What Your Equipment Data Isn’t Telling You—And What It’s Costing You

The problem isn’t that construction fleets lack data—most operations collect more signals than ever. The problem is what happens between collection and interpretation. Without machine-specific normalization, a scissor lift running at 60% platform load looks identical to one sitting idle with the engine on.

This gap has compounding consequences. Poor data interpretation leads to equipment dispatched to the wrong sites, underutilized assets carried on the books, maintenance scheduled too early or too late. As AI-driven tools become embedded in fleet management, the stakes rise further. Predictive maintenance models are only as reliable as the data they’re trained on. Feed a model misclassified run hours and it learns to misclassify outcomes. Bad data at scale produces confidently wrong answers at scale.

This webinar examines what trustworthy data architecture looks like for construction equipment, why most current approaches fall short and what fleet operators gain when their data is accurate enough to power decisions—and the AI tools increasingly making those decisions at scale.

Join this webinar to learn:

• Why the most commonly tracked equipment metrics (run hours, location, fuel consumption) fall short of what’s needed to make confident operational decisions
• How data misinterpretation happens across mixed equipment fleets and why different machine types require different normalization logic to produce reliable outputs
• How contractors are using higher-fidelity equipment data to improve allocation decisions, reduce idle costs and sharpen maintenance timing across multisite operations
• How to reduce cost of underutilization: identify which assets are being counted as productive when they aren’t, and right-size fleet deployment accordingly
• How to get more out of AI investments: ensure the predictive maintenance, utilization forecasting and productivity tools you’re adopting are working from data that’s actually reliable