Leveraging data analytics in construction for smarter projects

Talk to five general contractors, and four will bring up lagging productivity as the most significant industry challenge today. Globally, engineers and constructors are struggling to complete projects even in their current pipeline, and a myopic approach to data is one of the reasons why. 

While data analytics in construction cannot single-handedly prevent schedule slippage and budget blowouts, it can at least shine a light on early warning signs, bringing prospective indicators into focus before issues escalate.

What is сonstruction analytics?

Construction analytics builds upon historical and real time data from BIM, ERP, sensors, wearables, drones, and project management tools to drive smarter decisions across construction projects. When it comes to advanced construction data analytics, it doesn’t just track and store data in data lakes and warehouses. It interprets the data and connects the dots, using NLP, ML models, GenAI, and other cutting-edge tech.

That data then flows into dashboards or digital twins to signal issues, such as schedule slips or safety risks, and debrief decision-makers on project performance, resource utilization, and risk areas.

Why data analytics in the construction industry matters

A shortfall in construction output looms in the built environment. The worries about the

Architectural, Engineering, and Construction (AEC) industry’s prospects are understandable: labor shortages and productivity challenges may lead to a dip in construction output of up to $40 trillion. Digital technologies, and data analytics tools, in particular, can flip the script in the high-stakes construction industry.

Improved decision-making

Over 43% of engineering and construction (E&C) leaders say they urgently need forecasting and analytics to anticipate labor needs and optimize teams.

Insights distilled by data analytics for construction go a long way, from fewer costly delays and rework to better resource allocation and on-site efficiency. With real-time visibility across sites, E&C firms can orchestrate labor, materials, and equipment with more precision, long before delays and cost overruns eat into the project timeline or budget.

For example, Bechtel, a global engineering and construction leader, deployed 60,000 RFID tags to track 100,000+ components for three LNG plants, which cut material tracking time by 50% and sped up logistics.

Increased productivity

Construction companies have been trapped in a productivity rut forever. In fact, the industry will need to double its growth rate to meet the optimistic projections for 2040. By advancing construction analytics, AEC teams can automate routine decisions like auto-adjusting crew schedules, spotting equipment underuse, and predicting material shortage, keeping the project moving without stalls.

Cost savings

Under the traditional linear engineering, procurement, and construction (EPC) model, the design is finished when the construction is already underway, resulting in costly late-stage changes and bloated expenses. Predictive analytics allows teams to layer schedules and costs early in the project, which connects design intent to execution and mitigates budget risks.

To that end, historical data analysis can also circle back to former construction projects to develop more accurate estimates for future projects. 

During the construction of the Lusail Iconic Stadium (Qatar 2022 FIFA World Cup), the team leveraged predictive algorithms to analyze procurement patterns and site logistics. This helped them cut material waste by 25% through just-in-time deliveries and optimized placement.

Enhanced safety

The annual number of fatalities in the construction process (1075 work-related deaths in 2023) is the highest among industries. By analyzing real-time data feeds from wearables, connected equipment, drones, and sensors, data-based risk analysis tools can catch unsafe conditions on the spot. ML models can also predict high-risk scenarios, while computer vision systems can keep an eye on using the appropriate PPE.

Optimized project outcomes

Analytics in the construction industry isn’t just about reacting faster, it’s about building smarter from the start. When longitudinal data is integrated across all project phases, analytics tools ensure that construction management reflects real field conditions rather than isolated static plans developed in a vacuum. 

Instead of leading with initial estimates and firefighting, teams can make evidence-based decisions in real time, reallocating resources, rerouting materials, and adjusting timelines in line with site performance. 

While working on 3 projects spanning 51,375 sqm (552,975 sqft), one of our clients relied on our custom performance-data tool to evaluate each subcontractor’s completion rates against the original plan. This not only helped our client stay ahead of issues like delays in material deliveries for the electrical teams but also allowed them to keep the overall project timeline on track.

Built intelligence in action: real-world construction analytics examples

A single construction project generates millions of data points. Equipment usage logs, material deliveries, supply chain lead times, and other operational data snowball into a tangle of insights. Being able to collect, process, and make sense of that farrago gives E&A companies the potential to realize value throughout the entire project lifecycle.

Project planning and budgeting: “Are we setting ourselves up for overruns?”

Construction data analytics tools inherit their intelligence from past performance, including internal unstructured data on things like RFIs, change orders, and weather delays, along with external data such as commodity prices. This combination of data allows forecasting systems to come up with more accurate project plans that factor in recurring risks and on-the-ground conditions.

For example, if HVAC procurement or permit approval timelines led to delays in the majority of past hospital projects, planning tools can embed those patterns into the timeline of a new build. This also gives a heads-up to companies to adjust their milestone targets or pre-order long-lead items. 

Additionally, sophisticated forecasting tools can plug into BIM to simulate project scenarios and optimize resource allocation and sequencing, long before construction begins.

Resource allocation: “Do we have the right people, equipment, and materials for that?”

At the workforce level, digital analytics tools can compare labor capacity against active and upcoming project needs, breaking down roles, timelines, and workload projections. For example, if the analytics suite detects a surplus of superintendents, project managers can re-assign them to other struggling sites or proactively line up new work by bidding on upcoming projects.

Advanced analytics systems can also comb through historical equipment usage rates, idle time, fuel consumption, and material delivery patterns to highlight areas for optimization in future projects. For instance, if telematics data shows a crane sits idle 30% of the time on similar projects, project managers can rotate equipment across sites to maximize utilization. 

Bidding: “Should we bid on this project, and if so, how much?”

Data-driven bidding tools tie historical data such as labor types, contract arrangements, local spending trends, win/loss ratios, and other retrospective information to project profitability and the right contingency cushion. Over time, such insights can support long-haul forecasting of workload and financial performance, allowing construction executives to match bidding strategies with financial and operational goals.

For one of our clients, data analysis of over 100 past projects demonstrated that less obvious factors like unionization rates and contract types (not just region or sector) have the strongest impact on profit margin. The revealed insights helped the company to overhaul its bidding strategy and skew it toward higher-margin project profiles.

Predictive equipment maintenance: “What equipment do we service now before it goes down later?”

Unlike operator logs and anecdotal evidence, sensors and telematics provide a real-time pulse check on machine health by collecting data on engine temperature, vibration levels, and other performance parameters. Analytics solutions ingest that real-time data and flag subtle signals of wear or potential failure. 

Based on the configuration, data-driven maintenance systems can either alert teams to anomalies or autonomously schedule maintenance orders. For example, if the vibration data from a bulldozer’s sensors surpasses a predefined threshold, the system can trigger a maintenance request months before downtime instead of waiting for it to fail.

Since they recommend service based on condition data rather than fixed intervals, maintenance tools extend the lifespan of assets, save thousands of dollars in maintenance costs, and ensure that machinery is serviced exactly when needed: no sooner, no later.

Subcontractor performance analysis: “Are our subcontractors walking the talk?”

Because of paper-based processes and inconsistent reporting, construction companies can’t get a handle on subcontractor performance until it’s too late. Let’s say the electrical subcontractor is scheduled to finish rough-ins this week. Ideally, a company should be able to access data like their daily reports, inspection issues, and budget tracking in real time to spot potential drag-ons in their track.

With manual tracking, this level of traceability would be lost for good in cryptic paper logs, email threads, and evasive verbal updates. But thanks to big data analytics in the construction industry, field updates, labor logs, cost codes, and other subcontractor-generated data are synced automatically in one central platform. This allows construction firms to proactively monitor KPIs and easily spot if the electrical subcontractor underperforms on daily productivity goals.

Sustainability and waste reduction: “Where are we generating avoidable waste?”

On the path to their green objectives, E&A companies resort to construction waste management tools that capture ESG data from LEED-certified projects, including metrics on carbon, water, energy, and material usage. The data is centralized and communicated through intuitive dashboards that zoom in and out on waste indicators, recycling rates, and M/WBE metrics.

Combined with predictive alerts, ESG analytics tools also warn teams when waste thresholds are approaching and how they can optimize their procurement strategies to reduce excess. Project-level insights roll up into corporate ESG reports, LEED documentation, and other sustainability compliance programs.

A clear 4-step roadmap to successful construction analytics implementation 

If your construction company is at the very dawn of adopting digital analytics tools, you need a solid foundation, similar to that of any well-built structure on construction sites.

1. Assess your data infrastructure 

Break the ground by estimating the quality and availability of your existing data sources, even if you don’t have formal systems like construction planning systems and risk management tools. This will help you and your data analytics partner lay the ground for a tailored roadmap, including tech stack selection, data integration strategy, and more.

2. Kick the tires by starting with small-scale, high-value use cases

Start with focused, easily measurable pilot projects that would deliver quick gains and easily scale into bigger initiatives. Examples include projects like construction planning solutions, predictive maintenance for select machinery, material inventory management, and crew scheduling optimization.

3. Integrate construction-specific systems within a unified data hub

Connect BIM, ERP, IoT sensors, and other data-generating construction technology into a centralized platform. Through API-driven integrations and real-time data pipelines, this unified data hub will enable precise analysis of construction data at the organizational and project levels.

4. Employ advanced technologies for precise analysis

Machine learning, natural language processing, computer vision, and generative AI add new dimensions to data analysis by extracting insights from different data modalities. Whether it’s structured sensor data, unstructured text from reports, or camera feeds, these technologies can reveal insights that traditional analytics methods overlook.

5. Focus on data governance and security

Make sure to establish defined data governance protocols to ensure data accuracy, consistency, and compliance across construction data sources like BIM, ERP, and others. Use industry frameworks such as ISO 19650 as a reference point and supplement your strategy with solid data security measures such as RBAC, end-to-end encryption, and other fit-for-purpose safeguards.

Put your construction data to work

Data analytics in construction has taken on a strategic, compound mission of driving performance, clarifying complex insight narratives, and giving E&A companies a decisive edge. But to pull its weight, data analytics tools demand clean, connected, and contextualized data, paired with the right tech stack and a clear roadmap. 

Partner with our ISO-certified, AI-driven software engineering company to transform your raw construction data into real-time insights that keep your projects on schedule and under budget.