Challenges in the Telecommunications Industry

Before we dive into the transformative potential and promise of real time analytics, let us start with this: 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. Shortly, we will take a look at data analytics platforms. First, let us focus on a more fundamental aspect: real time data analytics.

Real Time Data Analytics: An Overview

 Gartner defines real time data analytics as a discipline that applies logic and mathematics to data, in order provide insights that support faster, better decision-making. The engine that drives real time data analytics — and therefore transforms massive amount of raw information into reliable, relevant, and actionable business intelligence — is a data analytics platform.

What is a Data Analytics Platform?

Essentially, data analytics platforms are unified solutions that contextualize data by integrating multiple analytical tools as part of a single, centralized, and complete package. This enables organizations to analyze large volumes of complex, dynamic data, so they can capture, combine, explore, and visualize data from multiple sources.

5G Use Cases and Industry Applications in the Real World

The worldwide 5G services market is expected to reach $664.75 billion by 2028, which represents a CAGR of 46.2 percent from 2021-2028. This explosive growth is driven by rapid adoption across numerous industries. According to TechTarget, some of the most promising 5G use cases and industry applications in the real world include:

• Enhanced agricultural productivity
• Improved remote education
• Smarter logistics
• Advanced healthcare
• Improved manufacturing operations
• Modernized mining, oil and gas operations
• More personalized and efficient retail
• Smarter government management and services

While these applications of 5G are certainly noteworthy and exciting, the fact is that no industry relies more on data analytics for innovation than telecommunications. Due to enormous increases in data volumes, the rise of 5G telecommunications, and the proliferation of connected devices, only organizations that invest in a modern data infrastructure will be prepared to integrate, process, and analyze the mountains of new data.

Role of Data Analytics in the Telecom Industry

Current options for data analytics in the telecom industry to analyze 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, instead of a robust, enterprise-grade telecom data analytics solution. Those limitations prevent any significant progress against telecom network analytics challenges.

Real Time Analytics for Telecom Industry

Only the ability to to leverage a real time analytics platform in order to perform 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 of telecommunication 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 real time analytics for the telecom industry, watch this on-demand webinar.