Kyle Cline is a Product Development expert specializing in Telematics Big Data Analytics and the Industrial Internet of Things. As an IoT Analytics Manager at Cat Digital, the digital technology arm of Caterpillar, Inc., he leads a team of data scientists focused on machine analytics and predictive modeling. These efforts analyze how over a million connected Caterpillar machines are utilized by customers. With a degree in Electrical Engineering, Kyle spent 12 years in Product Development, designing, building, and testing new motor grader products. He also worked in Piracicaba, Brazil, as a technical liaison for Caterpillar’s largest South American manufacturing facility. At Cat Digital, Kyle leverages his engineering background and expertise in analytics to enhance Caterpillar’s global product and service offerings.
Dr. Andrei Khurshudov is an expert in Big Data Analytics, the Industrial Internet of Things, cloud storage and computing, in-memory computing, and data storage technology. As Director of IoT Analytics at CAT Digital, he leads a team focused on data analysis and predictive modeling for over a million connected machines and devices. Andrei previously spent over a decade at Seagate Technology as a Chief Technologist, managing R&D in big data analytics, cloud storage, and computing. His experience includes roles as Chief Data Officer at Formulus Black and CTO at Alchemy IoT, where he developed cloud-based analytics solutions. Holding a Ph.D. in Engineering, Andrei has worked at IBM, Hitachi Global Storage, and Samsung, contributing to numerous publications, patents, and conference presentations.
Caterpillar Inc. has been a leader in heavy machinery for over 95 years, helping customers build a better world. With onboard computers, sensors, and cameras, more than a million connected assets transmit data, enabling large-scale IoT analytics. This data, collected from job sites worldwide, includes time-series data, machine health alerts, fuel usage, GPS, and operator-specific usage. Managing this Big Data requires infrastructure and specialized skills from data scientists, analysts, and product engineers to develop the next generation of products and services.
The industrial sector is increasingly reliant on connected devices and data analysis to improve operations, business decisions, and product development. Two primary approaches are commonly used to process and analyze this data. The first approach involves raw data access for highly skilled users, providing flexibility in analysis but requiring specialized expertise in programming and data science. The second approach focuses on status dashboards designed for broader audiences, offering easy-to-interpret insights but limited analytical depth. Each approach has its drawbacks, as the first requires significant data expertise while the second lacks advanced modeling capabilities.
Many engineers need a solution that combines depth and flexibility with efficiency and ease of use. A third approach, called the “Library of Solutions,” addresses these challenges by providing reusable analytics tools connected to clean data. Engineering teams identify common analytical needs, and standardized tools are developed within a web-based application. This solution enables engineers to quickly access validated data for modeling and analysis while maintaining access to raw data for advanced users.
A common IoT backbone connects assets to an IoT platform, where data is stored and processed. Traditionally, this data is either fed into dynamic dashboards or made available in raw form for analysis. The Library of Solutions approach integrates modular analytics tools into cloud-based environments, offering scalable computational power and pre-curated datasets. Engineers benefit from streamlined analytics, reduced data preparation time, and improved consistency and accuracy across analyses.
This approach provides several key advantages. Engineers, regardless of their data analytics skills, gain easy access to relevant analytical tools. Reusable elements ensure consistency and accuracy in analytics results while accelerating custom analyses. Cloud-based deployment offers scalability to meet speed and budget requirements. With data pre-processed and curated, engineers can focus on insights rather than data preparation. The result is improved R&D efficiency, faster time to market, reduced warranty costs, and better alignment of products and services with customer needs.
An up-to-date library of modular analytics solutions democratizes data analysis, accelerates the R&D process, reduces investment costs, and enhances efficiency. This structured approach ensures that Caterpillar engineers and decision-makers leverage data-driven insights to optimize products and services in alignment with customer demands.
