Building a data analytics platform for telecoms operators – fit for the 5G Era

Presented by

Justin van der Lande, Analysys Mason, and Paige Roberts, Vertica

About this talk

As telecommunications operators roll out 5G services across the globe, data analytics will be a critical component for their success. Delivery of 5G and IoT solutions requires near real-time and predictive insights that leverage massive volumes of data from an increasing number of connected devices to automate operational processes. Join Vertica and Justin van der Lande, Principal Telecoms Analyst for Data, AI and Development Platforms for Analysys Mason, on Wednesday, June 16 at 11AM ET to learn how some of the largest telecoms operators are preparing for the 5G era with analytics. They will also assess some of the decisions operators will face with regards to analytics moving forward. Key topics include: addressing flexibility in using multi-cloud and hybrid environments, designing for interoperability and openness, operationalizing Machine Learning, and unifying disparate data sources for BI/reporting and predictive analytics.

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The Vertica Unified Analytics Platform is built to handle the most demanding analytic use cases and is trusted by thousands of leading data-driven enterprises around the world, including Etsy, Bank of America, Uber, and more. Based on a massively scalable architecture with a broad set of analytical functions spanning event and time series, pattern matching, geospatial, and built-in machine learning capability, Vertica enables data analytics teams to easily apply these powerful functions to large and demanding analytical workloads. Vertica unites the major public clouds and on-premises data centers, as needed, and integrates data in cloud object storage and HDFS without forcing any data movement. Available as a SaaS option, or as a customer-managed system, Vertica helps teams combine growing data siloes for a more complete view of available data. Vertica features separation of compute and storage, so teams can spin up storage and compute resources as needed, then spin down afterwards to reduce costs.