Databricks Case Studies Transforming the Australian Rail Industry’s Digital Ecosystem with IoT

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	Transforming the Australian Rail Industry’s Digital Ecosystem with IoT Databricks

Transforming the Australian Rail Industry’s Digital Ecosystem with IoT

Databricks

Technology Category	Analytics & Modeling - Predictive Analytics Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries	Railway & Metro Transportation
Applicable Functions	Logistics & Transportation Maintenance
Use Cases	Predictive Maintenance Transportation Simulation
Services	Data Science Services System Integration
Challenge	The Australia Rail Track Corporation (ARTC) is a crucial part of Australia’s supply chain, managing and monitoring goods transportation across 8,500km of rail network. The corporation collects vast amounts of data to ensure the timely delivery of cargo freight. However, ARTC was dealing with the complexity of siloed data across several segregated data sources, which posed inefficiencies and unnecessary complexity. This also impacted the cost of managing a network that is segregated across remote locations in Australia. The data sets were required by more than 150 applications generated from different data sources, meaning any change to the data needed to be altered in several systems. ARTC embarked on a business transformation strategy to digitally modernize and manage physical rail assets more efficiently, while building resilience to readily respond to customer demands, regulatory compliance, and varied world events. Read More
About Customer	The Australia Rail Track Corporation (ARTC) plays a vital role in Australia’s supply chain, managing and monitoring goods transportation across 8,500km of its rail network throughout Australia. ARTC collects vast amounts of data to monitor operations and ensure the timely delivery of cargo freight on its rail network. As part of its Digital Integration Strategy, ARTC endeavored to remove the complexity of its siloed data across several segregated data sources with a more modern, streamlined approach, in order to leverage data-driven insights and strategies — resulting in cost savings and increased efficiencies. Read More
Solution	ARTC implemented the first phase of its five-year digital strategy with the help of Databricks. They launched the Enterprise Integration Data Platform (EIDP) built on a cloud-based data lakehouse architecture on Microsoft Azure. This enabled the business to unify its approach to data management, advanced analytics, and ML-powered solutions. With the democratization of ARTC's data on a lakehouse architecture, data can now be extracted and decoupled easily, allowing immediate changes to be made securely from a single source of truth. Business intelligence tools such as Databricks SQL and Power BI have allowed ARTC's team to extrapolate insights easily in a self-service way, supporting decision making across ARTC's operations. ARTC's data engineers are now able to perform multiple functions in a secure and seamless way — across data ingestion, data preparation, and data analysis. They have built a data-driven ecosystem where ARTC's systems can communicate with each other seamlessly and implement groundbreaking design patterns with an event-driven architecture and high technical maturity. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

Railway & Metro

Transportation

Applicable Functions

Logistics & Transportation

Maintenance

Use Cases

Predictive Maintenance

Transportation Simulation

Services

Data Science Services

System Integration

Challenge

The Australia Rail Track Corporation (ARTC) is a crucial part of Australia’s supply chain, managing and monitoring goods transportation across 8,500km of rail network. The corporation collects vast amounts of data to ensure the timely delivery of cargo freight. However, ARTC was dealing with the complexity of siloed data across several segregated data sources, which posed inefficiencies and unnecessary complexity. This also impacted the cost of managing a network that is segregated across remote locations in Australia. The data sets were required by more than 150 applications generated from different data sources, meaning any change to the data needed to be altered in several systems. ARTC embarked on a business transformation strategy to digitally modernize and manage physical rail assets more efficiently, while building resilience to readily respond to customer demands, regulatory compliance, and varied world events.

About Customer

The Australia Rail Track Corporation (ARTC) plays a vital role in Australia’s supply chain, managing and monitoring goods transportation across 8,500km of its rail network throughout Australia. ARTC collects vast amounts of data to monitor operations and ensure the timely delivery of cargo freight on its rail network. As part of its Digital Integration Strategy, ARTC endeavored to remove the complexity of its siloed data across several segregated data sources with a more modern, streamlined approach, in order to leverage data-driven insights and strategies — resulting in cost savings and increased efficiencies.

Solution

ARTC implemented the first phase of its five-year digital strategy with the help of Databricks. They launched the Enterprise Integration Data Platform (EIDP) built on a cloud-based data lakehouse architecture on Microsoft Azure. This enabled the business to unify its approach to data management, advanced analytics, and ML-powered solutions. With the democratization of ARTC's data on a lakehouse architecture, data can now be extracted and decoupled easily, allowing immediate changes to be made securely from a single source of truth. Business intelligence tools such as Databricks SQL and Power BI have allowed ARTC's team to extrapolate insights easily in a self-service way, supporting decision making across ARTC's operations. ARTC's data engineers are now able to perform multiple functions in a secure and seamless way — across data ingestion, data preparation, and data analysis. They have built a data-driven ecosystem where ARTC's systems can communicate with each other seamlessly and implement groundbreaking design patterns with an event-driven architecture and high technical maturity.

Impact #1	The implementation of the Enterprise Integration Data Platform (EIDP) has resulted in a more agile digital ecosystem with data at the helm of its innovation. The platform has enabled ARTC to unify its approach to data management, advanced analytics, and ML-powered solutions. The data engineers are now able to perform multiple functions in a secure and seamless way, across data ingestion, data preparation, and data analysis. This has led to the creation of a data-driven ecosystem where ARTC's systems can communicate with each other seamlessly and implement groundbreaking design patterns with an event-driven architecture and high technical maturity. The new system has also prepared ARTC for future events, providing seamless access to rail track data and a consolidated view of its track health. This helps to prevent outages and forward-plan track maintenance schedules using predictive analytics, further preventing disruption to operations and network failures.

Impact #1

The implementation of the Enterprise Integration Data Platform (EIDP) has resulted in a more agile digital ecosystem with data at the helm of its innovation. The platform has enabled ARTC to unify its approach to data management, advanced analytics, and ML-powered solutions. The data engineers are now able to perform multiple functions in a secure and seamless way, across data ingestion, data preparation, and data analysis. This has led to the creation of a data-driven ecosystem where ARTC's systems can communicate with each other seamlessly and implement groundbreaking design patterns with an event-driven architecture and high technical maturity. The new system has also prepared ARTC for future events, providing seamless access to rail track data and a consolidated view of its track health. This helps to prevent outages and forward-plan track maintenance schedules using predictive analytics, further preventing disruption to operations and network failures.

Benefit #1	30% faster time to insights for freight logistics optimization
Benefit #2	80% improvement in data ingestion speed
Benefit #3	80% reduction in data engineering hours, decreasing overall costs

Benefit #1

30% faster time to insights for freight logistics optimization

Benefit #2

80% improvement in data ingestion speed

Benefit #3

80% reduction in data engineering hours, decreasing overall costs

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Overview

Transforming the Australian Rail Industry’s Digital Ecosystem with IoT

Operational Impact

Quantitative Benefit