Blueprinting a Better Data Strategy in Construction

Construction projects are inherently complex, marked by tight margins, shifting timelines, and workforce volatility. Fragmented, inaccurate, or inaccessible data exacerbates many of these risks.

According to McKinsey, construction firms invest just 1–2% of their revenue in IT — less than half the cross-industry average of 3–5%. This underinvestment leaves critical systems disconnected due to siloed or inaccessible data.

And the cost is real: IBM estimates bad data drains $3.1 trillion annually from U.S. businesses through lost sales, rework, and inefficiency. For construction entities, this often takes the form of missed deadlines, inaccurate forecasts, and lower productivity.

But a well-executed data strategy enables smarter decision-making, empowers more accurate job costing, and prepares your company for tools like AI, IoT, and automation.

Here’s how to build a construction data strategy that works:

Inventory & Map Your Data Landscape

Start by asking: What decisions are we trying to make, and do we have the right data to support them?

Then, conduct a comprehensive audit, documenting what data you capture, how you capture it, and where it resides (spreadsheets, ERP, field apps, QMS tools).

The goal here isn’t just cleanup. It’s laying a strategic foundation by uncovering redundant or siloed data sources and identifying integration points.

Why It Matters

Strong analytics cannot be built on disconnected systems or incomplete information. A data map is your blueprint for smarter integration.

Next Step

Engage finance, operations, and IT stakeholders to document your full data ecosystem.

Centralization & Clean Data

Next, create a single source of truth:

• Use a central data platform like a cloud-based data warehouse or modern ERP.
• Cleanse inputs to ensure consistency (e.g., vendor naming, job codes, cost categories).
• Automate data capture whenever possible — mobile apps, RPA, sensor feeds.

Why It Matters

Clean, centralized data allows better insights and saves hundreds of hours in report consolidation.

Next Step

Assign a data governance lead to define naming conventions and cleanup cadence.

Analytics & Insights

Visualization is key. If your crews can’t quickly grasp the story, the data loses value.

Once the foundation is set, it’s time for the analytics. Use a data visualization platform like Power Bi to:

• Build dashboards for key metrics.
• Benchmark performance across crews, locations, and project types.
• Predictive analytics can quickly flag overruns, staffing issues, or supply chain bottlenecks.

Why It Matters

This strategic shift allows you to act before problems escalate.

Integrate & Automate Operations

Use integration and automation to make insights usable:

• Connect mobile field apps to ERP payroll and procurement systems.
• Implement RPA for manual workflows like invoice entry, submittals, and meeting minutes.
• Ensure bi-directional flows — data informs operations and operations generate quality data.

Why It Matters

Smart integrations cut manual work, speed up processes, and reduce risk. To begin, identify one repetitive workflow and pilot a solution that connects your field tools to ERP. Measure time saved and rework avoided to build ROI momentum.

Automating Invoice Approvals with RPA

Use Robotic Process Automation (RPA) to streamline the invoice approval process. Instead of manual entry, the bot scans PDF invoices, extracts key data, and routes it to an approver via Microsoft Teams. Once reviewed, the invoice is saved to SharePoint, uploaded into the billing system, and logged in a tracking spreadsheet — all without human intervention.

Embed Accountability & Culture

Data tools aren’t enough — they require disciplined usage:

• Assign data stewards for each domain (finance, operations, field).
• Tie KPIs to performance reviews and project accountability.
• Provide regular training and forums for sharing best practices.

Why It Matters

Without defined roles and expectations, even the best systems stall out. Accountability ensures insights turn into action.

Next Step

Create a cross-functional data council to maintain standards and encourage adoption across teams.

How Data Drives Real Impact: Building Tech Trust & Transparency with BZI

When Building Zone Industries (BZI) partnered with us, their goal was clear: break down operational silos and improve real-time decision-making through better data visibility. By integrating key systems — mobile field reporting, scheduling, procurement, and finance — into a shared data platform, BZI achieved:

• Real-time dashboards for data on job progress, profitability, and resource use
• Automated project updates, eliminating duplicate data entry and delays
• Data transparency that empowered frontline crews and corporate teams with the same up-to-date project insights

As a result, BZI saw a marked increase in innovation, faster responses to field issues, and improved trust across teams — a transformation grounded in accessible, integrated data.

Proven Keys to Data Success

Through our work in the construction industry, we have found that these lessons consistently drive the best results:

1. Executive sponsorship is mandatory. Data leadership must come from the top.
2. Define clear, job-relevant metrics — not vanity metrics.
3. Prioritize quick wins to build momentum and trust.
4. Use scalable technology with integration and flexibility in mind.
5. Embed accountability into your culture. Data adoption must be sustained by people and processes.

Ready to Build Data-Driven Construction Business?

A high-performing data strategy starts with a clear map, clean inputs, and smart automation. When done right, it fuels intelligent operations, AI readiness, and stronger margins.

At Eide Bailly, we help construction firms transform disconnected efforts into integrated, scalable performance.

Let’s build smarter together.

Construction Data Strategy FAQs

Why is a data strategy so important for construction companies?

A robust data strategy improves job-cost accuracy, increases productivity, and lays the foundation for future tech investments like AI and IoT. Without it, companies risk financial losses, missed deadlines, and lower productivity due to fragmented or inaccurate data.

What’s the first step to building a better data strategy?

Start by inventorying and mapping your data landscape. Document what data you capture, how you capture it, and where it resides (spreadsheets, ERP, field apps, QMS tools). This audit uncovers redundant or siloed data sources and identifies integration points for smarter decision-making.

How can construction firms centralize and clean their data?

Use a central data platform like a cloud-based data warehouse or modern ERP. Cleanse inputs to ensure consistency (e.g., vendor naming, job codes, cost categories) and automate data capture whenever possible—using mobile apps, RPA, or sensor feeds. Assign a data governance lead to define naming conventions and cleanup cadence.

What are the risks of poor data management in construction?

Poor or incomplete data can lead to significant financial losses, decreased productivity, missed deadlines, inaccurate forecasts, and lower margins.

How can analytics and visualization help construction teams?

Visualization platforms like Power BI help crews quickly grasp key metrics and performance benchmarks across projects, locations, and teams. Clean, centralized data allows for better insights and saves hundreds of hours in report consolidation.

Who should be involved in developing a data strategy?

Engage finance, operations, and IT stakeholders to document your full data ecosystem and ensure buy-in across the organization.