Teradata

Train operators the world over are expected to work miracles, i.e. never to be late. So, with acute service and availability targets to meet, an efficient maintenance program is important. And data-enabled functionality is a must for Siemens. Reactive maintenance (after an incident) and routine, preventive maintenance with its visual inspections and scheduled exchange of components, are no longer enough. We’ve moved on to more cost-effective, condition-based, predictive maintenance. The actual condition of components is measured via the transfer and remote monitoring of diagnostic sensor data; data which is also used to analyse patterns and trends. This helps predict when a component is likely to fail, so it can be repaired before anything untoward happens. To ensure the commercial sustainability of this approach, Siemens needs to use and re-use existing data, creating a kind of ‘Internet of Trains’. Towards this end, they’re analysing sensor data in near real time, which means they can react very quickly, ensuring that customer transport services aren’t interrupted. “It is really difficult to define every issue before it impacts operations using only data from the trains”, Kress explains. However, recent success stories prove that everything is possible.

The case study presents three different enterprises: a Top-5 Global Retail Bank, a UK Retailer, and a European Multiline Insurer, each facing unique challenges in enhancing their customer experience (CX). The bank was struggling with personalizing CX, requiring more granular detail in their data and analytics, and managing CX across all their digital channels. The UK Retailer was unable to maximize customer relationships due to a lack of insight into their customers' online activities. Their aggregated data was always 24 hours out of date, and they could only infer what customers wanted based on past behavior. The European Multiline Insurer was finding it difficult to capture insights from customers self-serving online. The limited data they had was typically 48 hours old, making it impossible to react to customers in the moment.

The citywide multi-modal transportation authority was facing a significant challenge due to the city's fast-paced development and growing population. The projected demand was expected to strain the public transportation system if not planned for properly. To serve these anticipated needs, the authority drafted its Land Transport Master Plan for transportation network investments that forecasted needs over a decade. In addition to a rapidly growing population, ever-expanding data volumes posed a considerable challenge to their technology infrastructure. The authority relies on data and applications to ensure smooth travel for all—capturing more than 12 million records on public transport each day. However, land transport IT systems were designed for quick response time, with priority given to keeping transactions moving; but not for keeping data beyond three months. Lost data meant a lost ability to conduct meaningful trend analysis, create long-term policy planning, or engage in data mining.