# EdgeDataTwin ## Schedule a complimentary consultation Let’s connect to explore how EdgeMethods can help you unlock value, solve key challenges, and accelerate your digital transformation journey. ### EdgeDataTwin ###### **Model physical assets and processes into actionable digital intelligence** EdgeDataTwin creates semantic, graph-based representations of physical assets, systems, and processes — turning operational data into a structured digital model that can be monitored, explored, and acted on in real time. It is a semantic modelling and digital twin product built to make complex environments easier to understand, simulate, and optimise. Most organisations have access to telemetry and system data, but lack a coherent way to represent how assets, components, variables, and processes actually relate to each other. EdgeDataTwin solves this by combining graph models, ontologies, near real-time data, and lifecycle context to create a navigable digital representation of the physical world. Ontologies play a central role in this capability. They provide a formally structured vocabulary and set of relationships that define how different asset types, behaviours, parameters, and events connect. By taking an ontology-driven approach, EdgeDataTwin ensures data is not only captured, but understood — enriched with meaning, context, and standardized semantics. This reduces inconsistency between systems, improves interoperability across data sources, and establishes a shared understanding of how the operational environment behaves. The result is a more actionable foundation for analytics, visualization, optimisation, and AI-driven decision support. ### Core Capabilities EdgeDataTwin transforms raw operational data into a meaningful, queryable model of the physical world. It is designed to solve the practical problems that sit between disconnected data sources and real operational understanding: - data that describes assets but not the relationships between them - limited context around how machines, sensors, components, and processes interact - inconsistent representations of physical systems across tools and teams - difficulty modelling lifecycle, state, and behaviour in a reusable way - limited ability to simulate, optimise, or reason over complex environments By resolving these issues, EdgeDataTwin provides the semantic and graph-based foundation needed to move from visibility to action. - **Graph-based modelling** EdgeDataTwin captures relationships between entities such as machines, sensors, components, and processes in a connected structure that reflects how the real environment works. - **Ontology-driven semantics** Ontologies define the meaning of physical-world concepts in data terms, providing consistent language, structure, and interoperability across systems and use cases. - **Near real-time synchronisation** Telemetry and operational signals keep the twin aligned with current conditions, enabling near-live visibility into state, performance, and change over time. - **Lifecycle representation** EdgeDataTwin can represent design, operation, and maintenance states, helping organisations connect what was intended, what is happening, and what needs to happen next. - **Simulation and optimisation readiness** By combining semantic models with real-time data, EdgeDataTwin creates a foundation for predictive maintenance, performance optimisation, scenario modelling, and autonomous decision support. ### How it works - **Ingest → Model → Relate → Synchronise → Visualise → Act** **Ingest** – Data is collected from sensors, enterprise systems, operational platforms, and external sources to create a high-quality representation of the physical asset or process. **Model** – A semantic data layer is created to represent the physical world in data terms, including entities, attributes, states, and behaviours. **Relate** – Graph models and ontologies define how assets, components, sensors, and processes connect to one another. **Synchronise** – Real-time telemetry keeps the model aligned with live conditions, enabling current-state monitoring and ongoing updates. **Visualise** – The twin can be explored as a navigable, queryable digital representation of the physical environment. **Act** – The model supports analytics, simulation, optimisation, and decision-making across operational and enterprise use cases. ### Why it matters Customers are not really asking whether they can build dashboards. They are asking: - How do we create a single, trusted understanding of our assets, processes, and operations so we can act faster and make better decisions? Which means asking: - How do we make sense of all the data we already have? - How do we create a consistent understanding of assets, processes, and operations across different systems and teams? - How do we connect data from systems that do not speak the same language? - How do we understand how assets, components, variables, events, and processes actually relate to each other? - How do we get context, not just raw data? - How do we simulate or predict what might happen next? - How do we trust our data enough to automate decisions and enable AI safely? EdgeDataTwin answers these questions by turning operational complexity into a semantic, graph-based model that can be explored, analysed, and acted on. ## Business Benefits **Semantic, real-time models for better decisions and stronger operational control** EdgeDataTwin helps organisations: - improve understanding of how assets, components, and processes interact - create interoperable, reusable representations of complex environments - support predictive maintenance and performance optimisation - test scenarios virtually before making physical changes - strengthen analytics, visualisation, and AI with richer operational context - reduce risk by enabling simulation and proactive decision support ### Summary ###### **Turn data about the physical world into a model you can understand and act on** EdgeDataTwin combines graph models, ontologies, lifecycle context, and real-time telemetry to create a semantic representation of assets, systems, and processes — helping organisations monitor, simulate, optimise, and make better decisions. **EdgeDataTwin creates graph-based, ontology-driven digital representations of physical assets and processes so data becomes actionable, navigable, and ready for optimisation, simulation, and AI.