Construction Analytics
Data analysis and business intelligence for construction projects and portfolios.
Definition
Construction Analytics applies data analysis and AI to construction project data to provide actionable insights. These platforms aggregate data from multiple sources—schedules, costs, quality, safety—to identify trends, benchmark performance, and predict outcomes. AI-enhanced analytics can surface hidden patterns, identify root causes of problems, and recommend actions to improve project and portfolio performance.
In Depth
Construction analytics transforms project data into business intelligence — revealing patterns in cost performance, schedule adherence, safety incidents, quality issues, and productivity that are not visible in individual project reports. AI enables analytics across project portfolios, identifying trends that inform strategic decisions.
The cross-project analysis is where the strategic value lies. By analyzing data across all completed and active projects, AI identifies patterns — which project types consistently exceed budget, which subcontractors deliver the best quality, which building systems generate the most RFIs, and which specification sections cause the most submittal rejections. These patterns guide decisions about project selection, team assignments, subcontractor qualification, and specification improvement.
Examples
Dashboards showing project health across an entire portfolio
Identifying which project types consistently exceed budget
Analyzing change order patterns to improve future estimates
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Compatible Platforms
Nomic integrates with these platforms so you can use construction analytics across your existing project data:
Frequently Asked Questions
Construction Analytics applies data analysis and AI to construction project data to provide actionable insights. These platforms aggregate data from multiple sources—schedules, costs, quality, safety—to identify trends, benchmark performance, and predict outcomes. AI-enhanced analytics can surface hidden patterns, identify root causes of problems, and recommend actions to improve project and portfolio performance.
Dashboards showing project health across an entire portfolio. Identifying which project types consistently exceed budget. Analyzing change order patterns to improve future estimates.
Project Research: Instantly access all project-critical information from a single search interface.