There’s some growing skepticism in the industry around digital twins, and for good reason. Too many implementations stop at visualization and never become the operational systems they were designed to be. The problem isn’t the technology; it’s the angle of attack.
“When I first started learning to fly, I thought a stall meant something was wrong with my speed,” says Steve Mulberry, a 37-year GIS veteran at NV5 with experience spanning the U.S. Marine Corps, Esri, and state and municipal agencies.
“A lot of organizations think the same way about digital twins, that they need to move fast to get to the operational outcomes. Speed has nothing to do with it.”
What causes a stall, in aviation and in digital twin programs alike, is the wrong angle of attack.
Organizations that prioritize technology deployment and speed over business outcomes find themselves with a sophisticated visualization tool that no one acts on. NV5 explores this challenge in depth in their latest e-book on connected data ecosystems and digital twin strategy, read on for a preview of the key ideas.
Visualization Is the Foundation, Not the Finish Line
One of the most persistent misconceptions in the industry is that a digital twin is a 3D model, but it isn’t. It may be built on one, but it doesn’t end there.
“To visualize your infrastructure – your water mains, your telephone poles, your buildings – from a digital perspective is huge. It’s the foundation,” Mulberry explains. “But it shouldn’t stop there.”
The real value emerges when that digital foundation is integrated with GIS, IoT sensors, work order management systems, financial platforms, and other operational data sources. Organizations that skip this step end up with a model that shows them their infrastructure but doesn’t help them manage it.
The integration layer is where many initiatives stall, not because it’s technically impossible but because teams try to connect everything at once before seeing any value. The better approach is incremental: start with the visualization, then layer in the integrations that matter most to your specific business outcomes.
Simulation vs. Operational Execution
A mature digital twin does two things that are easy to conflate but critically different: it supports day-to-day operational decisions and it enables what-if simulation for planning.
Consider a water utility. On the operational side, a digital twin tracks pressure, flow rate, consumption, and infrastructure status in real time, helping crews respond to leaks, manage outages, and interact with customers. On the simulation side, it answers forward-looking questions: What happens to our water infrastructure if we add a new subdivision? What’s the impact on consumption rate, supply capacity, and revenue?
“To be able to simulate upfront helps the organization be better and more efficient when they plan, design, deploy, operate, and maintain that infrastructure,” Mulberry says. “A digital twin supports that whole end-to-end workflow.”
The same logic applies to transportation, electric utilities, and facilities management. The digital twin isn’t just a record of what exists, it’s a tool for modeling what comes next.
Levels, Not a Light Switch
Organizations often stall because they treat digital twin adoption as binary: either you have one or you don’t. In reality, it’s a maturity model that includes a series of levels, each adding capability and value.
“Not all organizations will get to a level five, and that’s totally fine,” Mulberry says. “The beauty of a digital twin is you don’t have to have everything in place to benefit from the outcomes.”
Levels zero through two are largely about data capture and visualization. Levels three and four are where the connected data ecosystem takes shape. Financial systems, sensor networks, customer data, and maintenance workflows all feed into a single operational picture. At this stage, the digital twin isn’t just showing you your infrastructure. It’s actively informing how you maintain it, fund it, and serve the people who depend on it.
What a Connected Data Ecosystem Actually Means
A connected data ecosystem isn’t a product or a platform. It’s the result of intentional integration over time, the point at which your digital twin starts working as a true operational system.
“Anything we do in all these different infrastructures is to provide a service to our customers,” Mulberry says. “Electricity, water, power, transportation – all of it is to help manage and maintain a very efficient world for all the people who consume these services. A digital twin gives us that ability.”
The organizations that get there are the ones that stay focused on business outcomes from the start. They don’t chase the most sophisticated visualization. They ask: what decisions do we need to make, and what data do we need to make them?
A Lesson from Apollo 13
Perhaps the most compelling proof of concept for digital twins happened before the term existed. When the Apollo 13 spacecraft suffered an explosion en route to the moon, NASA’s ground crew built a physical replica of the damaged spacecraft, fed it real-time data from space, and ran scenarios until they found a solution that could bring the astronauts home safely.
“They reconstructed that spacecraft down to the nuts and bolts, grabbed all the information in real time from out there in space, simulated the outcomes on Earth, and went through different scenarios until they got the right one,” Mulberry says. “Then they pushed that information back out to the real world.”
That is a digital twin. A virtual representation, integrated with live data, used to simulate outcomes and drive real-world decisions. The technology today may be far more sophisticated, but that principle stays the same.
Want to go deeper? NV5 has released a comprehensive e-book on connected data ecosystems and digital twin strategy.
