Access this on-demand session to explore how predictive maintenance and advanced analytics can transform asset management. Many organisations still rely on reactive fixes or broad scheduled maintenance, which can lead to unnecessary cost, unexpected failures and operational inefficiencies. This session demonstrates how a data-driven approach enables more accurate risk assessment and more effective maintenance planning across a wide range of asset types.

You will see practical examples of how predictive asset management applications are built and deployed, whether the assets involved are sewer pipes, turbines, servers, pylons, rails or automated machinery. The session shows how to prepare and model asset data using SPSS predictive analytics, how to combine static and dynamic information to generate precise risk estimates and how to enrich those predictions with insights extracted from unstructured sources such as inspection notes or call-centre records.

Finally, you will learn how predictive outputs like risk scores are integrated with existing systems, including asset registers, work management tools and operational dashboards. This is a clear, practical introduction for teams looking to move from reactive or scheduled regimes to a more targeted, data-driven maintenance strategy.