Transforming IT with Digital Twin Technology
Due to the rapid evolution of IT infrastructure, business organizations are in a never-ending struggle to keep pace. However, making changes and upgrades to mission-critical IT systems can carry a great deal of risk. After all, a single network outage or critical equipment failure can bring operations to a halt. It is enough to give IT decision-makers nightmares.
Fortunately, there is a solution. By utilizing digital twins, IT managers and decision-makers can model changes and tinker with new technological approaches without risking critical infrastructure or disrupting operations. In this article we explore the ins and outs of digital twins, as well as how to create them, secure them, and integrate them with other enabling technologies. Let’s get started.
What is a Digital Twin?
A digital twin refers to a virtual or digital replica of a real-world object or system. For example, a business might create a digital twin of a proposed new product, the control systems in their office building, or even of the machines in a production line on a factory floor. The purpose of a digital twin is to provide businesses with a realistic simulation they can use for testing, to feed an AI system for predictive maintenance, or for any other task too disruptive or risky to perform on the real thing.
There are typically two kinds of digital twins that a business might use. One would be a digital twin that represents a real-world object or system and draws from a static snapshot of that object or system. The other is a digital twin that relies on real-time data coming from sensors and monitoring equipment connected to its real-world counterpart. The latter is significantly more complex to set up but has far more potential use cases.
Designing and Implementing a Digital Twin
The task of designing and implementing a digital twin varies depending on the real-world object or system a business is trying to replicate. For the purposes of this article, let us suppose a business wishes to build a digital twin of its main office computer network.
In that case, the process would begin with the building of a comprehensive map of every part of the network. It would require every model number of every switch, router, device, and anything else attached to the network. It would also require a complete accounting of how every bit of hardware connects back to the rest of the network.
From there, using specialized software, a simulation of the real-world network to serve as its digital twin is constructed. Or, for more elaborate simulations, a business might instead turn to a real-world simulation using World Wide Technology’s Advanced Technology Center (ATC) or a similar solution.
Using the digital twin of its network, the business can then model changes and upgrades to assess their impact on operations before committing to them. Or, they could have the twin ingest real-time statistics and operational data from the real-world network to create a proactive maintenance schedule, using simulations to see which components might soon buckle under their workloads.
Ensuring the Data Security of a Digital Twin
Any organization that wishes to utilize a digital twin must also grapple with the data security implications inherent in the process. After all, a digital twin could include proprietary product design data, privileged information from inside the business’s networks, or all manors of other sensitive or secret information.
The good news is that securing digital twins does not require procedures that would be unfamiliar to organizations that are already working to protect their data and digital systems. Securing a digital twin starts with basic cybersecurity hygiene, including implementing carefully managed access protocols, strong authentication methods, and following the principle of least privilege (PoLP) at every turn.
For digital twins that utilize real-time data streams, it is also a good idea to harden the communication channels between the digital twin and its real-world counterpart. That effort must include strong end-to-end encryption, as well as encrypting all data while at rest within the digital twin system. Doing so should ensure robust protection against data exfiltration or other types of attacks, making any stolen data useless to an attacker.
Monitoring and Managing Digital Twins
Making effective use of a digital twin requires the deployment of a monitoring and management infrastructure to process the digital twin’s data output. Fortunately, most of the platforms businesses use to house their digital twins also include robust monitoring and management functionality. For example, Microsoft’s Azure digital twin platform, which is a popular option for businesses that rely on IoT-enhanced digital twins, contains a robust monitoring and metrics toolkit that businesses can use for the task. The same is true of other popular platforms like Amazon’s AWS IoT TwinMaker and Google Cloud’s digital twin offering, although the latter is limited to the modeling of supply chains and the like.
Integrating Digital Twins with Other Technologies
It is important to recognize that the most impactful uses of digital twins depend on an array of enabling technologies. This means how a given business integrates those technologies with its digital twins plays a big role in how big a benefit they may derive from them.
Currently, the two most important enabling technologies connected to digital twins are Internet of Things (IoT) sensors and devices, as well as artificial intelligence (AI). IoT devices can enable the collection of real-time data that can turn a digital twin from a static replica into a living, breathing, copy of its real-world counterpart. That data enables a digital twin to be far more useful because it makes any simulations carried out on it all but indistinguishable from the real thing.
AI, on the other hand, plays an important role in making the most of the data businesses can collect from a digital twin. For example, businesses can use AI to look for insights on how they might improve a twinned production process or procedure, or to forecast network demand to keep ahead of the business’s evolving needs.
AI is also a popular enabling technology for businesses that use digital twins for predictive maintenance purposes. In that role, AI can help businesses to replace components or parts before they fail without doing so prematurely. That helps them hold down costs while perfecting system reliability throughout their operations.
On-Demand Digital Twin Expertise
The concept of digital twins is vast, encompassing near-endless use cases and applications. There is far more to learn than could ever fit into a single article. Fortunately, the experts here at Outsource IT have the knowledge necessary to help any business stand up a digital twin initiative and use it to further their ends. Whether it is designing and executing a digital twin project or configuring and managing the cloud infrastructure one requires, Outsource IT has you covered. For more information about how Outsource IT can help make digital twins a part of your company’s future, contact one of our knowledgeable account managers today.