We are excited about last week’s announcement of the general availability of AWS IoT TwinMaker. In this post, we will discuss how Element Unify and AWS IoT TwinMaker combine to help analytics teams accelerate their digital twin projects.
Let’s first establish a common understanding of the term ‘digital twin’ since there are many definitions among our customers and stakeholders. Digital twins are digital representations of physical systems that are dynamically updated with real-world data to mimic the structure, state, and behavior of the systems they represent to drive business outcomes. These digital replicas must live virtually and reflect the true state and behavior of the real-world systems. Industrial operations go through a long lifecycle. Factories and plants exist for years and decades. The digital twin must reflect the physical reality of that lifecycle and be continuously updated as the industrial operation evolves.
We are speaking with more and more customers about digital twins across many industrial sectors including manufacturing, power and utilities, energy (both hydrocarbon-based and renewable), and construction. What's driving a lot of these conversations is the need to improve the effectiveness and reduce the complexity of conducting operations (often with aging infrastructure) and meeting financial target and regulatory mandates. Industrial enterprises are using digital twins to help production managers, operators, and engineers make better and more timely data-driven decisions.
Building and managing digital twins can be time consuming, complicated, and costly. It requires developers with specialized skills working together to build custom, integrated solutions that combine data from different sources, generate live insights from streaming data, and create contextualized visualizations to better connect end users to the data.
AWS IoT TwinMaker makes it easier for developers to create digital twins of real-world systems providing them the ability to use existing data from multiple sources, create virtual representations of any physical environment, and combine existing 3D models with real-world data so that they can deliver against business objectives such as:
Using data from disparate sources and building and maintaining a data model of complex real-world systems are the cornerstones for any digital twin. The key to success on the data and model front is a scalable workflow to integrate and contextualize data from disparate IT/OT sources.
Element Unify transforms siloed IT/OT data into contextualized, knowledge graph-based models to support digital twin development. The efficacy of a digital twin is critically dependent upon the data fed to it. Built with industrial organizations in mind, the Element Unify data platform uses automated, no-code data pipelines to integrate and contextualize IT/OT data. The Unify integration with AWS IoT TwinMaker delivers significant benefits with it comes to building digital twins:
With Unify, users can easily connect to multiple data sources using pre-built connectors, and then integrate and contextualize the data using no-code pipelines. These pre-built connectors and the no-code model development environment greatly eliminate manual effort which in turn reduces time to getting the digital twin operational. The resulting data models can be explored locally in graph form within Unify both visually and using graph query language.
From Unify, you can publish graph models to AWS IoT TwinMaker in the form of components, documents, and parameters. AWS IoT TwinMaker then provides a plugin for Grafana to make it easy to integrate the digital twin into a web application for plant operators and maintenance engineers to monitor and improve industrial operations.
Watch the Unify / AWS IoT TwinMaker integration demo video:
Questions? Please contact us.