanced automation and a more cohesive foundation to accelerate transformation with AI-enabled applications on Snowflake.

• Vertex: Access full-fidelity 3D digital twins of products and factories without moving files or locking into a specific computer-aided design (CAD) platform, making it easy to scale immersive work instructions, service diagnostics and other critical use cases beyond engineering on Snowflake.

Supply chain: Enhance resilience, real-time visibility and cost control across sourcing and logistics.

• Blue Yonder: Delivers supply chain control tower solutions to monitor disruptions and optimize logistics in real time on Snowflake.

• Tredence: Leverage  AI/ML models and Snowflake solution accelerators to deliver faster time to value for the supply chain by enabling demand sensing and supplier collaboration, optimizing manufacturing processes and improving visibility for automotive customers.

Production: Drive smart factory initiatives, quality assurance and predictive maintenance.

• DXC Technology: Unlock connected mobility analytics to streamline product quality, such as DTC code root cause analysis (RCA), SDV, parts planning, service/warranty operations and data monetization for automotive companies using Snowflake.

• evolv Consulting: Enhance auto plant-level operations and automate processes.

• Cirrus Link, HighByte, HiveMQ and LTIMindtree: Enable native ingestion of IT/OT data into Snowflake with low network burden to drive IT/OT convergence of shop floor and enterprise data.

• Kipi.ai: Kalibrate, a Snowflake Native App, is able to predict when an equipment or machinery is likely to fail, allowing manufacturers to take preventative measures to reduce downtime and costs related to failures. 

• LandingAI: LandingAI’s LandingLens, a Snowflake Native Application, offers Visual AI for automotive manufacturing, improving quality control efficiency. LandingAI also provides agentic document extraction, accurately processing complex documents like process flow diagrams and inspection reports. This enables faster, data-driven decision-making in manufacturing processes.

• Sigma: Understand trends and even predict anomalies by analyzing high volumes of data natively within Snowflake, enabling manufacturing and supply chain use cases, such as energy optimization, manufacturing insights and quality control automation.

Sales and aftermarket service: Boost revenue, customer experience and data monetization across vehicle ownership.

• Snowflake Marketplace: Provides access to data products such as EV charging station maps, mobility behavior trends and vehicle inventory data to enrich marketing, customer service and operational planning.

Looking ahead, what’s the automotive industry’s next frontier? What’s it going to take to get there?

All auto companies are embarking on an AI-driven digital transformation, leveraging data generated across the vehicle development process. A key to succeeding on this journey is having a robust data strategy and a data management platform agnostic of underlying cloud infrastructure. 

Snowflake can help accelerate this journey with our AI Data Cloud and prebuilt solution offerings. With such full integration of all data, the industry can soon speed toward delivering AI-powered digital twins, fully autonomous fleets and personalized mobility services. 

Join Snowflake’s AI Data Cloud for Manufacturing to access new automotive solutions and partnerships today. And register for our June 17 webinar, Accelerating Automotive Innovation: Drive the Future of Mobility with AI and Data, where Long will be joined by Penske Logistics Vice President of Data Science and Analytics Vishwa Ram for a discussion around the most pressing AI and data challenges and opportunities in the auto industry.