Can You Hear Me Now? Network Optimization at Work

Posted May 1, 2019 by Jeff Healey, Sr Director, Vertica Product Marketing at Micro Focus

Chicago cityscape with various data type icons and network connection concept

For sports fans, there’s nothing that matches the heart-stopping action of the NCAA Division I Men’s Basketball Tournament – more commonly known as March Madness. This year’s tournament delivered a four-year high in terms of its Turner ratings, causing an advertiser frenzy in hopes to capture those audiences. If you watched this year’s tournament, you couldn’t miss the AT&T commercials, which were even parodied as part of actual halftime analysis. AT&T’s advertising message is “Just OK is not OK” when it comes to your wireless network.

How can a telecom company live up to a slogan like that? How do companies in the telecommunications market ensure that their networks are optimized? And how do analytics and machine learning help in that effort?

Network Optimization Services is Big Business, Backed by Big Data

According to Markets and Markets, the network optimization services market size is expected to grow from USD 4.78 billion in 2017 to USD 9.08 billion by 2022, at a CAGR of 13.7%. This growth is being driven by an increasing number of branch offices and data centers as well as Wide Area Network (WAN) optimization and local network optimization.

Also, increasing data traffic over networks owing to high penetration of smartphones across the globe is a major factor expected to drive growth of the global mobile network optimization market. Rising use of software utilities for better customer experience and enhanced network optimizations is another factor.

Perhaps most importantly, 5G technology with its lightning-fast speeds and ultra-low latency, promises to enable entirely new user experiences and a more intelligent kind of connectivity. Telco organizations are gearing up their analytics and machine learning initiatives to seize the opportunity.

Capitalizing on the Network Optimization Opportunity with Vertica

Read on to learn about some real examples of how data-driven organizations are employing Vertica analytics and machine learning in their network optimization to deliver higher levels of customer satisfaction.

Anritsu Transforms Its Service Assurance Business Model and Achieves a 351% ROI

Based in Japan, Anritsu is one of the largest international manufacturers of radio measurement instruments. The company’s Service Assurance business unit develops advanced analytics solutions for telecom operators in the areas of network performance monitoring and customer experience management. Traditional service assurance solutions only provide basic near real-time monitoring of the network to alert service providers when malfunctions are detected, but with limited insights on possible causes.

With Vertica, Anritsu has extended its focus on sophisticated analytics to automate data correlations and causal investigations, thanks to the ability to analyze a greater depth and variety of data. This is integrated with an advanced BI tool that can drill-down into network data in near real-time, and a real-time machine learning based extension for customer network analyses.

As a result, Anritsu has increased clients’ employee productivity, reduced operating costs for clients, expanded customer base with new types of customers, and more. And, Anritsu recorded a return of 351% on their Vertica investment, with a payback time of only 4 months, and a cost-benefit ratio of about 1:43.

Maxcom Telecommunications Improves Regulatory Compliance and Slashes Fraud-Related Costs by 85%

Maxcom Telecommunications, a leading Mexican integrated telecommunications operator, faced a challenge when the Federal Law for Telecommunications in Mexico increased the retention period for call detail records (CDR) from two months to two years. With 1,500 enterprise and 100,000 residential customers generating 25 million calls each day, that posed a big data problem for Maxcom. Moreover, the time to answer queries was now shortened to within an hour, so the company’s Oracle database put them at risk of non-compliance.

Within just three weeks, Vertica was operational and the Maxcom team started reaping the benefits. The regulatory requirements with faster query time and longer CDR retention were quickly met, once the relevant queries were defined in Vertica.

As a result, Maxcom increased query performance by 60%, reduced fraud-related costs by 85%, and now maintains full compliance with Federal Telecommunications Law.

SysMech Gives Telcos Operational Intelligence to Optimize Their Networks

SysMech, based in the United Kingdom, offers telco operators a new generation of service management applications to optimize their networks. Most of SysMech’s competitors have single-domain applications that they try to expand through analytics, but they fail to extend coverage in a highly scaled heterogeneous network. SysMech applications merge all network data into one platform to enable major telco operators to correlate data from any part of their network for quick analysis.

By embedding Vertica into SysMech’s Zen Network Management Software, the company enables its telco customers to correlate massive complex data from 75 various sources and relay information to hundreds of users. Previously, a team of 80 engineers took two hours each to aggregate data from various areas of the network to conduct their analyses and run reports at the start of the day. Now, the application assimilates all of the data and provides visibility across the network before the engineering team arrives, saving up to two hours per engineer per day.

Ahead, SysMech is looking to move network management to a self-analyzing, self-actioning AI stage to fully optimize telco operator networks, and deliver business value from underlying big data.



For more in-depth information, read the white paper: Advanced Analytics for Intelligent Connectivity: Machine Learning as a Growth Engine in the Age of 5G



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Related Resources and Pages:
Web page: Communication and Network Analytics
Web page: Internet of Things (IOT) Analytics
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Web page: Machine Learning
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