AusNet Services predicts asset failure with Databricks-powered analytics
AusNet Services, an Australian energy and infrastructure company serving 1.5 million customers, migrated its data platform to Azure Databricks and Delta Lake to consolidate over 11 billion electricity, gas and network connection assets, enabling predictive maintenance across asset classes that reduced risk and cut costs, achieving 50% cost savings on the data platform, 3x faster data processing, and a 20%-30% reduction in operational overhead.
Overview
AusNet Services, an Australian energy and infrastructure company serving 1.5 million customers, migrated its data platform to Azure Databricks and Delta Lake to consolidate over 11 billion electricity, gas and network connection assets, enabling predictive maintenance across asset classes that reduced risk and cut costs, achieving 50% cost savings on the data platform, 3x faster data processing, and a 20%-30% reduction in operational overhead.
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Inspect the highlighted sourceThe challenge
AusNet Services faced stability and scalability challenges with its existing compute platform as it managed over 11 billion assets and anticipated more than 65TB of data growth over four years across separate data warehouses, making it hard to derive meaningful insights or assess asset condition ahead of damage.
The solution
AusNet Services migrated its production workloads to the Databricks Data + AI Platform on Azure, using Delta Lake's Time Travel feature and Data Vault 2.0 modeling to process large volumes of metering data reliably, shifting to data-driven predictive maintenance for three asset classes based on condition, criticality and risk assessment.
Reported business value
AusNet Services achieved 50% cost savings on its data platform, 3x faster data processing, and a 20%-30% reduction in data platform operational overhead, and is now expanding predictive maintenance across its entire fleet of assets after significant risk reduction and cost optimization in its initial three asset classes.
Sources
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