Session has ended. Available on-demand.
Session has ended. Available on-demand.
Session has ended. Available on-demand.
Session has ended. Available on-demand.
As one of the earliest adopters and collectors of big data, Telcos are now leading the world in addressing the challenges that come from having lots of data, but oftentimes too few insights. Some of those challenges include: ● Multiple sources of data: The number of data sources continues to grow, with multiple network elements, BSS systems, inventory management systems, business assurance systems, etc. Each has its own native tools to analyse its own piece of the business, but operators want to see the full picture and understand the relationships between the data. ● Lengthy ETL processes: Data is often already out-of-date by the time analysis takes place, which can lead to poorer decisions. Operators need real-time access to data that they can interact with at the speed of curiosity. ● Limited data science teams: Access to the data, and the ability to interact with it for gaining insight and making decisions, has been limited by the tools available. Operators need to be able to give the right tools to the right people at each step in the data science workflow. ● Growing data complexity: Technology continues advancing, from basic 2G, all the way to modern 5G, for voice, SMS and high speed data moving towards Network Slicing, with guaranteed SLAs for different service types. Operators need a system that can handle the existing complexity, and be flexible enough to leverage whatever the future holds. ● Multiple service offerings: A diverse menu of services are offered in 5G, which address new customer needs and open new business opportunities. In order to ensure these offerings are successful, operators need network planning and operations tools that offer more accurate and detailed predictive capabilities.. In this talk, Charter Communications and OmniSci will show how they are addressing these challenges for the telecom industry. We’ll demonstrate using GPUs and OmniSci’s accelerated analytics platform for fast queries and joins of multiple sources of data, interactive visualizations of spatiotemporal data, and integration with the latest AI/ML tools for running complete data science pipelines without the need for a large team of data scientists.
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