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Cutting through to accelerate time to value from critical asset management platform.

Cloud-ready enterprise data warehouse the foundation for enabling asset managers to solve business critical problems.

The client manages rail freight services across all major capitals cities, markets, regional freight centres and import/export ports in Australia. The implementation of a new asset management platform required outside thinking to leverage the massive volumes of data being captured to fully realise the benefits of platform. Velrada took control of the business intelligence components of the solution and integrated a range of data sources to join the dots across the organisation and give the executive and operational team’s deep insight into how to optimise operations across the country.

Understanding the movement of assets across the network has delivered significant efficiency improvements and laid the foundation to migrate on premise technology infrastructure to the cloud faster than predicted.

The importance of understanding data and optimising operations in transport and logistics is a key driver of efficiency. For the client, forecasting and scheduling capabilities rely on an up to the minute understanding of movements across the rail network and existing systems required manual data management, creating inefficiency and the opportunity to increase the capacity of the network. Velrada took control of the implementation of a ‘cloud-ready’ enterprise data warehouse (EDW), and ensured ease of use through a self-service BI solution and data visualisations.

With deep experience in ‘traditional’ BI and enterprise data warehouse capabilities as well as a modern, cloud-first approach to data and business intelligence, Velrada was able to develop a structured implementation plan which catered to existing constraints by delivering an on-premise EDW initially, with the vision to move to a fully hosted Azure platform. This enabled rapid data cleansing and supporting data quality improvements and accountability meaning asset managers could resolve problems such as long and short kilometres, linear asset performance and scheduling efficiency.

The self-service BI solution was built on SQL Server Analysis Services, allowing the client to leverage their existing technology investment while supporting a variety of analysis and visualisation toolsets. Models were developed using SSAS Tabular – which provides a number of advantages over traditional SSAS cubes – to provide a business-oriented translation of the relational structures of the EDW, and augment the underlying data by defining relationships, calculated measures and hierarchies. The models are exposed through connection files and samples made accessible through the client’s SharePoint-based intranet environment.

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