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Microsoft Azure Digital Twins and Element Unify: Accelerated Deployment for Faster Value

June 1, 2022

Summary

Element is pleased to announce the Element Unify integration with Microsoft Azure Digital Twins. The integration delivers several key benefits to organizations looking to build digital twins:

  • Accelerate the deployment of digital twin models through the use of Unify templates (i.e. assets, equipment, etc.) and attributes which are translated to Digital Twins Definition Language (DTDL) interface Telemetry, Properties, Components and Relationships. These DTDL objects are published to Azure Digital Twins as interfaces. The Unify Graph models are published to Azure Digital Twins as digital twin instances and relationships.

  • Ensure that the digital twin effectively mimics the physical reality and delivers sound analytics in a live execution environment. This is accomplished through synchronizing Azure Digital Twins and the Unify semantic data model.
  • Leverage pre-existing Azure Digital Twins models by importing to Unify allowing reuse and governance in Unify’s semantic data model. Following DTDL translation the results are saved in Unify's Template Library and manifest in Unify Graph.

Digital Twins: Powered by Data

Digital twins appear to be rapidly gaining resonance with organizations looking to try and adopt digital twins to address a myriad of business objectives spanning a full gamut of use cases.

Digital Twins help address business challenges by tying together IT/OT/ET data

It is worth bearing in mind how various data come together in a digital twin to deliver business value. A good way to think about it is in terms of four levels of structuring data.

Data Journey: From Raw to Consumption
  1. IT/OT/ET Data: At the lowest level is what might be considered the “core” or raw fact data. Typically siloed, it spans structured, semi-structured and unstructured data types. Examples include time series data from equipment sensors that might be stored in a historian, maintenance data from enterprise asset management (EAM) systems, and as-designed engineering data from say, piping and instrumentation diagrams (P&IDs).
  1. Data Model: Relates (or contextualizes) the data from the various IT/OT/ET sources. Ideally, a computable, semantic data model that allows the upper (Analytical and Consumption) layers to flexibly use the core data. In a sense this is the heart of (or brains behind, if you prefer that phrase) making the digital twin’s use of the data effective and efficient. Done well, the model is easy to build and update to  keep in sync with the physical reality.
  1. Analytical Model: Encodes how the data model is to be acted upon to generate insight. This includes not only considerations of set points and operating limits but also rules (or calculations formulae) for computing derived values or even advanced algorithms for simulations and predictions under different conditions. 
  2. Consumption: Some may think of this as the level at which the “rubber hits the road”. I like the food analogy, i.e. having sourced all the right ingredients and prepared them the right way, consumption (or eating) is where the value is experienced. The consumption may for example, be in the form of a visualization so that you can see that all is well at the plant and no action is called for, or as automated alerts that a problem state is nearing, or perhaps it may be a routine to drive autonomous action to improve a production outcome.

How Unify and Azure Digital Twins Help

Microsoft Azure Digital Twins is a cloud service for creating digital twin models of physical and logical  assets (buildings, factories, farms, energy networks, people, and processes) that describe the asset’s contents  and relationships (properties, components and inheritance).

Element Unify provides platform capabilities for industrial data ingestion, data contextualization, the creation and maintenance of a semantic data model and data governance. Unify’s no-code connectors access source system data and deliver data models to apps and services like Azure Digital Twins. No-code data pipelines utilize data transformations and functions to contextualize IT/OT/ET data and auto-sync for up-to-date source system representation. The Unify Graph stores relationships across all data in a computable semantic data model to enable flexibility, rapid schema materialization, data governance and re-use.

As such, Unify fulfills a valuable role in the four stage model of how data goes from core (or raw) data to consumption by providing a semantic data model that acts as an “intelligent glue” layer. Glue, because it serves to tie data sources together. Intelligent, because of its ability to be flexible, be updated as needs evolve and stay in sync with the physical reality that the twin seeks to mimic.

Unify provides the Semantic Data Model

The integration of Element Unify with Azure Digital Twins delivers several key benefits to organizations looking to build digital twins:

  • Accelerate the deployment of digital twin models through the use of Unify templates (i.e. assets, equipment, etc.) and attributes which are translated to Digital Twins Definition Language (DTDL) interface Telemetry, Properties, Components and Relationships. These DTDL objects are published to Azure Digital Twins as interfaces. The Unify Graph models are published to Azure Digital Twins as digital twin instances and relationships.

  • Ensure that the digital twin effectively mimics the physical reality and delivers sound analytics in a live execution environment. This is accomplished through synchronizing Azure Digital Twins and the Unify semantic data model.

  • Leverage pre-existing Azure Digital Twins models by importing to Unify allowing reuse and governance in Unify’s semantic data model. Following DTDL translation the results are saved in Unify's Template Library and manifest in Unify Graph.

I recommend watching the short demo video below to get a further sense of how Unify and Azure Digital Twins work together. It also includes a visualization based on the newly announced Azure Digital Twin 3D Scenes capability. 3D visualizations are by no means a must have aspect of digital twins but sure can add a powerful new dimension of utility.

Unify for Azure Digital Twins methane emissions monitoring demo

Unify has a range of pre-built connectors (e.g. for Azure Blob Storage, Azure Data Lake Storage, JDBC Connector for Azure, etc.) that make it simple to ingest data or publish models within an Azure cloud context.

Element Unify includes pre-built connectors for Azure Services

The Execution End of Things

Our team has been working closely with the Microsoft Azure Digital Twins team both on the integration and the corresponding reference architecture. Read the Microsoft technical blog to learn more. The collaboration has been great, and I feel, exactly what our customers deserve.

Element Unify and Azure Digital Twins Reference Architecture

I encourage you to check out the resources below and reach out with your digital twins questions.

Learn more

Additional Resources