The automotive industry is undergoing rapid transformation as manufacturers and suppliers embrace data as a strategic asset. From the factory floor to the vehicle itself, vast amounts of data are being generated every millisecond—offering opportunities to improve operations, deliver new services, and drive revenue growth.
Yet for many organizations, the challenge isn’t collecting the data—it’s making it usable.
Industry 4.0 set the standard for connected, data-driven manufacturing, but many automotive companies still struggle to gain enterprise-wide insights due to siloed and complex industrial data.
One of the biggest hurdles? Accessing and leveraging operational technology (OT) data from the factory floor.
Typically, automotive manufacturers and suppliers begin their digital transformation by tapping into IT systems, which is a natural starting point—they’re more familiar, more structured, and easier to integrate. However, IT data alone only tells part of the story and doesn’t give manufacturers the information they need to improve factory efficiency and achieve Industry 4.0 use cases.
Common Data Challenges
To unlock use cases like predictive maintenance and real-time quality control, automotive manufacturers and suppliers need access to contextualized OT data. Some common industrial data challenges include:
- Fragmented, siloed data: Industrial data lives across disconnected machines, plants, departments, and systems. This fragmentation prevents a unified view of operations and makes collaboration across teams and systems difficult.
- Lack of context in OT data: Raw industrial data is often unstructured and lacks key business context—such as units of measure, asset relationships, plant location and more—limiting its value for analytics and decision-making.
- Data integration complexity: Integrating data across operational and IT systems can be time-consuming and resource-intensive, slowing down the development of data products, advanced analytics, and AI adoption.
- Change-adverse culture: Manufacturers often face rigid production processes, high operating costs, tight margins, costly quality control inconsistencies, and unplanned equipment downtime. These risks have created cultures that prioritize risk aversion over innovation.
Solution Reference Architecture
To unlock the full value of industrial data, automotive manufacturers and suppliers need more than just a place to store their massive amounts of industrial data—they need structured, contextualized data that’s ready for advanced analytics. They need a solution that breaks down the gap between OT and IT systems and teams and encourages agility. This is why the integrated solution provided by Snowflake and HighByte is so compelling.
The Snowflake AI Data Cloud for Manufacturing helps users integrate data across the automotive lifecycle, reaching into design systems, production environments, connected vehicles, service operations, and warranty systems to provide a holistic view of vehicle performance and customer experience. Snowflake users can democratize access to AI and ML tools across their organization, enabling teams to develop predictive models for vehicle design, production quality, and maintenance without specialized infrastructure.
Yet this requires operations and production data that is usable. It requires industrial data that is accessible, contextualized, and structured for the business.
HighByte Intelligence Hub is a software application that natively runs at the edge, collecting, contextualizing, and transforming industrial data as it is in motion. Users can merge telemetry, transactional, file, and time-series data into a single payload—turning raw data into meaningful “information” that can be ingested by Snowflake.
The Intelligence Hub delivers contextualized data to Snowflake AI Data Cloud, where it can be unified with data from IT systems and analyzed at scale using AI and machine learning.
Because the integration is bi-directional, insights generated in Snowflake can also be pushed back to the factory floor—visualized in a human machine interface (HMI) or dashboard and used to guide frontline operators in “real time.”
Together, HighByte and Snowflake are driving change in automotive manufacturing by reducing operational costs and delivering new data products that break down barriers between business units, systems, clouds, and partners.
VIVIX Case Study
One company that has successfully overcome the challenges of collecting, contextualizing, and utilizing OT data to optimize its operations is VIVIX, a leading glass manufacturer in Brazil supplying the automotive industry.
VIVIX is one of the most modern flat glass plants in the world. Producing around 900 tons of float glass per day in its 90 thousand square meter facility, the company has secured a unique position as the only glass company in Brazil with its own solar plant to supply energy for the glass production facility and its own raw material processing plant. Utilizing a "mine-to-line" method, VIVIX aims to ensure the consistent production of high-quality glass.
After establishing an Industrial Transformation department in 2021, VIVIX set out to modernize its operations and become a truly data-driven organization. Early use cases focused on digitizing manual processes, optimizing temperature control in furnaces, and improving asset maintenance. But to scale these efforts and unlock long-term value, VIVIX needed a solid data foundation.
The Approach
VIVIX implemented a modern Industrial Data Fabric architecture with HighByte Intelligence Hub, AWS, and Snowflake.
- They deployed HighByte Intelligence Hub in the corporate data center to curate, orchestrate, and model data from OPC servers, SQL servers, and other industrial sources at the edge before publishing payloads into the AWS ecosystem.
- They built a highly scalable Industrial Data Fabric architecture using the Intelligence Hub as the DataOps layer and Amazon S3 as the centralized cloud data store.
- They also hosted the Intelligence Hub in the AWS cloud to streamline integrations and transformations between various cloud-based services, including Amazon Bedrock, Snowflake, and business intelligence applications like Mendix.
The Results
By adopting this architecture, VIVIX has seen significant operational improvements. They reduced unscheduled downtime and increased asset lifespan, which reduced operating and maintenance costs, respectively. Teams within the organization are working more collaboratively, speeding up the development of new data products and applications. The company plans to accelerate the adoption of both Machine Learning (ML) and Generative AI to rapidly develop AI products with Mendix and Snowflake. These improvements aren’t just about efficiency—they’re helping VIVIX focus on the bigger picture: Driving digital transformation forward and building a strong foundation for future innovation.
According to Aristóteles Terceiro Neto, Industrial Transformation Manager at VIVIX: “HighByte Intelligence Hub provides an agile solution for industrial data integration. It has fundamentally changed our approach to the way we architect and use data in our plant. With the Intelligence Hub, we've built a digital infrastructure that enables us to improve operational efficiency and make data-driven decisions for the consistent production of high-quality glass. The low-code approach is key. We're excited by the innovation coming out of HighByte and the impact it will have on our business.”
Wrap Up
As the automotive industry continues to evolve, the ability to integrate, contextualize, and act on connected vehicle and OT data is essential to remain competitive.
With HighByte and Snowflake, automotive manufacturers and suppliers gain the tools they need to break down data silos, accelerate digital transformation, and drive smarter decision-making across the enterprise.
Ready to see what unified industrial data can do for your operations? Start your free trial of HighByte Intelligence Hub today. Choose between the 2-hour resettable trial or the 30-day trial, which includes a HighByte team member to guide you through your evaluation.
