The future of industrial AI will not be defined by who collects the most data. It will be defined by who can make that data understandable, actionable, and meaningful.
Today, connected devices generate vast amounts of telemetry. Yet businesses do not operate devices—they manage assets, operations, and business outcomes. Without a way to connect telemetry to operational reality, critical context is lost.
Just as importantly, every new device, sensor, or equipment type often introduces additional integration work. Teams spend valuable time building custom mappings, translating payloads, and recreating operational context before they can deliver business value.
That’s why we’re excited to announce the Public Preview of Data on Assets, a foundational capability within Cumulocity’s native semantic layer that transforms raw machine telemetry into structured operational knowledge.
By linking telemetry directly to standardized asset definitions, relationships, and business context, Data on Assets helps organizations onboard and integrate new devices significantly faster while creating a reusable operational foundation for analytics, automation, enterprise systems, and AI applications.
Why Operational Context Matters
Industrial organizations continue to invest heavily in connected products, equipment, and AI initiatives. Yet many continue to face the same challenge: integrating new devices and operational systems remains unnecessarily complex.
Traditional device-centric architectures tightly couple connectivity, data, and applications. Every new device type often requires custom mappings, application-specific logic, and repeated efforts to translate telemetry into business-relevant information.
As organizations scale, teams frequently spend weeks integrating devices and recreating context across dashboards, workflows, analytics platforms, and enterprise systems.
The result is a growing “translation tax” that increases implementation costs, slows solution delivery, and limits reusability across projects.
At the same time, AI adoption is accelerating across industrial environments. Yet the same challenge applies: AI systems require operational understanding, not just raw telemetry.
Most industrial data exists as machine-generated telemetry with limited operational meaning, forcing applications and AI systems to infer context themselves.
Simply put:
Devices produce telemetry, but businesses operate structured assets.
Introducing Data on Assets
Data on Assets addresses these challenges by serving as a foundational capability within Cumulocity’s native semantic layer.
As telemetry enters the platform, it is linked directly to standardized asset instances and enriched with operational relationships, metadata, and business context. The result is structured operational knowledge that reflects how organizations actually manage their operations.
Rather than forcing every downstream application to interpret device-specific payloads, Data on Assets creates a common operational language across devices, assets, enterprise systems, applications, and AI solutions.
This means organizations can define operational context once and reuse it everywhere.
New devices can be integrated faster, applications can be delivered more efficiently, and business logic can be applied consistently across entire fleets without rebuilding context for every project.
Connectivity and business understanding can evolve independently while maintaining a consistent view of operational reality.
Accelerate Device Onboarding and Solution Delivery
Data on Assets transforms telemetry into a reusable operational foundation.
By associating incoming data streams with real-world assets, organizations gain:
- Standardized asset models across heterogeneous fleets
- Faster onboarding of new devices and equipment
- Reduced integration complexity and custom mapping effort
- Consistent operational views across applications
- Simplified enterprise integration
- AI-ready operational data by design
- A stable foundation for future AI-driven operations
Instead of rebuilding context for every project, organizations define it once and reuse it everywhere.
The outcome is structured operational knowledge that can be consumed directly by dashboards, automation workflows, smart rules, enterprise systems, analytics applications, and future AI capabilities.
Deliver Business Outcomes Faster
The value of Data on Assets extends beyond architecture. It directly accelerates solution delivery and operational impact.
Asset-Based Dashboards
Teams can visualize and manage fleets of assets using common asset definitions rather than individual sensor mappings. Whether managing pumps, production lines, generators, or refrigeration systems, users gain a consistent operational view aligned with business reality.
Asset-Level Smart Rules
Organizations can apply automation and monitoring logic at the asset level rather than the device level. This improves scalability, simplifies maintenance, and reduces operational complexity as deployments grow.
Enterprise Integration Readiness
By transforming telemetry into structured operational knowledge, Data on Assets creates clean operational models that integrate naturally with ERP systems, CRM platforms, FSM solutions, analytics environments, and enterprise data platforms.
Enterprise applications consume business-relevant asset information rather than interpreting device-specific telemetry.
Because asset context is standardized, new integrations can be delivered faster and reused more broadly across the organization.
AI-Ready by Design
AI systems perform best when operating on structured operational knowledge rather than massive volumes of low-context telemetry.
Without context, AI models must infer relationships, operational structures, and business meaning from raw data streams. This increases token consumption, raises implementation costs, and can reduce reliability.
Data on Assets addresses this challenge by contextualizing telemetry before it reaches AI systems.
This provides:
- Reduced token consumption
- Lower AI infrastructure costs
- Improved AI accuracy and reliability
- Reduced hallucination risk
- Sustainable AI operations at scale
By replacing noisy telemetry streams with contextualized asset information, organizations can build more efficient and trustworthy AI solutions while protecting long-term AI operating margins.
Just as importantly, Data on Assets establishes the structural blueprint that future Expert Agents will require to safely understand, navigate, and interact with physical enterprise environments.
Join the Public Preview
Data on Assets represents a foundational step toward faster, more scalable industrial operations.
By transforming telemetry into structured operational knowledge, organizations can dramatically reduce the effort required to onboard new devices, integrate enterprise systems, and deliver new solutions.
Dashboards, workflows, automation systems, enterprise applications, analytics platforms, and AI solutions can all operate using a shared understanding of assets and operations.
What previously required weeks of custom integration, manual mapping, and repeated context-building can now be delivered through a reusable operational foundation.
The future of AIoT belongs to organizations that can rapidly connect, contextualize, and operationalize data across their business.
With the Public Preview of Data on Assets, we’re making that future available today.
How to get started
Data on Assets is currently available as a Public Preview feature.
If you are interested in trying it, go to Administration → Ecosystem → Microservices and verify that dtm-data-service is subscribed to your tenant. If it is not available, contact Global Support or your tenant administrator.
Before using the service, ensure that the tenant option “notification2.tenant.all.apis” is enabled. Once configured, create data point links using the DTM data point links. The service will then automatically propagate incoming device measurements to the linked assets in the Digital Twin hierarchy, making operational data available directly in asset context.