Reclaiming Data Sovereignty: Transforming the "Hidden Labor Tax" into Predictive Pavement Intelligence

State Departments of Transportation (DOTs) are currently at a crossroads: while data collection technologies have advanced rapidly, the actual analysis and project prioritization often remain trapped in siloed spreadsheets or "Black Box" consulting dependencies. This session explores the industry-wide shift toward Data Sovereignty, where agencies move from renting a static report to owning a live, predictive intelligence model.

We will examine the reality of the hundreds of staff hours lost each year to manual "swivel-chair" data entry and to reconciling disconnected pavement plans with work management systems. By transitioning to a GIS-centric, native integration framework, U.S. DOTs are now eliminating this friction, ensuring that their Linear Referencing Systems (LRS) and asset models live in a single source of truth.

A key component of this session will be a discussion on how this framework ensures that data captured during construction transitions seamlessly into long-term safety and performance reporting. We will further explore how AI-informed predictive modeling can reduce the Total Cost of Ownership (TCO) by 30% by transforming raw as-built data into actionable "as-maintained" digital twins.

Attendees will learn how to:

· Quantify the Cost of Data Friction: Understanding how to identify and eliminate "phantom assets" and synchronization errors that plague traditional modeling.

· Shift from "Worst-First" to Multi-Constraint Optimization: How to use benefits-driven analysis (area-under-the-curve) to justify long-term funding increases, as demonstrated by peers.

· Integrate Digital Twins into the MQA Lifecycle: Strategies for connecting Maintenance Quality Assurance (MQA) condition assessments directly to budget projections and work orders.

Collaborative Perspective: This session is designed as an educational review of current digital delivery trends. It will feature insights into the real-world experiences of a representative agency (such as Texas or California) to discuss the workforce transformation required to move from manual data stewardship to strategic, interoperable decision-making.

Sponsored by