What is the role of big data in construction?

This is a part of the Data feature

By Casimir Macgregor - 1 July 2025, Build 206

This article explores the challenges and opportunities of leveraging big data to understand the sector’s
capacity and capability.

The construction sector plays a vital role in the economy, but its ability to plan effectively is often constrained by fragmented, inconsistent data. Big data has the potential to improve construction planning, risk management and operational efficiency. Access to accurate and timely information – whether from historical or upcoming projects – is essential for construction professionals and project owners alike.

Much of the big data used in construction is high level – it identifies and classifies projects, offering a broad overview. Yet construction is a complex system with numerous interdependencies, which makes drawing meaningful insights challenging.

Big data refers to vast and continually growing digital datasets and the analytical tools used to interpret them. Big data has been defined by three main attributes:

Volume – massive amounts of data (terabytes or petabytes).
Variety – diverse formats such as text, numerical, sensor data, audio and video.
Velocity – real-time, constantly streaming data.

Some researchers add value (usefulness) and veracity (trustworthiness) as additional dimensions. In construction, data is typically large, diverse and ever changing. It can inform project planning, track company financials, monitor progress and optimise operations.

Big data’s role in construction has grown significantly. It offers new possibilities, particularly in forward planning for workforce capability and sector capacity. However, big data can only offer useful insight if the subject is well understood.

In August 2023, a workshop was held during CanConstructNZ, an MBIE Endeavour-funded project led by Massey University aimed at creating capacity and capability for the New Zealand construction sector. This event brought together key government and construction stakeholders to identify major data challenges and collaboratively design solutions for improving access to pipeline and sector data. Participants highlighted three key areas of interest:

Construction pipeline – understanding current and future projects, especially overlaps or niche typologies (for example, community housing or high-skill projects).
Sector performance – monitoring how the industry responds to demand, including internal workforce migration and quality metrics.
Construction economics – analysing costs, including price indices, land costs and benchmarking data.

Together, these themes aim to clarify what the sector is doing and how insight from data such as aligning workforce capability with pipeline demand can improve delivery.

A consensus emerged in the workshop – construction companies across Aotearoa New Zealand face similar data-related challenges:

Data quality and consistency – many sources are disjointed, outdated or insufficiently detailed. What level of detail is needed? What qualifies as data that is good enough?
Data access – high-quality data often resides in the private sector and may be commercially sensitive.
Data silos – there is no central repository for construction data.

Another issue was differing organisational and company priorities. One participant noted, ‘The needs of organisations are different, so they only capture what they need. If we use [the data] for another purpose, it might not fit.’ This raises the issue of fit for purpose – but for whom? The end user? The analyst?

Conceptualising big data insights for complex construction systems

To make sense of big data in such a complex environment, a structured framework is needed:

Define system boundaries: Identify key subsystems (project types, supply chains, labour, safety, scheduling) and how they interact. Mapping these relationships helps highlight dependencies and feedback loops that affect capacity and performance.
Identify data inputs, sources and quality: Start with a minimum dataset to track projects. Additional data may be needed to capture interdependencies – historical performance, productivity metrics, environmental conditions and more. However, this data must be cleaned to remove errors and inconsistencies, which are common in construction environments. A data quality matrix can help assess external sources for validity.
Choose analytical methods based on need: Big data insights depend on the right analysis. There are three key approaches:
- Descriptive analytics – summarise what has happened such as trends in costs, delays and incidents.
- Predictive analytics – use machine learning to forecast outcomes such as completion times or supply chain issues.
- Prescriptive analytics – leverage AI or simulations to recommend actions such as optimal scheduling and cost reductions.
Apply systems dynamics modelling: It is crucial to understanding cause-and-effect relationships. For example, how do supply chain disruptions affect construction timelines? Modelling these dynamics and including feedback loops can simulate how one change affects the broader system. Scenario testing helps prepare for different conditions, improving scheduling and resource optimisation.
Communicate insights effectively: Turning technical insights into actionable strategies is essential. Risk assessments, performance tracking (planned vs actual) and project interdependence should all be communicated clearly. Consider the audience – project managers may prefer dashboards, while executives might need high-level summaries. Tailor formats to ensure insights drive decisions.
Embed feedback in decision making: Insights must be fed back into future planning and operations. Embedding data use into the wider organisational culture such as consistent project management practice can improve how analytics are used across the business. A culture that embraces data can help shift construction from reactive to proactive decision making guided by real-time, predictive insights.

Using big data has the potential to transform the construction sector. However, this requires a cultural shift towards data-driven decision making and collaboration across the industry. If implemented effectively, big data insights can help the construction sector move from reactive to proactive management, driving long-term improvements in efficiency, productivity and resilience.

While big data alone will not solve all the challenges facing the construction sector, a well-structured, systems-based approach to data collection and analysis can provide the insights needed to support informed decision making.