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Big Data

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.

DHagar 7/21/2015 | 5:00:38 PM
Re: Big Data Analytics is out of hype mendyk, that is very true.  If all we do is feed the data to data scientists, rather than integrate it into business decision making, we won't make real progress.

The value has to come in providing meaning information that enables the business user to make good decisions that make a difference.  When that serves the operational units, the value will be seen.
mendyk 7/21/2015 | 8:48:55 AM
Re: Big Data Analytics is out of Hype Another big issue with analytics is the likelihood that initiatives will end up being driven by quant people rather than business leaders. That seems to be happening a lot. The end result is lots of esoteric findings that have little benefit in the way of process improvement.
[email protected] 7/21/2015 | 1:15:58 AM
Big Data Analytics is out of Hype To some extent, I agree with what is being said in the article. However, Big data is still a Hype as the boundaries for Data collection is still not drawn. It is important to create a customer experience archteture and roadmap to achieve those KPIs. This will bring Analytics to the fore front of operator's decision making and real time responsiveness. The data volume is so huge while adopting big data, the ROI has to be aligned with Customer experience architecture and roadmap.
jabailo 7/20/2015 | 1:47:44 PM
We have the eyes; we need the brain Analytics seems to stall out after the initial "easy" steps of data warehousing and setting up tools like pivot tables and virtualization spaces are completed.

To complete the cycle there has to be provable ROI.    A human, sitting at a workbench probably isn't going to be able to do that on a regular basis.  For that you probably need some machine learning tools to evaluate and even make decisions based on the data.

 
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