Digital Twins Help Unlock Sensor Data in Industrial Environments

by Brad Johnson, on Sep 13, 2017 10:27:51 AM

Recently, the Swim team posted Key Takeaways from Gartner’s Hype Cycle for Emerging Technologies 2017, which referenced technologies like Edge Computing, Deep Learning, Machine Learning and a few other automation and data-centric technologies that are transforming industrial environments around the world. However, there’s another emerging technology that Gartner included, but we have not mentioned in prior posts which is the concept of the “Digital Twin.”

Digital Twin_No URL

Digital Twin sounds like the plot out of a Sci-Fi movie, but it’s actually a concept that makes it easier to build applications that use real-time sensor data. According to Gartner, “a Digital Twin is a digital representation of a physical object. It includes the model of the physical object, data from the object, a unique one-to-one correspondence to the object and the ability to monitor the object.” In other words, the Digital Twin is a virtual avatar that mirrors the state of a physical device or sensor. This decouples the physical sensor from the data it is creating, allowing applications to subscribe to the data from a sensor without having to worry about integrating with the physical sensor itself.

The data flow model for an industrial system utilizing the Digital Twin model looks something like this:

The applications where it makes sense to implement a Digital Twin model are numerous. Any system with multiple sensors producing data for use in n-number of applications may benefit from the logical separation of device from device-data. Examples include industrial environments such as manufacturing and distribution centers, Smart Cities, Intelligent Transportation Systems (ITS), fleet management applications, and more. In all of these situations, applications rely on a myriad of sensors and data sources to establish how the system is performing. As Gartner explains, the “physical objects that CIOs and their business unit stakeholders can apply a Digital Twin to are broad. They range from people, things and places to complex environments, such as buildings, factories or cities.”

The benefits of utilizing the Digital Twin model are clear; Digital Twins make it easier to integrate sensor data into applications, and do not require developers to have access to the devices themselves. Gartner explains that “Digital Twins offer strong potential to achieve better insights on their objects and drive better decisions.” Because Digital Twins can be more easily composed within an application, developers can model the real-world systems, while abstracting away the complexity of dealing with physical devices. This helps application developers to deliver more granular, and potentially more relevant insights.

Digital Twins also have benefits for operations professionals. Digital Twins allow for contextual feedback loops between devices and their Digital Twins; if an issue is observed it can be dealt with at the edge or escalated to a higher order depending on available context. Should a physical device fail, the application continues to work, as the Digital Twin is still available. When the device is fixed, or a new device replaces the failed one, the Digital Twin can resume with all prior context already established.

According to Gartner, “digital twins add value to traditional analytical approaches by improving situational awareness, and enabling better responses to changing conditions, particularly for asset optimization and preventive maintenance. They can lower operating expenses and potentially capital expenses too, by extending the life of the object they represent and optimizing the performance of the asset as it runs.” If you are experiencing issues in these areas, it may be worth exploring the use of Digital Twins in your industrial environment.

Learn More

Learn how SWIM uses Digital Twins to optimize and alleviate the data overload created by industrial devices and sensors. Swim ESP can help transform and manage your sensor data in real-time, applying Machine Learning to sensor data at the edge. Transform your flood of data into actionable business insights.


Shetty, Sony. “How to Use Digital Twins in Your IoT Strategy.” Smarter With Gartner, Gartner, 8 June 2017.

Topics:Machine LearningSWIM SoftwareIndustrial IOTEdge AnalyticsDigital Twin