Across industries, maintenance and reliability professionals face a growing paradox: organisations have more operational data than ever, yet decision-making confidence is falling. Despite advanced systems, teams spend more time validating or reconciling data than interpreting it. This session explores the widening gap between data collection and data utilisation across mining, energy, utilities, and infrastructure sectors.

Rapid digitisation has created not just data abundance but also fragmentation, inconsistency, and loss of historical context. Field technicians and engineers are increasingly burdened by data entry and quality control, leading to a shift where technology overshadows intuition and poor data erodes trust in analytics and maintenance decisions.

Our panel of will unpack the root causes of this “data quality versus quantity” issue. Through real-world cases, they will show how governance frameworks, data purpose, and quality standards can turn raw information into actionable intelligence. Participants will explore how aligning data strategies with business needs restores trust and ensures every data point adds value. By challenging assumptions about digital maturity, this session reframes “data-driven” as a focus on relevance, not accumulation — charting a path to renewed confidence and clarity in maintenance data ecosystems