Data is one of the most powerful assets in the modern engineer’s toolkit. Digital engineering helps us use it to design and deliver more resilient infrastructure, more efficiently than ever.
Once regarded as a mere design tool, digital engineering is fundamentally changing how projects are brought to life, saving time and money while producing better spaces through data-driven decision-making. Yet many infrastructure providers and utility operators are yet to realise its full potential.
Digital engineering is no longer about building better-looking models. It’s about the data that sits behind them, allowing teams to make better decisions throughout the project lifecycle. In a time when organisations are under pressure to do more with less—and faster than ever—digital engineering could be the difference between a project being delivered as planned, and one that’s held up by unforeseen obstacles due to costly, time-consuming changes.
That might be working with clinicians to design hospital spaces that actually work, or responding quickly when a centuries-old well is uncovered during a museum development. Digital engineering gives design and construction teams the ability to test, plan, and make changes on the fly—without dramatically impacting timelines or budgets.
By bringing project information together from the outset, digital engineering enables better, data-driven decisions throughout the project lifecycle.
" }The evolution of digital engineering: Why data is engineering’s most powerful asset
Five years ago, digital engineering was often seen as a bolt-on—something brought in halfway through a project because “it’d be cool to have a model”. And yes, a beautiful 3D model is cool, but it was still treated as a nice-to-have rather than something that shaped delivery.
Now, the focus has shifted beyond the model to the data behind it. Across the industry, expectations are shifting. Clients are no longer asking for models—they’re asking for outcomes. They want better asset information, smoother handover, and fewer surprises during delivery. At the same time, projects are becoming more complex with tighter programs, more interested parties, and higher performance requirements. Investing that time in bringing the right data together from the outset can save considerable time and money later.
Information generated through Building Information Modelling (BIM) and other digital engineering processes is all being captured in Common Data Environments (CDE). This shared source of truth helps teams make better decisions, earlier and with more confidence. A well-managed CDE provides everyone with an accurate, current view of the latest information, removing the risk of working from out-of-date or disconnected files, lengthy email chains, and making decisions based on incorrect information.
BIM models and the data they contain now influence how projects are set up from day one. Decisions around coordination, asset information, how teams will work together, and what the client needs at handover can be made much earlier. That’s where the real value of digital engineering is: bringing accurate, up-to-date data together in one place, where it’s accessible to all who need it, to make better decisions and achieve better outcomes from the get-go. BIM remains an important part of that process, but the greatest value comes from the reliable project and asset data the models contain.
" }Digital engineering is no longer about building better-looking models. It’s about the data that sits behind them.
On the ground, we’re seeing digital engineering enable tighter integration between design, construction and operations. Data flowing seamlessly across the entire lifecycle. Capturing the right data earlier can, in some instances, change the course of a project before it even gets off the ground.
On a recent project delivering critical infrastructure in one of the most remote and extreme environments on Earth, we were asked to create a spreadsheet listing every single asset in the building, from light bulbs to generators. But when we dug into what the client needed, we realised it wasn’t a spreadsheet at all. It was a better way to organise the building’s data.
We discovered that what they really needed was an update to their facilities management system, so that teams could quickly find the information they needed when something went wrong. The problem with working in extremely remote places is you can’t just pop down the road to Bunnings for a sausage sizzle and a spare part. Getting what you need when you are miles from nowhere takes time—you don’t want to waste it wading through spreadsheets.
Building a CDE at the outset of the project meant time and money weren’t wasted on a solution that wouldn’t have solved the client’s problem. It’s because of discoveries like these that we’re seeing—and advocating—for digital engineering becoming the backbone of project delivery.
Using BIM in healthcare: Getting design decisions right, early
In mission-critical environments, small design decisions can have outsized consequences. In an airport, they can affect passenger safety and operational resilience. In an industrial facility, they can create maintenance challenges that last for decades. In a hospital, they can influence how quickly a patient reaches critical care, how safely clinicians can work, and how effectively services continue during construction.
When working in a live healthcare environment, such as a major hospital, the goal is always to minimise disruption—to get in and out as soon as we can. But every redesign extends that timeline.
Using BIM as part of the broader digital engineering approach means we can do that. We can test scenarios before construction begins, from whether new equipment will fit in the available space to how people will get out in an emergency. We can even model whether a ward needs to be shut down during construction and understand the knock-on impacts upon surrounding communities before anything happens on site.
Importantly, it allows us to gather feedback from the people who work in a space much earlier. Too often, a space gets designed and installed, and the people who use it turn around and ask, “who designed this?” because it doesn’t work in practice. In a hospital, something as simple as how a room will be cleaned, or whether two people can move safely around a bed gets missed.
With digital engineering, we can avoid that. Using a virtual reality headset, hospital staff can walk through the proposed design. They’ll quickly point out what doesn’t work—things we wouldn’t know otherwise—whether it’s the position of something as simple as a sink, the placement of a gas outlet, or whether there’s enough clearance for multiple staff during procedures.
Without that data, we’re left making assumptions. And in environments where people depend on critical infrastructure to perform from day one, making assumptions is an unnecessary risk when you have the ability to make decisions grounded in data.
" }In mission-critical environments, like Redcliffe Hospital in Brisbane, small design decisions can have outsized consequences, like how quickly a patient gets critical care, how safely clinicians can work, and how effectively services continue during construction.
Data-driven construction: Responding quickly when things don’t go to plan
Digital engineering doesn’t just allow for better planning. The real test is when we’re thrown a curveball in the middle of a project. And on complex infrastructure projects, the question isn’t whether something unexpected will happen—it’s when.
On the Sydney Metro project, for example, construction has unearthed everything from a 230-year-old convict hut in Parramatta to a 180-year-old boat in Barangaroo. In Brisbane, a 200-million-year-old fossilised tree was discovered. In Melbourne, they even uncovered the shell of an old pub.
We’re no strangers to unexpected discoveries either. When we were working on the New Museum for Western Australia during excavation, the team uncovered a centuries-old well beneath the site—and we had to figure out what to do with it, fast.
We went straight into the model to test the options. Could the building sit over it? Should it be removed? Was the well still working? Could it be retained and turned into a feature? Everyone was in favour of keeping the historic well, but that raised more questions. If we exposed it with a glass floor, would condensation become an issue? Would we need to move other elements to make room for it?
Using the model, we could test those scenarios on the spot. We quickly saw that retaining the well would mean reworking parts of the layout, including relocating an entire block within the building. That’s not something you could work through as quickly using 2D drawings.
The end result is now part of the finished museum—you can walk across a glass floor and look down into the historic well below. What started as a problem became a feature of the building, because the team had the tools to respond quickly and make informed decisions.
" }When we discovered a centuries-old well during the excavation for the New Museum for Western Australia, we used 3D models to save the well, turning it into a central feature of the new building.
Rethinking digital engineering: From better models to better decisions
The projects that succeed will be those that treat data as an asset and digital engineering as a strategy across the entire project lifecycle. Those that don’t evolve their approach risk slower decisions, higher costs, siloed workflows, and outcomes that don’t meet long-term operational needs.
To realise the full value of digital engineering, we need to bring the data we have together at the outset of a project in a CDE—and think ahead to the information we’ll need at the end of it. That means bringing digital engineering in early, not midway through delivery. Otherwise, we’re only using a fraction of what the data can do.
We live in the age of data. We have access to troves of it—before, during, and after projects are completed. Not making the most of that data isn’t just a missed opportunity. It’s an operational risk.
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