Robert Halfhill

As energy operators advance toward predictive maintenance and AI-enabled asset management, fragmented inspection data and inconsistent criticality models continue to limit progress. This panel explores how establishing a single, trusted source of truth across inspection and asset data enables more accurate, asset-specific criticality. Panelists will share practical approaches to improving data quality, strengthening analytics, and embedding risk into enterprise decision-making, ultimately helping organizations prioritize the right work at the right time, reduce downtime, and unlock scalable asset intelligence.

• Consolidating inspection records, harmonizing code sheets and standardizing data structures to eliminate site‑to‑site variability and build a trusted foundation for risk‑based and condition‑based decisions
• Building a single, validated source of truth by standardizing and governing inspection data across systems and sites
• Strengthening analytics to detect degradation earlier and improve confidence in condition and risk insights
• Overcoming organizational and cultural barriers to scale data-driven, risk-based asset management