Big Data: A Closer Look with Guavus

How can CSPs use data analytics to succeed in new verticals, like IoT?

We see many similarities in telecom and IoT. Service providers need to move up the value chain, and this can be done through data analytics. Just becoming a connectivity or backbone provider is not necessarily a profitable business.

Companies who desire to succeed in IoT need the operational intelligence to put together an ecosystem for these high value solutions, such as tracking and tracing devices, proactive monitoring, and insights into IoT service performance. In many ways, big data analytics becomes a backplane of a new generation of applications.

Guavus serves a large customer base that includes the top telcos in the world. What are you hearing from your customers? What insights are they looking for?

Broadly, CSPs are looking more and more for insight to solve specific sets of business problems such as how to prevent unauthorized tethering or how to efficiently address network planning after a new smartphone release. Organizationally, some of the boundaries or silos that traditionally existed in service provider businesses are being broken up. Network teams are collaborating with marketing teams; operations is collaborating with service assurance. As a result, CSPs are beginning to see how business problems are solved across the entire organization. As the CSPs undergo transformation, they learn that operational intelligence is something to embrace along the whole subscriber chain.

This is a perfect environment for Guavus, because the company has always looked at operational intelligence in this manner; never just measuring one thing, but drawing from multiple data sources in high variety, volume, and velocity across the organization and in new ways.

In what areas is big data having the greatest impact for CSPs today? In the future?

Beyond the broad umbrella of CEM, we are seeing a lot of interest in new areas of expansion related to additional markets, such as industrial IoT. It’s about applying the same concepts: data in motion to provide actionable intelligence. At Guavus, we can leverage the amount of work we’ve had in telecom to go beyond telecom and apply to these emerging opportunities.  

We last met with Guavus in Nice, France in 2013. What has changed at Guavus over the past two years?

A lot has happened in the past two years! The company added over 10 tier-1 carriers and we are now in five out of five of the leading North American carriers. We’ve expanded beyond initial customers with new customers in Europe, Africa, Latin America, and Asia. Guavus applications crunch a combined pool of around a trillion data records every day!

In terms of products, we’ve evolved our operational intelligence platform and segmented with specific applications: network planning, service assurance, and real-time marketing. Financially, we recently received a new cash infusion of $30 million which will help us expand into new markets like IoT.

Can you give me a couple examples of how Guavus customers are leveraging big data analytics?

Absolutely. One of our customers in Asia is very interested in ascertaining the QoS for its video service. This means understanding the quality of experience in real-time; however, in today’s virtualized world, it requires much more than just looking at a network element. This customer is using Guavus for service assurance because our solution can pinpoint and isolate problems across any network at any tier down to a specific granular network element.

In the U.S., we are implementing an end-to-end CEM system for a major mobile operator. The solution creates full-time visibility into customer quality of experience for care agents. It consolidates and correlates data from a very wide variety of sources in real-time, including SMS, core network, subscriber data, CDRs, and voice data. When a care agent receives a call, he or she gets the full picture of what the customer is experiencing in half a second or less, across more than 65 KPIs! This gives the CSP customer an incredible advantage in terms of delivering an enhanced customer experience. 

In your first example, you touched on another red-hot topic in telecom. How is network virtualization impacting the big data analytics story?

The more network functions become virtualized, the more you need decisions to happen in real-time. This decisioning must be tied to an analytics solution because the data is not tied to a physical network element anymore.