For years, the value of a construction project was assessed primarily up until the facility was put into service. That was when the construction phase ended and the operational phase began—a phase often treated as a separate area of responsibility. Meanwhile, an increasing number of investors, property managers, and infrastructure operators are realizing that the true advantage may only become apparent after construction is complete—when the facility begins to generate data about its own operation.

Predictive infrastructure maintenance is changing the logic of asset management. Instead of reacting to failures, it is possible to detect early signs of deteriorating performance, plan maintenance interventions, and reduce the costs of unplanned downtime. For the construction market, this means shifting the focus from one-time projects to long-term operational value. This is precisely where the construction industry begins to profit from data—not as a trendy technological add-on, but as a tool that supports business decisions.

Why does data become a new source of value after a project is completed?

Once construction is complete, the infrastructure does not stop functioning—on the contrary, it is only then that it begins to reveal actual patterns of use, loads, material wear and tear, and susceptibility to failures. Operational data from sensors, BMS systems, technical monitoring systems, and maintenance management platforms allow for real-time observation of the facility and analysis of its behavior over the long term.

The value of this data increases particularly in situations where downtime costs are high or where a failure could lead to operational, financial, or reputational consequences. This applies not only to large technical infrastructure but also to warehouses, commercial facilities, industrial plants, hospitals, and data centers. In each of these sectors, reactive maintenance is becoming less and less effective.

Data begins to yield results when it is linked to a decision. Simply collecting information is not enough. It is crucial to establish a process in which a signal from the system leads to action by the service team, an adjustment to the maintenance schedule, a change in the system’s operating parameters, or a revision of the maintenance policy.

What is predictive infrastructure maintenance?

Predictive maintenance involves predicting when a piece of infrastructure may fail or reach a state of increased risk of failure. This is achieved by using historical data, current operational readings, and analytical models that identify deviations from the norm.

Unlike the reactive approach, which waits for a failure to occur, and the preventive approach, which performs inspections according to a fixed schedule —the predictive approach initiates intervention only when analysis indicates that a given component is actually approaching an undesirable technical condition.

This approach is particularly important in an environment where infrastructure operates under variable conditions and usage patterns differ from design assumptions. This makes it possible to better align the scope of maintenance with the actual wear and tear on assets.

What data is needed for a predictive model to be meaningful?

Data collected regularly and in a comparable manner is the most valuable. It’s not just about the quantity, but also about the quality, consistency, and the ability to place the data in a technical context. A predictive model should understand what the readings refer to, which installation or device they pertain to, and under what conditions they were collected.

The most commonly used are:

• data from sensors measuring temperature, vibration, humidity, pressure, or energy consumption,
• history of malfunctions and service requests,
• inspection schedules and technical inspection results,
• information about the actual load on the structure and how it is used,
• design and construction data, including BIM documentation, if available.

If data is scattered across different systems, the first step should not be to implement advanced analytics, but rather to organize the data sources and define a minimum standard for operational data.

When does an investment in data start to pay off?

Most often, this occurs when it is possible to reduce costly outages, shorten service response times, or extend the lifespan of key infrastructure components. The return does not always take the form of direct new revenue. Very often, it means avoided costs, greater predictability in the maintenance budget, and higher technical availability of the facility.

From the perspective of an asset portfolio owner, better planning of replacement expenditures also becomes essential. The data makes it possible to identify in advance which systems actually need to be replaced and which can continue operating longer without increasing risk. This is particularly important when managing multiple facilities of varying ages and technical standards.

In practice, the time it takes to see a return on investment depends on several factors: the criticality of the infrastructure, the quality of the input data, the scale of the assets, organizational maturity, and readiness to change maintenance processes. For this reason, it is advisable to begin implementation in areas where the impact on operating results will be most quickly apparent.

Should every facility implement predictive maintenance?

Not every facility requires advanced analytical models right away. In some cases, well-organized preventive maintenance—based on thorough inspections and simple monitoring—is sufficient. Predictive maintenance makes the most sense where assets are high-value, there is high sensitivity to downtime, or there is extensive technical infrastructure.

A cost-benefit analysis should take into account:

• the cost of a potential failure,
• frequency and recurrence of defects,
• availability of historical data,
• the ability to perform technical measurements of the system,
• an organization's ability to apply the results of an analysis in practice.

That is precisely why implementation decisions should be preceded by a business assessment, rather than a purely technological one. Increasingly, this approach is supported by consulting services that combine market analysis, operational analysis, and potential return scenarios.

What role do the design and construction phases play?

The biggest mistake is assuming that operational data can only be put to good use after the facility is completed. In reality, the future quality of predictive maintenance is often determined as early as the design phase. That is when automation systems, the scope of metering, data integration standards, and the structure of technical documentation are selected.

If a facility is designed without considering future monitoring, the costs of retrofitting and integration after completion may increase significantly. Conversely, a well-prepared data layer increases the likelihood of a smooth transition from construction to operation and minimizes the problem of knowledge loss between the contractor, the developer, and the operator.

In this context, our experts note that periodic reports published for the construction and infrastructure sectors increasingly cover not only investment activity but also changes in operating models after facilities are put into service.

What are the most common obstacles to implementation on the organization’s side?

Technology itself is rarely the biggest barrier. Much more often, the problem lies in the lack of a process owner, the fragmentation of responsibility, and a misalignment of goals between the technical, financial, and management departments. If maintenance, IT, and asset management operate in separate silos, data does not translate into decisions.

Another common challenge is a lack of confidence in the quality of the input data. Predictive models cannot correct errors in asset records or replace up-to-date documentation. Without getting the basics in order, an organization will have difficulty obtaining reliable results.

In Polish analyses, studies conducted by, among others, independent research centers and analytical firms can be helpful. Such sources help place investment and operational decisions in a broader market context and better assess which solutions have long-term potential and which are merely a response to a passing trend.

How can we measure the effectiveness of predictive maintenance?

The effectiveness of implementation should be assessed not by the number of dashboards, but by changes in operational and financial metrics. Organizations often start by monitoring alerts and readings, but at a later stage, they need to move on to metrics related to infrastructure availability and maintenance costs.

The most useful metrics are those that show whether the model actually improves maintenance decisions. These may include the ratio of unplanned failures to planned interventions, reduced diagnosis time, improved equipment availability, or greater predictability of replacement costs. Depending on the segment, it’s also worth monitoring the impact on energy consumption and the comfort of facility users.

When assessing market potential or choosing a technological direction, it is important to rely on reliable, up-to-date data—especially when the decision involves multiple assets, different locations, or a long investment horizon. It is precisely in such situations that PMR Market Experts’ industry reports are most valuable.

Does predictive maintenance affect the valuation and competitiveness of assets?

This is increasingly the case. Infrastructure that is better monitored and managed becomes more predictable from an operational standpoint. From an investor’s perspective, this means lower risk of disruptions, more reliable cost planning, and greater transparency regarding future expenditures. These factors can enhance an asset’s appeal when refinancing, selling, or seeking partners.

Regulatory and environmental considerations are also important. Better data on a facility’s operation makes it easier to document energy efficiency, the quality of maintenance, and compliance with technical requirements. In some segments, this will become an increasingly important factor in competitive advantage.

When we factor in scenarios for changes in costs, demand, and investment activity, market forecasts take on particular significance, as they help ground decisions regarding maintenance technology in real economic and sector-specific conditions.

How can you get started without overpaying or getting stuck in the pilot phase?

A phased approach yields the best results. First, it’s a good idea to select a limited set of assets for which the cost of failure is high and for which data is available to quickly build a test model. Such a pilot project should address a specific business question, such as whether it’s possible to detect the degradation of a specific type of installation in advance.

Next, it is necessary to determine what decisions will be made based on the results. Without this answer, the project will remain a technological initiative with no impact on operations. That is why implementation should include not only the analytical layer, but also procedures, responsibilities, and rules for responding to alerts.

A practical plan for getting started might include:

• an audit of the quality and availability of operational data,
• selection of critical assets for the pilot program,
• defining metrics for measuring the success of the implementation,
• integration of technical and service data,
• preparing the decision-making process on the maintenance side,
• scaling the solution only after the results have been confirmed.

Summary and Key Findings

Predictive infrastructure maintenance is not merely an IT project or an add-on to building automation. It is an asset management model that enables the value of data to be transferred from the observation level to the decision-making level. The construction industry begins to monetize data after a facility is completed when operational information leads to a tangible reduction in risk, costs, and inefficiencies.

The greatest potential lies where:

• The infrastructure is of great operational importance,
• Breakdowns result in costly consequences,
• The data is collected in an organized manner,
• the organization is able to translate analysis into action,
• Maintenance decisions are linked to business objectives.

How can PMR Market Experts support your efforts?

Where exactly is the break-even point, and which infrastructure segments offer the greatest implementation potential in your case? Ask us this question—our experts’ regular market analyses and industry knowledge will help you make more informed decisions regarding facility maintenance, investment, and development.

Feel free to contact us!

Hume’s Institute stands for knowledge. PMR Market Experts stands for experience. When we join forces, you gain a competitive edge. See how we can support your business.

Hume’s Institute stands for knowledge. PMR Market Experts stands for experience. When we join forces, you gain a competitive edge. See how we can support your business.

Dariusz Śmiejkowski

Chief Executive Officer