Two questions to answer before you build a Digital Twin

Digital Twins have become one of the most talked-about concepts in the built environment. It’s easy to understand why: from design optimization to predictive maintenance and smarter operations, the promise is certainly compelling.  

But in practice, many ‘Digital Twin’ efforts struggle to move beyond pilots. Or in other cases, they exist, but they aren’t being utilized in a way that justifies the hype.  

With more powerful technology available today than ever before, it begs the question: What is holding us back from creating Digital Twins that are actually useful? In 2026, we’re still seeing teams deal with project information spread across systems and tools. This information is seldom connected, and doing so isn’t always straight-forward. So having the “right tools” doesn’t automatically help generate a usable twin. 

What we’ve seen is one clear pattern, that applies to both BIM in general and Digital Twin initiatives: Failure isn’t due to a lack of software or human effort; rather, it happens when your foundations are shaky. It’s whether teams can produce data that’s consistently usable and then operationalize it in decisions. 

Before investing further in your Digital Twin ambitions, there are two simple questions every organization should be able to answer: 

1. Can you trust your model data quality consistently, at scale? 

2. Can that trusted data reach the people and systems that need it? 

Answer these questions, and you will be able to determine whether your Digital Twin will be an initiative that delivers value, or one that slows you down.  

First things first: Why is everyone talking about Digital Twins? 

Before answering the questions that will help you assess your Digital Twin readiness, it’s worth pausing to answer a more fundamental one:  

Why have Digital Twins captured so much attention in the first place?  

A true Digital Twin is a sophisticated digital representation of its real-world counterpart. Mature Digital Twins are even capable of supporting real-time and predictive insight. When done correctly, it becomes a powerful way to manage complex sites, interconnected buildings, and ongoing projects that require continuous oversight rather than one-off delivery.  

Increasingly, Digital Twin requirements are appearing in the scope of major projects, particularly at national or portfolio level. Many see this as the next logical step in the evolution of BIM: moving from information delivery to information use. 

When implemented correctly, a Digital Twin can support: 

• Better decision-making by giving teams the confidence that the information they’re using truly reflects reality 

• Reduced operational risk with earlier insight into issues, changes, and dependencies 

• Greater efficiency across the lifecycle by avoiding repeated data creation, re-entry, and manual reconciliation 

• Continuity beyond handover so that information remains usable long after the project team has moved on 

• A shift from reactive to proactive management, particularly in operations and maintenance 

For example: faster location of critical assets during incidents, or maintenance prioritization based on condition signals, rather than spreadsheets and site walks. 

In short, the value from a Digital Twin comes from its ability to turn trusted, connected information into actionable insight, day after day. 

Question 1: Can you trust your data consistently, at scale? 

Simply put, a Digital Twin is only as reliable as the information it’s built on. 

Many organizations produce high-quality models for individual projects. But consistency is where things break down. Ask yourself if any of these scenarios sound familiar:  

• Standards are defined, but not enforced 

• Validation happens late, or manually (or both) 

• Quality varies per project, team, or discipline 

• Asset data looks complete, until it’s actually needed for decision-making  

All of the above creates uncertainty. Teams spend time correcting information instead of using it. Confidence erodes. And Digital Twin use cases stall because no one is quite sure what can be relied on. 

This is where model and data quality matters. Not as a one-off check, but as a repeatable, built-in capability that ensures information is correct, complete, consistent, and predictable over time. 

Without this level of trust, Digital Twins become fragile. They might look impressive, but they won’t support confident decision-making.  

Question 2: Can that trusted data reach the people and systems that need it? 

Even reliable data has limited value if it remains trapped in silos. 

Many organizations will recognise this pattern: 

• Design data lives in one place; construction teams work somewhere else 

• Handover involves conversion and re-entry 

• Operations start from scratch 

• Decisions lose context because history is fragmented 

In these situations, the problem isn’t quality; it’s continuity. 

A future-ready Digital Twin depends on consistent data flow: connected workflows that allow information to move across teams, phases, and systems without losing meaning. This is where your Common Data Environment (CDE) factors into your foundation. To enable this consistent flow of data, it needs to act as more than just a document management system. It should function as a shared source of truth that connects people, processes, and data across the lifecycle. 

When predictable, consistent data flow is missing, Digital Twins remain isolated snapshots instead of living systems. 

Digital Twin maturity is a process 

True Digital Twin value can be realized when data trust and data flow mature together.  

Organizations typically fall into one of four patterns: 

• Some are still building their foundation, where neither trust nor flow is stable yet. 

• Others produce reliable information but remain siloed and unable to carry that reliability across phases. 

• Some collaborate actively, and are connected but inconsistent, leading to quality issues that scale with speed. 

• And a smaller group have reached Digital Twin readiness, where trusted data and connected workflows unlock insight, automation, and portfolio-level value. 

None of these stages are failures. They are simply different points on a maturity journey.  

The risk lies in skipping steps: investing in Digital Twin ambitions before the underlying issues have been dealt with.  

Before embarking on this journey, the most successful organizations start by asking: 

• Can we trust our information? 

• Can it move where it’s needed, when it’s needed? 

When those questions are answered, Digital Twin initiatives accelerate naturally because the foundation is already there. 

A practical starting point 

Understanding your Digital Twin maturity shouldn’t be guesswork. 

To help organizations assess where they stand today, understand what that means, and identify the most relevant next steps, we’ve created a Digital Twin Maturity Test.  

The Digital Twin Maturity Test will help you assess your Digital Twin foundation, and outline a simple approach for a 30-60-90 day plan, depending on where you are in your journey.