To gain the organizational insights and greater agility the mirrored world promises, you first need a comprehensive and robust data foundation for your twins. When intelligent twins are connected in mirror environments, they are a powerful way to turn data into actionable, big-picture insights. But incomplete or incorrect data will lead to false conclusions.

High-quality historical data is critical for intelligent twins — it’s what the twin uses to monitor real-time machine performance, build models of business processes and high-value assets, and more. But COVID-19 has made historic data increasingly unreliable. Everything from traffic and shopping patterns to energy consumption and international travel changed abruptly due to the pandemic. These anomalous changes in behavior and activity patterns have sent many machine learning models that have been trained on “normal” behavior off course, impacting supply chains, inventory management, marketing, and more. Going forward, enterprises cannot rely on historic data blindly — they need to check and correct their models as the world changes.

On top of historical data, federal agencies need a strategy for real-time data collection, or they'll miss out on the real-time analytics intelligent twins can provide. There are two sides to this: investment in sensors and IoT devices to collect data and the tools to prepare, analyze, and visualize the massive amounts of information gathered. Today, many agencies are already investing in IoT devices and sensors, but some struggle to fully utilize the data these devices generate. New cloud-based services and platforms are being developed to bridge this gap and help enterprises achieve real-time insights. Snowflake, for instance, which Barron’s recently described as a “growth juggernaut,” offers clients data warehousing as a service, which can load continuously generated real-time data, requires no manual effort, and can even digest semi-structured data.

From there, intelligent twins can make real-time data actionable in the moment, as many of the examples above illustrate. Going even further, some enterprises are starting to explore how multiple intelligent twins, connected in mirror environments, can use real-time data to safely increase autonomy. GEMINA (Generating Electricity Managed by Intelligent Nuclear Assets) is a U.S. Department of Energy program funding research projects that use AI and digital twin technology to increase the flexibility and autonomy of nuclear reactor systems and reduce operation and maintenance costs. Two of the projects to receive funding are tied to GE Hitachi’s BWRX-300 boiling water reactor design. GE Research intends to move from time-based to condition-based predictive maintenance, which will lead to significant savings. To make this possible, they will develop an array of digital twins for continuous monitoring, diagnostics, prognostics, and early warnings for the reactors. They will also develop a "Humble AI" framework that defaults to a safe operation mode when confronted with situations the algorithm does not recognize. In doing so, the system ensures the secure handling of uncertainties and increases the feasibility of more autonomous operations.

As they continue building out their mirrored worlds, agencies will also need to think about data integration across multiple twins or multiple sub-components that feed into a single twin. API connections can help achieve that data synchronization, enabling different twins or components to connect and interact.

Today’s federal agencies are not self-contained; they rely on partnerships, co-experimentation, and collaboration with other agencies, academia, industry partners, and stakeholder groups, and this must be reflected in the mirrored world. It’s not always enough to have a real-time view of what’s happening within your own organization. The full picture includes what’s happening with the supply chains, vendors, research organizations, and interagency partners that you rely on too.

Europe is demonstrating one way that the mirrored world can increase big-picture visibility with its DigiTwins initiative, which aims to revolutionize healthcare by creating digital twins of individual patients that will enable healthcare decision-makers to identify optimal therapies, preventions, and health maintenance programs. The idea is to use digital twins of individual patients to safely simulate many treatments and outcomes, cheaply, and quickly before critical decisions are made. To do this, the DigiTwins initiative — which is supported by more than 200 partners from 118 academic and clinical research institutions and companies in 32 countries — is leveraging the vast knowledge base of its participating subject matter experts and organizations.

Here in the U.S., the Department of Energy’s National Renewable Energy Laboratory (NREL) has developed a modeling and simulation toolkit that can create a digital twin of an urban area to assist researchers and city planners in quantifying the advantages and disadvantages of various transportation options. The Automated Mobility District (AMD) toolkit can create digital twins of the transportation systems in selected urban districts with which it can assess the mobility and energy impacts of various transportation options. “The AMD Toolkit moves past the basic analysis of connecting point A to point B,” said NREL researcher Stan Young. “We are looking at accessibility of resources in the district — such as food, healthcare, entertainment, and employment — to its inhabitants and to outside visitors.” In one example, the toolkit analyzed the impact of deploying a half-dozen shared automated vehicles (SAVs) at Clemson University’s International Center for Automotive Research in Greenville County. The study found that adding the electric-powered SAVs would result in fuel savings of between 11 percent and 38 percent. But it also found that the addition of SAVs didn’t improve the vehicle miles traveled, occupant-free miles traveled, or travel time.