Structured asset data – the quiet catalyst reshaping maintenance performance – Plant & Works Engineering

Matt Kent, Director at engineering at EMCOR UK, explores how structured asset data strengthens each stage of the asset management pyramid, and why creating a dependable dataset is becoming the most practical route to better reliability and reduced cost
Manufacturing has never been a gentle environment for assets. Heat, vibration, dust, load cycles and production pressures all conspire to shorten lifespans and complicate planning. Yet most engineering and maintenance teams are still working without the depth of information they need.
One recent report found that almost one third of firms in Europe still rely on spreadsheets, and more than 40% depend on paper-based checklists. It places teams on the back foot, reacting to issues rather than influencing performance.
Digitalisation is often discussed in grand terms, but the truth is more grounded. Structured asset data does not need to be complicated to be transformative. Once you collect reliable information on the condition, criticality and lifecycle of equipment, you unlock a level of operational clarity that no reactive or time-based maintenance regime can compete with.
For organisations under pressure to improve uptime, reduce risk and control cost, this shift is essential. The pyramid model of asset management provides a helpful way to think about it. It starts with strategy, builds through maintenance methodology, and peaks with continuous management. Each layer requires good data to stand up, yet most organisations try to build the upper layers on foundations that are not stable.
Structured asset data strengthens each stage of this pyramid, and creating a dependable dataset is becoming the most practical route to better reliability and reduced cost.
Building the strategic base
Every engineering leader wants the same thing: fewer failures, fewer surprises, more predictable performance and a better handle on cost. The challenge is knowing which actions will make the greatest impact. Without structured data, strategy falls back on instinct, familiarity or historic expectations. That makes it difficult to balance cost, risk and operational performance in a consistent way.
The base of the asset management pyramid asks a simple question: what are we trying to achieve and what do the assets need to deliver? A production line running three shifts a day has a very different risk appetite to a lightly used administrative building. A heat-intensive process plant will wear components at a pace that bears little resemblance to manufacturer guidance.
During the strategic phase, the aim is to understand the real operating context. This includes failure modes, environmental conditions, business priorities, compliance requirements and resource constraints. Doing this well requires a reliable dataset. It gives you clarity on which assets drive your cost base, which create the most disruption when they fail, and which represent realistic opportunities for improvement.
By bringing performance, cost and risk into one view, leaders can set maintenance strategies that support wider business aims, whether that is carbon reduction, productivity, safety, compliance or a mix of all four. Structured data helps to test assumptions and exposes areas where long-held practices may no longer serve the organisation.
Turning strategy into maintenance plans
Once the strategy is set, the next layer of the pyramid focuses on implementation. This is where structured asset data becomes a practical benefit to engineers on the ground. Over the past year, I’ve worked with a national utilities company to help them move from a time-based approach towards predictive, risk-led maintenance. The team surveyed 28,500 assets, generating more
than 280,000 data points. Each asset was assessed for condition, criticality, lifecycle stage and performance. The data was then verified, cleaned and analysed to build a complete picture of the
estate.
With a unified dataset, our customer could see where maintenance effort was misaligned with risk. Some assets received scheduled checks far more often than their operational importance justified. Others were critical to resilience but lacked clear lifecycle information or had outdated profiles that underestimated their likelihood of failure.
By adopting a risk-based methodology, the utilities company reduced the time required for planned maintenance by 35%. That time was redirected to tasks that actually influenced performance, such as root cause investigations, targeted inspections and improvement works.
This proves an important point. Digitalisation is not about collecting data for the sake of it. It is about creating a dataset that gives you the confidence to prioritise. When engineers trust the information, they can make better decisions with less effort.
Climbing towards the management peak
The top of the pyramid represents the point where strategy, maintenance and management meet. Here, structured asset data enables more advanced approaches such as condition-based monitoring, predictive analytics and targeted renewal planning.
These techniques are not new, but they only succeed if the underlying data is accurate. Before you can apply sensors, algorithms or modelling tools, you need to know what the asset is, how it behaves and where it sits in the broader system. Otherwise, teams waste time investigating false alarms, adjusting thresholds or chasing data quality issues.
At the management peak, structured data allows organisations to:
Identify assets approaching end-of-life and compare replacement against continued maintenance
Recognise repeat failure patterns that point towards design or environmental problems
Build business cases for capital investment using real operational evidence
Understand where sensor deployment is worthwhile and where it is unnecessary
Shift from fixed schedules to intelligent intervals that reflect actual usage and condition
None of these activities require complex AI platforms or wholesale digital transformation. They simply need clean, consistent data and a methodical approach to interpreting it.
For example, if a critical pump shows a predictable degradation curve, and you have the data to confirm its age, failure history and downtime impact, you can judge whether a sensor will deliver value or whether a planned replacement is more economical. If a low criticality asset has no meaningful consequence when it fails, you may consciously let it run to failure as part of a business-focused maintenance strategy.
Structured data gives you the confidence to apply these decisions consistently rather than relying on individual judgement or incomplete information.
Grounding digitalisation in real engineering
There is a temptation to think of maintenance digitalisation as a technological exercise. In reality, it is closer to an engineering one. Structured asset data helps teams return to fundamentals: understand the asset, measure its condition, observe its behaviour, and plan accordingly.
For organisations moving away from spreadsheets and paper-based systems, the journey does not need to be dramatic. It starts with building an accurate asset register. It grows through verifying condition and criticality. It matures when teams use that data to shape strategy and maintenance plans.
The utilities project demonstrates that real benefits follow quickly once the dataset reaches a consistent standard. Major engineering improvements happen when teams can see clearly, not when they collect endless volumes of data they cannot use.
A future built on clarity rather than volume
As manufacturing becomes more automated and more interconnected, the value of structured asset data will only rise. It supports better decision making at every level, from capital planning to daily operations. It reduces waste. It strengthens resilience. It frees engineers to spend time where their judgement matters most.
The pyramid model reminds us that asset management does not begin with advanced analytics. It begins with the basics of knowing what you have, understanding what matters, and acting on evidence rather than habit.
Structured data is the quiet catalyst behind this shift. For many organisations, it is the most achievable and most productive step they can take towards a smarter, more reliable maintenance strategy.
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