At the heart of any process is the transformation of past experience into a tool for planning the future. Experience in the modern sense is a structured set of data, the analysis of which allows making reasonable forecasts.
It is historical data that serves as the foundation of forecasting, as it clearly demonstrates the results of the work performed and provides insight into the factors affecting those results.
Let’s take a concrete example from monolithic construction: usually when planning the timing of works, the volume of concrete, the complexity of the structure and weather conditions are taken into account. Suppose that a particular site foreman or the company’s historical data for the last three years (2023-2025) show that pouring a 200 m² monolithic structure in rainy weather took between 4.5 and 6 days (Fig. 1.1-3). These accumulated statistics become the basis for predicting lead times and costing resources when planning similar work in future projects. Based on this historical data, the foreman or estimator can make an informed forecast, based on experience, of the time required to complete future similar work in 2026 under similar conditions.
In this case of time-analytic assessments, the analytical process acts as a mechanism for transforming disparate data into structured experience and then into a precise planning tool. Data and processes are a single ecosystem where one cannot exist without the other.
Count what can be counted, measure what can be measured, and make what cannot be measured measurable (“Monitoring: making use of the tools which are available,” 1980).
– Galileo Galilei
In today’s business landscape, data analytics is becoming a critical component of effective project management, process optimization, and strategic decision making. The construction industry is gradually mastering four key levels of analytics, each of which answers a specific question and provides unique benefits (Fig. 1.1-4):
- Descriptive analytics – answers the question “what happened?” and provides historical data and reports on past events and results: over the past three years (2023-2025), it took 4.5 to 6 days to pour a 200 m² monolithic structure in rainy weather.
- Diagnostic analytics – answers the question “why did this happen?” by identifying the causes of the problems: the analysis shows that the pouring time of the monolithic structure increased due to rainy weather, which slowed down the concrete curing process
- Predictive analytics – future-oriented, predicts possible risks and lead times by answering the question “what will happen?”: based on historical data, it is predicted that pouring a similar 200 m² monolithic structure in rainy weather in 2026 will take approximately 5.5 days, taking into account all known factors and trends.
- Prescriptive analytics – provides automated recommendations and answers the question “what to do?”, allowing companies to choose optimal actions: To optimize work, for example, it is recommended to: use special additives to accelerate concrete curing in high humidity conditions; plan pouring for periods with the lowest probability of precipitation; organize temporary shelters for the structure, which will reduce the work time to 4-4.5 days even in adverse weather conditions.
Full-fledged digital transformation, which implies a transition to system analytics and data-driven management, requires not just outsourcing, but the formation of a competent internal team. The key members of such a team should be product managers, data engineers, analysts and developers, who will work in close collaboration with business units (Fig. 4.3-9). This collaboration is necessary to ask educated analytical questions and effectively parameterize business decision-making tasks. In an information society, data becomes not just an auxiliary tool, but the basis for forecasting and optimization.
In construction, digital transformation is fundamentally changing the way facilities are designed, managed and operated. This process is referred to as the digitalization of information – when all aspects of the construction process are digitized into a form suitable for analysis.
