Data-Driven Construction Decisions
Using AI and analytics to make better decisions in construction projects.
Definition
Data-Driven Construction Decisions refers to using artificial intelligence and analytics to inform decisions throughout the construction project lifecycle. AI can analyze historical data, current project status, market conditions, and predictive models to recommend actions that improve outcomes. Data-driven decision making replaces gut feelings and assumptions with evidence-based insights, leading to better project performance.
In Depth
Data-driven construction replaces opinion-based decisions with evidence-based decisions — using actual project data rather than individual memory and intuition to guide project strategy, resource allocation, and risk management. AI is the analytical engine that makes data-driven decision-making practical by processing the volume and variety of data that construction projects generate.
The shift from opinion to evidence is most impactful in preconstruction. When estimating a new project, data-driven firms analyze actual cost data from comparable completed projects rather than relying on one estimator's rules of thumb. When scheduling, they use actual duration data from past projects rather than optimistic projections. When assessing risk, they use claims and change order data from similar projects rather than gut feel.
Examples
Using historical data to predict realistic project durations
Analyzing subcontractor performance data to inform selection
Tracking KPIs to identify projects needing intervention
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Compatible Platforms
Nomic integrates with these platforms so you can use data-driven construction decisions across your existing project data:
Frequently Asked Questions
Data-Driven Construction Decisions refers to using artificial intelligence and analytics to inform decisions throughout the construction project lifecycle. AI can analyze historical data, current project status, market conditions, and predictive models to recommend actions that improve outcomes. Data-driven decision making replaces gut feelings and assumptions with evidence-based insights, leading to better project performance.
Using historical data to predict realistic project durations. Analyzing subcontractor performance data to inform selection. Tracking KPIs to identify projects needing intervention.
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