AWS and Hexagon: Advancing Digital Twins, Enterprise Asset Management, and Industrial Data Fabrics
AWS and Hexagon: Advancing Digital Twins, Enterprise Asset Management, and Industrial Data Fabrics
The transformative role of cloud computing and AI is accelerating in asset-intensive industries as collaborations across solution-provider ecosystems expand. ARC Advisory Group notes that the essential role of platforms for optimal industrial data fabrics is exemplified by recent developments from AWS (Amazon Web Services) and Hexagon.
AWS and Hexagon have partnered to enable rapid adoption of industry-specific Enterprise Asset Management (EAM) solutions for energy, utilities, manufacturing, and transportation, delivering highly secure, AI-driven insights and process improvements.
To realize AI’s full potential, industrial data fabrics require platforms that optimize insights for specialized use cases and de-silo data-centric workflows. This platform approach fosters cross-functional collaboration and provides deeper, more scalable, and more secure insights.
Real-time operations and maintenance data—from text and numerical sensor readings to visual media captured by drones and cameras—creates opportunities for improved management and has accelerated the development of new platforms.
Historically, limited compute constrained scalable, cost-effective deployments. Building digital twins from spatial data demands significant computational power that rises with model size and data complexity. Storage and access are also challenging, with files ranging from gigabytes to terabytes or even petabytes. Distributed data capture and construction teams require remote collaboration and access to massive datasets for visualization and analysis.
As part of its AWS partnership strategy, Hexagon leverages Amazon Bedrock Data Automation to create specialized AI solutions, including HxGN Alix, an AI assistant for EAM built on Amazon Bedrock.
HxGN Alix is among the enhancements in Hexagon’s latest EAM release, HxGN EAM 12.2. It provides step-by-step guidance for managing assets, creating work orders, and generating reports. By supporting both foundational and advanced EAM use cases, it helps users be more self-sufficient. It also lets end users generate new code in Python, JavaScript, and Java, with security protocols protecting sensitive data.
Many EAM workflows and asset models depend on Geospatial Information Systems (GIS) and digital twins—an area where Hexagon’s HxDR is pivotal.
Hexagon’s HxDR on AWS: Digital Reality Visualization
HxDR uses airborne, ground, and mobile sensor data—such as LiDAR and photogrammetry—to create digital representations of physical locations. These digital twins support project design and 3D visualization while addressing the computational and storage demands of model creation. For example, a handheld imaging laser scanner like the BLK2GO can produce large files (averaging 5 GB) that are uploaded to Amazon S3. HxDR then uses these files and AWS services to create integrated views, enabling remote collaboration.
AWS supplies collaboration-enabling infrastructure to manage industrial data fabrics’ complexity. Its platform tools, storage services, and elastic compute capabilities allocate resources on demand, removing the need for local resource pools or lengthy approvals.
In sum, the AWS–Hexagon partnership highlights key trends in optimizing platforms that support industrial data fabrics. These initiatives enable Enterprise Asset Management and Digital Twin benefits to be realized more deeply, driving digital transformation, improving operational efficiency, enhancing safety and reliability, and reducing costs.
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