A railroad network is a complex physical and digital system, including vast volumes of constantly changing data related to schedules, infrastructure, maintenance, staff availability and customer feedback. Reliable and shared situational awareness is just as important for passengers and stakeholders as it is for VR’s business units. When we started, data collection, transformation and sharing were fragmented, laborious and prone to human errors that come with manual work. To solve these issues, we co-developed a centralized data platform and system interfaces that help keep people on track.

WHAT WE DID

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Our work included participation in two areas: system integrations and architecture and data warehousing and modeling. The system integrations and architecture define how the platform performs and how it can be further developed. The architectural backbone was built using technologies like AWS and Snowflake. The goal of the data warehousing and modeling development was to create a centralized data storage expanding over multiple source systems, from which the data is collected and modeled for several user needs with the frequencies varying from hours to weeks. To handle the large data masses, ingested from numerous different data sources, reliably, Data Vault 2.0 was chosen as a modeling methodology. The entire development process was run using agile Scrum methods, with our experts working as a part of the VR data team. The solutions were tested with the end users and iterated until all parties were satisfied.

WHY IT MATTERS

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The centralized data platform provides stakeholders, employees and business representatives with the information they need, when they need it. Shared data helps prevent, predict and solve problems as well as understand their root causes. All this improves the travel experience, ensuring successful cooperation with VR and strengthening the interaction between business units. As the data is modeled to clearly describe real operational concepts, technical developers and business representatives can work together more efficiently, understand the real value of data and generate new ideas. The data platform also enables the use of analytics and creates the foundation for data-enabled applications, such as situational awareness, machine learning and AI and saves costs in terms of workflow and task automation.