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Telecom networks generate voluminous fault, configuration, accounting, performance, and security (FCAPS) data. Communication Service Providers (CSPs) are looking at ways to use this data efficiently to improve the network health, as well as reduce Mean Time to Detect (MTTD), and Mean Time to Resolve (MTTR). They want to increase the service uptime, reduce spectrum interference, reduce network capacity forecast, and eliminate drop calls. This business’ desired outcome can be fulfilled by leveraging machine learning/artificial intelligence (ML/AI).
Telecom network operation engineers are not data citizens in general. Although they understand the call flows, KPIs, and metrics, they may need data science skills and require spending time to build highly complex ML models. In this post, we explain how CSPs can leverage the built-in capabilities of Amazon QuickSight to create intelligent insights for their network performance. The dataset we are using for this blog is a CSV file that has the Voice over Long Term Evolution (VoLTE) drop calls count and percentage by zone, location, and tower. The dataset shows also the drop call counts by VoLTE network function such as Evolved Node B (eNB) and Mobility Management Entity (MME). Here’s a screenshot of the dataset: