Big Data: A Closer Look with Guavus

By: Jesse Cryderman

The first hard drive my family owned had less memory capacity than my latest smartphone, cost more, and used much more power. Today, hard drives are huge, cheap and plentiful, but fundamentally the financial data I stored in 1990 is just as valuable as the data I just backed up on my Google Drive. Similarly, big data is constantly in the news; but what’s more important than its “bigness” is how the data is used to solve real business problems in real-time. This is particularly true for communications service providers (CSPs) who have been riding the wave as the big data buzz has turned from storage and capture to real-time operational intelligence.

Pipeline recently had the opportunity to have a conversation with Guavus, a big data analytics company that has been working on the front lines with communications service providers (CSPs) for many years and is helping its global customers transform into data-driven enterprises. Guavus has developed an operational intelligence platform comprised of big data analytics applications for planning, operations, and marketing, and counts five out of five of the leading North American carriers as its customers.

We met with Gabriele Di Piazza, senior vice president, marketing and products at Guavus, to discuss how CSPs are transforming and leveraging big data analytics applications, how Guavus is helping them along their journey, and how big data intersects with other hot trends like network virtualization and IoT.

Big data, as a trend, has been with us for several years. How has the story evolved for communications service providers?

Big data has been a trend for a while. CSPs have been looking at and acquiring big data solutions for quite some time. The evolution is now they are really moving to real-time, specific applications that solve business problems; moving from acquiring general-purpose data from offline data sets to a more focused approach that enables them to solve a business problem right now.

CSPs have always stored lots of data such as call records, network performance data, and customer data; but today they also have access to many more data streams. What are some examples of valuable data streams that CSPs can tap into, and how can they be integrated into a big data solution?

There are two items to consider. First, in terms of new data streams, there is much more customer information data out there now. This data can be sourced through the network or through social media, web-interactions, and other unstructured data sources. This multi-channel interaction data was typically never correlated by CSPs in the past.

Secondly, it’s exploiting the data that already exists to its fullest. In reality, many of these CSPs have been looking at CDR data, but never been able to actually exploit the data they had. For example, there is an explosion of interest in understanding subscribers and creating more personalized customer experiences, and a lot of this data is actually hidden inside the data and consumption patterns that CSPs already possess in various data sets.

We see the emergence of “Data scientists.” What role do data scientists play in the big data analytics story, and how important are they?

They play a very big role; and, in fact, you can see the role emerging in sales and customer conversations. We are really moving ahead past traditional reporting to finding complex insights, and a data scientist is someone who can find patterns in your data across multiple domains (network planning, service assurance, marketing). At Guavus, we have a team of data scientists who work with our customers both pre- and through post-sales to continually look at data patterns and identify areas for optimization and new use cases.

What are some new ways in which CSPs can leverage data analytics?

There are many ways, easily 20 to 30 applications, but we see the emergence of customer experience management (CEM) as the overarching topic for CSPs. This includes the fact that they need to understand Quality of Service (QoS) and customer satisfaction in real time to do things like predict what the customer would want next, predict network performance, or to take preventative action.

All of these topics, whether deep in the network all the way up to marketing strategies fundamentally accrue to customer experience. Customer experience today is a major area of differentiation for CSPs as they continue to face new, competitive threats. Big data, real-time data analytics is driving these innovations.


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