June 2025
It’s all about the data. Big data.
In construction, big data isn’t just mountains of information. It’s a nonstop stream of complex, real-time inputs from stakeholders, teams, workflows and systems—all meant to generate insights into every aspect of a project, from performance and progress to productivity, resource allocation, risk and safety.
But here’s the catch: it’s often fragmented, siloed or inconsistent—leading to confusion or costly mistakes.
Industry experts drive this home: no matter what tools you’re using—artificial intelligence (AI), business intelligence (BI) dashboards or even spreadsheets—the quality of your insights depends entirely on the quality of your inputs.
That’s why better data matters.
What is better data? It’s data that’s consistently captured, digitized, structured and connected. That’s what creates the accuracy and reliability that define quality.
Let’s break it down—why better data is the key to making big data deliver.
The Risk: Relying on Flawed Insights
Poor data quality is more than a technology issue—it’s an all-out business risk.
No matter how much data you’ve collected, if it’s incomplete, inconsistent or poorly structured, even the most advanced tools—including AI—will deliver flawed insights. Poor inputs yield poor outcomes.
How poor could outcomes be for megaprojects?
- Inaccurate estimates, budgets and unreliable forecasts
- Unfeasible schedules that lead to costly delays
- Unidentified risks with little to no mitigation or contingency planning
- Avoidable rework that adds to costs and timelines
- Reputational damage that affects future bids and partnerships
- Faulty benchmarks that skew future project planning
- Diminished profits and financial solvency
No construction leader wants to base multimillion- or multibillion-dollar decisions—or the success of an entire megaproject—on questionable data. Because if you can’t see the whole picture, neither can the tools analyzing it—AI or otherwise.
Big data can be a competitive advantage—but only when it’s good data. Otherwise, it becomes a liability. The financial toll of bad data on construction is substantial. In 2020 alone (the most recent year for which there’s data), an often-cited report estimated the industry lost $1.8 trillion—an amount that’s likely grown as project costs have surged.
The real head-scratcher is that much of this is preventable with better data management.
So what can construction companies do?
The Priority: Managing Data, Not Just Collecting It
Project truth starts with quality data—and that takes disciplined management. Focusing on these areas builds it.
Eliminating data silos for a more complete project picture. Data comes from every facet of a project—including subcontractors, suppliers, jobsite crews, systems, equipment and daily on-site activities. However, when that data lives in disconnected silos, you don’t have a complete view—like trying to put together a puzzle with a bunch of pieces missing. And the decisions will reflect that; without clarity, you can’t manage costs, mitigate risks or make well-grounded choices.
To get real value out of big data, it has to be usable—and that means it has to move. It needs to flow across teams, platforms, systems and project phases. Standardizing formats and using a shared platform breaks down silos and improves visibility.
It’s the difference between guessing and knowing.
When data is integrated into a single source of truth, reactive problem-solving becomes proactive control. It’s easier to compare actual labor productivity to what was planned, isolate delays tied to specific trades or vendors and validate whether actual costs align with project progress. Patterns are identified early—such as material delivery lags or approval workflow slowdowns—for faster course-correction.
Validating and refining datasets to strengthen big data insights. Your company might be ready—with digital tools in place, even exploring AI.
But is your data ready?
This is all about getting your data right so that it makes sense. That means prepping the data—cleaning, structuring and validating it—so it can be analyzed accurately.
- What is being fed into your system?
- Is it current and reliable?
- Where is it stored?
- What is our governance process?
Digital tools—from dashboards to forecasting software to cost control platforms—won’t deliver meaningful insights if they’re working from inadequate inputs.
Ensuring clean data is no small task—especially when digitizing older, incomplete or unstructured data that now feed into big data environments. However, if you’re counting on digital insights to manage large-scale projects, it’s essential.
A clear data strategy—built to clean and structure large volumes of inputs—helps make big data more usable and your insights more accurate.
Leveraging historical project data to strengthen future forecasting. Every major decision has cost implications. And those decisions are only as strong as the data backing them up. That’s what makes historical project data such a powerful forecasting tool. It’s a record of trends, risks and cost drivers that influenced past outcomes—giving leaders a data-backed way to build smarter estimates and plans.
Even when past projects missed milestones or blew past budgets, the data still has value. Analyzing it reveals what went wrong, why it happened and how it impacted the outcome. Using these insights can help teams avoid repeating those mistakes.
Without historical data, a data gap exists—and that gap is often filled with assumptions or outdated benchmarks instead of real, reliable information.
That’s what makes historical data more than just a reference point. It’s a strategic forecasting asset.
One important caveat: historical data isn’t always clean or consistent. If project standards weren’t in place at the time, it may need to be validated, cleaned and structured before it’s ready to support accurate forecasting.
The takeaway: Don’t overlook the data you already have. Understand what worked, what didn’t and why—so you can forecast with more accuracy and confidence.
The Advantage: Making Big Data Work for You
Big data in construction has massive potential—but only if it’s built on the right foundation. When data is messy or incomplete, it leads to flawed insights, risky decisions and missed opportunities.
To make big data work for you—not against you—you need a clear data strategy. That means focusing on quality: cleaning, connecting and standardizing your information so your digital tools have something solid to work with.
With unified, trusted data, you can:
- Derive more meaningful insights
- Make faster, better-informed decisions
- Forecast costs and schedules with greater confidence and accuracy
- Benchmark more clearly against past performance metrics
Bottom line: Better tools won’t fix bad data. But better data will make your analytics sharper and every decision stronger.
Contruent can help you get the most out of your big data. Its lifecycle cost management software, Contruent Enterprise, empowers you to manage project data more effectively, delivering real-time insights that lead to positive outcomes. Learn more or request a demo today.