As an example, mobile network operators are increasing their investment in big data analytics and machine learning technologies as they transform into digital application developers and cognitive service providers. With a long history of handling huge datasets, and with their path now led by the IT ecosystem, mobile operators will devote more than $50 billion to big data analytics and machine learning technologies through 2021, according to the latest global market study by ABI Research. Machine learning can deliver benefits across telecom provider operations with financially-oriented applications - including fraud mitigation and revenue assurance - which currently make the most compelling use cases. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization.
In rail, and specifically when it comes to rolling stock maintenance, big data is synonymous with Condition Based Maintenance (CBM) and Predictive Maintenance (PM). In terms of operational intelligence, some of the relevant AI techniques to address problems like fleet monitoring and asset maintenance in the Rail industry include Knowledge Based Systems, Case Based Reasoning, Genetic Algorithms, Neural Networks and Fuzzy Logic etc. They eliminate the need for lengthy root cause identification and arrive at the required repair action more quickly, leading to faster repair, reduced maintenance cost and increased fleet availability and customer satisfaction. When it comes to asset intelligence, the continuous data streams produced from various sub-systems in trains help OEMs build digital twins that represent physical systems in real-time.