Do digital twins have what it takes?

Digital twins sound like the future: a full virtual replica of your building, fed by real-time data, designed to optimise performance. But what does that really mean?

It’s not just a 3D map. A true digital twin combines design data, real-time system inputs, and big data modelling. Think: a Google Street View of your building, layered with live equipment data, behavioural trends, and predictive analytics. In theory, that’s total visibility. In practice, it’s a heavy lift.

The problem: big promises, big investment

To build one, you need it all—plant data, sensors, weather feeds, IT systems, occupant behaviour. Then you have to maintain it. Without constant updates, the model loses accuracy. That means high upfront costs and ongoing resourcing just to keep it relevant.

And while the investment goes into data infrastructure, the basics—like fixing poor control settings or inefficient run-times—can get delayed. The insight may be sophisticated, but if it takes 18 months to surface, it’s not helping you now.

The alternative: not big data—right data

Most portfolios already hold the data that matters. Utility records. Historic usage. Standard control logic. When analysed properly, these reveal the patterns that are causing waste—and the actions that will cut it.

With the right benchmarking and performance insight, you can start seeing results in under six months. No waiting for a full twin. No major system installs. Just clear, focused decisions that drive measurable improvement.

Better decisions start sooner

Digital twins may play a role later. But you don’t need one to get started. Especially if what you need is clarity, not complexity. The right data, in the right hands, beats the perfect model that’s still in development.

It’s not about big data. It’s about smart priorities.