Analytics: Out of the Hype Cycle

Big data and analytics has been a hot topic for some time, but the value proposition is only now emerging from the hype cycle fully formed, with a mandate for commercialization.

Jim Hodges, Chief Analyst - Cloud and Security, Heavy Reading

July 20, 2015

3 Min Read
Analytics: Out of the Hype Cycle

Big data and analytics have been a hot topic within telecom circles for the past three years. However, it's only now that the analytics value proposition is emerging from the hype cycle fully formed, with a mandate for commercialization.

When you analyze the business and technical factors that are now driving analytics, it's evident that the beneficial timing and alignment of a number of foundational technologies has a very positive impact. Let's start with the business factors.

Business-wise, operators continue to find it difficult to monetize their investment in mobile and fixed broadband networks. And there is now little doubt that to make significant gains in the monetization process, they need to be able to have a much better view of the personal profile and application consumption patterns of their subscribers in real-time. This means putting in place some techniques that provide a real-time view. Stated another way, marketing and monetizing real-time IP-based applications to subscribers requires real-time analytics capabilities because without this capacity, quality of experience (QoE) can't reflect immediate subscriber requirements.

This is applicable to not only monetization but also to delivery of proactive customer care. In my discussions with operators, many have articulated that real-time analytics has a strong value proposition when subscribers contact the help desk.

The value here is that analytics provides customer care reps a much better view of the user's recent application experience vs. the obligatory when and where questions that many reps now face. Because customer churn often happens after a less than effective customer care engagement, having a successful experience it vital. For the subscriber it ultimately means a better experience, and for the operator, lower opex given call holding times are reduced and revenue retention.

Technically, a number of technologies are also aligning, which bodes well for big data and analytics. First, the continued rollout of 4G, a pure IP technology, can be used to leverage big data and analytics to improve and expand critical network monitoring capabilities. Real-time analytics makes fraud prevention and QoS assurance much easier.

And when failures do occur, we are now seeing operators looking to be more proactive by using analytics to get the first view of network implications. The idea here is to be able to send out notifications that some service interruptions may be encountered, well before subscribers plaster social media with messages related to diminished network performance.

Other strong technology drivers for real-time analytics adoption are the implementation of NFV and SDN, as well as the advance of the Internet of Things (IoT).

Even though SDN and NFV do not specifically mandate support of real-time analytics, it's readily apparent that analytics will be a vital component of the commercialization process. When communications service providers start deploying and tearing down VNFs at the network's edge or in the cloud, they need to have a real-time view of which applications have failed and which ones have recovered, since there will be no historical network data. The same rationale applies to when security threats are encountered and VNFs must be deployed in other locations.

Because not all IoT applications will be real-time, not all will require real-time analytics. But, it's also evident that the real money will be in real-time mission-critical applications, such as medical and connected car, which realistically cannot perform without powerful analytics capabilities.

When you consider all the factors driving the implementation of big data and analytics, it's no longer a question of if, but rather when communications service providers deploy big data and analytics programs.

— Jim Hodges, Senior Analyst, Heavy Reading

This blog is sponsored by Huawei.

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About the Author(s)

Jim Hodges

Chief Analyst - Cloud and Security, Heavy Reading

Jim leads Heavy Reading's research on the impact of NFV on the control plane and application layers at the core and edge. This includes the evolution path of SIP applications, unified communications (UC), IP Multimedia Subsystem (IMS), session border controllers (SBCs), Diameter signaling controllers (DSCs), policy controllers and WebRTC. Jim is also focused on the network and subscriber impact of Big Data and Analytics. He authors Heavy Reading's NFV and SDN Market Trackers. Other areas of research coverage include Subscriber Data Management (SDM) and fixed-line TDM replacement. Jim joined Heavy Reading from Nortel Networks, where he tracked the VoIP and application server market landscape and was a key contributor to the development of Wireless Intelligent Network (WIN) standards. Additional technical experience was gained with Bell Canada, where he performed IN and SS7 network planning, numbering administration, technical model forecast creation and definition of regulatory-based interconnection models. Jim is based in Ottawa, Canada.

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