5G makes real-time analytics the new standard in telecommunications

In the telecommunications business, every consumer and business is a potential consumer. That creates a huge universe for monetization opportunities. Even so, telecom companies are under pressure like never before in several ways:

• Competition: The market is consolidating and fewer telecoms are going head-to-head for the same business.
• Subscriber growth: At the same time, subscriber growth has plateaued, limiting potential revenue growth along with most customers expecting an “all-you-can-eat” pricing model.
• Network build-out: 5G will require one of the biggest network build-outs in a decade, requiring a substantial investment while growth is flat and prices are being driven down.
• Customer churn: Telecoms have to invest more in improving customer experience, from the network to the website to the call center, to retain more customers, longer.
• IoT: The data exhaust produced by edge devices is growing exponentially (with direct impact from 5G), and requires specialized analytics infrastructure for ingestion and processing of streaming data.

With those challenges, telecom companies have a critical need for faster, more efficient data analytics platforms that can provide insight in-the-moment across all data, especially new 5G-generated data, without massive new IT investments.

But current options for analyzing vast, complex data like that produced by 5G networks are too limited: Legacy approaches like Teradata and Oracle are inflexible and expensive to buy and scale. Cloud-only options like Snowflake have poor and unpredictable price/performance beyond a few terabytes or tens of users, and are unsuitable as edge systems for latency reasons. And data lakes have proven to be useful only as low-cost data stores. Those limitations prevent any significant progress against telecom challenges.

Only the ability to do real-time analytics at telecom scale can help reach goals like analyzing billions of data points to optimize networks, predict equipment/IoT device failure, and allocate capacity more accurately; get richer customer views that spans years of history to minimize and improve customer lifetime value; and save millions be improving the speed, accuracy, and granularity of billing assurance. That ability requires best price/performance for network and application analytics available, along with operational simplicity and flexibility to support any deployment choice, whether in a data center/private cloud, in multiple public clouds, at the 5G edge, or all of the above.

To learn more about some of these trends and their impact on analytics requirements in the Telecom industry, watch this on-demand webinar ("Modern Data Analytics in Telecom").