Infrastructure owners spend billions on physical assets. If you manage these assets—pipelines, hospitals, water treatment plants, roads, power networks—your biggest risk probably isn’t physical. It’s data fragmentation.
Accurate data can help support these investments. Having up-to-date, trusted data is essential. It helps with decisions, designs, maintenance, operations, compliance, and overall performance.
When we see infrastructure projects fail, most often it’s not because of design. It’s because data is treated as an afterthought, instead of as critical infrastructure.
When data is poorly managed, teams may spend more time searching for answers, duplicating work, and reacting to issues after they occur. Over time, this can lead to rework, delayed decisions, and higher operational costs.
If data isn’t managed like an asset, infrastructure performance will suffer. Full stop.
Better access to reliable information can help teams make more informed and timely decisions across the life of an asset. And it starts with structured data environments accessible by multiple systems.
" }Data must be managed like an asset, not treated as an afterthought. Otherwise infrastructure performance will suffer.
We need structured data environments. Why is that? Because disconnected information is too risky. Here, we will discuss the need to elevate the role that data governance plays in better decision-making by looking at how structured data environments and systems work in practice.
Building a connected data ecosystem
A connected data ecosystem works a bit like a solar system. A system of systems, one might say.
At the centre sits a trusted information hub. This could be a SharePoint site or website that provides links to other data environments (storing reports, registers, meeting records, approvals, and project correspondence), displays key dashboards, etc. Think of this as the sun.
Around it, sit specialist systems in orbit. Engineering teams may use BIM (building information modelling) environments for models and survey data. Planners and environmental specialists may use GIS (geographic information system) for mapping and field inspections. Asset teams may work within asset and maintenance management systems. In the field, crews capture inspection records through mobile apps.
Different teams can and should use different fit-for-purpose tools. Treating data as critical infrastructure doesn’t mean replacing every system and finding one system to rule them all. The most important thing is that the information is connected and is trusted throughout the full life cycle of the asset.
And then there’s gravity.
In connected data ecosystems, people are the gravity. They are what keep the systems current and relevant. If your team stops following agreed processes, or the system is used differently by different people, data will quickly fragment again. The trust in that information is compromised.
" }In connected data ecosystems, people are what keep systems current and relevant. Following agreed processes and everyone using the data the same way means the trust in that data isn’t compromised.
The risks of disconnected data—and the value of getting it right
Treating data as critical infrastructure can help teams make timely and informed decisions, identify risks earlier, and improve service delivery. But it only works if people can find and use that data.
Too often, information sits across disparate systems. Teams may not know the data exists, may not see it at the right time, or may be unsure which version to trust. In some cases, people download information and work locally simply because they don’t understand the systems in place or how they are meant to be used. Over time, this leads to duplication, fragmented knowledge, and decisions being made without full context.
Worse, it can lead to actual infrastructure failure and create public safety risks. On one job, a council had inspection data showing pipe deterioration. But they failed to share it with maintenance teams. Without that data, the maintenance teams didn’t know there was an issue, and this led to a sinkhole forming above the failed pipe in the middle of a busy road.
The issue wasn’t a lack of data—it was that the maintenance teams didn’t have access to it. They were not aware of the risks. If they had the access and information, they could have planned or predicted the repairs required to the pipe before the sinkhole occurred, reducing the overall cost of the repairs, service downtime, and the risk to the public and environment.
" }These are the types of risks and missed opportunities that our clients, like Watercare in Auckland, aim to avoid. As part of our strategic planning partnership with Watercare, we’re helping them develop the Asset Risk Model. It’s a data-driven framework used to evaluate the likelihood and consequence of infrastructure failures across its water and wastewater networks. The approach combines multiple data sources to help predict failures and understand the resulting consequences. This helps teams understand the risk profile of their assets and to prioritise what to fix and when; it also helps them know where to focus maintenance budgets.
When budgets are tight, visibility matters. Trusted and structured information is key. It helps organisations make more informed decisions about where to focus time, maintenance, and investment. It’s effectively a systematic, fact-based, repeatable process to understand where they will get the best value for their money.
These same risks can also emerge during major project phases. Such an example is definitive feasibility studies, where large volumes of technical data must be brought together to support critical investment decisions.
Our Australian team is working on a large mining project in the Philippines where multiple disciplines are working across different platforms, each fit for purpose but not always visible to others. Without a shared understanding of where information sits or which source to trust, there is a potential of duplication, delays, and choices being made without full context.
To address this, our project team established a connected data ecosystem following a ‘solar system’ approach. A central hub acted as a clear landing point, showing which systems were in use, who owned them, and where trusted information could be found. Around it, specialist tools remained in place—but they were connected through a shared understanding of how data flowed between them.
This improved visibility and helped teams find what they needed more easily. It also reduced duplication of effort and supported more confident decision-making during the study. Altogether, it highlights the practical value of getting connected data environments right.
" }We’re helping clients like Watercare in Auckland, New Zealand, develop asset risk models to help prevent infrastructure failure.
How to approach data governance in infrastructure projects
Treating data as critical infrastructure doesn’t always require a major change or new tools. Most organisations already have the data and tools they need. The challenge is making those tools work together consistently. It needs to work in a way that gives project and asset interested parties confidence that the information within the tools is trustworthy and of the appropriate quality.
Just like physical infrastructure, data also needs governance and maintenance.
In practice, that often means:
- Deploying data structures such as standard data collection forms, naming conventions, and folder structures.
- Defining a clear source of truth for diverse types of data.
- Making sure teams understand where to find the information they need. And making it easy to access.
- Reducing duplicate and inconsistent records across systems.
- Setting clear roles and responsibilities for creating and maintaining data over time.
- Alignment with industry standards to make the data auditable for business continuity and reputation.
These may sound simple, but we’ve seen that most are often overlooked.
In New Zealand, the 3 Waters Asset Data Standard is one example of effective data governance. This standard was designed to create greater consistency in how water infrastructure information is procured, managed, and maintained throughout the full life cycle of the asset.
Better visibility of water asset information is becoming more important for planning, design, maintenance, and compliance. Our colleagues discussed this previously around 3 Waters reform conversations. Frameworks like these can improve collaboration and reduce uncertainty across teams.
Stronger data governance is important as technology changes. It provides a better foundation for things like artificial intelligence (AI) and predictive analytics.
Artificial intelligence and the importance of structured data
More infrastructure organisations are starting to explore AI tools. These tools may help predict pipe failures, spot maintenance trends, understand risk, or help teams decide what to repair, replace, or upgrade first.
But AI tools are only as useful as the data behind them.
If inspection records are incomplete, teams use different data structures, or information is difficult to access, it becomes harder for AI systems to interpret the data correctly. Here’s a simple example. A maintenance team calls a pipe ‘XYZ’, but the planning team calls it ‘ABC’. This makes it harder for the teams to find the same pipe. In the future, everyone—including AI—must be on the same page.
AI tools will become more widely used and deployed. Now is the time to improve how data is managed before AI advances more. Treating data as critical infrastructure can help support more consistent and reliable outputs and decisions.
" }Everyone—including AI—must be on the same page for data infrastructure to work.
Building better data environments over time
Treating data as critical infrastructure requires both cultural and technical change. For decades, infrastructure owners have focused on maintaining physical assets over the long term. Increasingly, the same thinking now needs to apply to the data behind them.
A connected data ecosystem is not about creating perfect systems or replacing every platform already in use. It’s about making it easier to connect people with reliable information. And then to use it consistently across the asset life cycle.
Our infrastructure networks are only becoming more complex. That means the connections between systems, people, and information are more important.
The organisations making progress are often not the ones with the most advanced systems. They are the ones that create clear, easy-to-use processes, stronger visibility, and better connections between people, systems, and data.
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