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.

Given this city’s fast-paced development and growing population, the projected demand is 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 for 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.With the expectation that data volumes would continue to grow, a decision was made to create a data management ecosystem focused on implementing new measures to better address commuter needs.

A global bank was struggling to engage with customers. Their average-basedprofitability practices weren’t improving customer relationships, maximizingcustomer value, or reducing attrition. Cross-sell and up-sell functions weren’thitting performance targets and the bank’s segmentation analyses were failingto inform pricing and customer service decisions.With no corporate confidence in profitability metrics, the bank needed a newway to reach customers and inspire them to act.

The business was lack of expertise in-house and needed help in deploying analytics in their network to generate actionable insights.Achieve continual subscriber growth, and minimize churn attributed to poor network services.Customer feedback scores mention poor network experience as the key reason for churn and dissatisfactionMarketing, Sales, and Network teams use siloed decision-making using disparate data sets.Lack of technical expertise to integrate build, and manage analytical capability on large data volume related to network experience.