Spirent Leverages Big Data to Keep User Experience
Quality a Winning Factor for Telcos
Transcript of a discussion on the use of big data to provide improved user experiences
for telecommunications operators' customers.
Listen to the podcast. Find it on iTunes. Get the mobile app. Sponsor:
Hewlett Packard Enterprise.
Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast
Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and
moderator for this ongoing discussion on IT innovation and how it’s making an impact on
people’s lives.
Our next big-data case study discussion explores the ways that Spirent
Communications advances the use of big data to provide improved user
experiences for telecommunications operators.
We'll learn how advanced analytics that draws on multiple data sources
provide Spirent’s telco customers’ rapid insights into their networks and
operations. That insight, combined with analysis of user actions and behaviors, provides
a "total picture" approach to telco services and uses that both improves the actual services
proactively -- and also boosts the ability to better support help desks.
Spirent’s insights thereby help operators in highly competitive markets reduce the spend
on support, reduce user churn, and better adhere to service-level agreements (SLAs),
while providing significant productivity gains.
HP Big Data Analytics Engines
Meet Complex Enterprise-scale OEM Requirements
Get More Information
To hear how Spirent uses big data to make major positive impacts on telco operations,
we're joined by Tom Russo, Director of Product Management and Marketing at Spirent
Communications in Matawan, New Jersey. Welcome, Tom.
Tom Russo: Hi, Dana. Thanks for having me.
Gardner: User experience quality enhancement is essential, especially when we're
talking about consumers that can easily change carriers. Controlling that experience is
more challenging for an organization like a telco. They have so many variables across
networks. So at a high-level, tell me how Spirent masters complexity using big data to
help telcos maintain the best user experience.
Gardner

Russo: Believe it or not, historically, operators haven't actually managed their customers
as much as they've managed their networks. Even within the networks, they've done this
in a fairly siloed fashion.
There would be radio performance teams that would look at whether the
different cell towers were operating properly, giving good coverage and
signal strength to the subscribers. As you might imagine, they wouldn't
talk to the core network people, who would make sure that people can get
IP addresses and properly transmit packets back and forth. They had their
own tools and systems, which were separate, yet again, from the services
people, who would look at the different applications. You can see where
it’s going.
There were also customer-care people, who had their own tools and systems that didn’t
leverage any of that network data. It was very inefficient, and not wrapped around the
customer or the customer experience.
New demands
They sort of got by with those systems when the networks weren't running too hot.
When competition wasn't too fierce, they could get away with that. But these days, with
their peers offering better quality of service, over-the-top threats, increasing complexity
on the network in terms of devices, and application services, it really doesn't work any
more.
It takes too long to troubleshoot real customer problems. They spend too much time
chasing down blind alleys in terms of solving problems that don't really affect the
customer experience, etc. They need to take a more
customer-centric approach. As you’d imagine that’s
where we come in. We integrate data across those
different silos in the context of subscribers.
We collect data across those different silos -- the radio
performance, the core network performance, the provisioning, the billing etc. -- and fuse
it together in the context of subscribers. Then, we help the operator identify proactively
where that customer experience is suffering, what we call hotspots, so that they can act
before the customers call and complain, which is expensive from a customer-care
perspective and before they churn, which is very expensive in terms of customer
replacement. It's a more customer-centric approach to managing the network.
Automate Data Collection and Analysis
In Support of Business Objectives
With Spirent InTouch Analytics
Russo

Gardner: So your customer experience management does what your customers had a
difficult time doing internally. But one aspect of this is pulling together disparate data
from different sources, so that you can get the proactive inference and insights. What did
you do better around data acquisition?
Russo: The first key step is being able to integrate with a variety of these different
systems. Each of the groups had their different tools, different data formats, different
vendors.
Our solution has a very strong what we call extract, transform, load (ETL), or data
mediation capability, to pull all these different data sources together, map them into a
uniform model of the telecom network and the subscriber experience.
This allows us to see the connections between the subscriber experience, the underlying
network performance and even things like outcomes -- whether people churn, whether
they provide negative survey responses, whether they've called and complained to
customer care, etc.
Then, with that holistic model, we can build high-level metrics like quality of experience
scores, predictive models, etc. to look across those different silos, help the operators see
where the hot spots of customer dissatisfaction is, where people are going to eventually
churn, or where other costs are going to be incurred.
Gardner: Before we go more deeply into this data issue, tell me a bit more about Spirent.
Is the customer experience division the only part? Tell me about the larger company, just
so we have a sense of the breadth and depths of what you offer.
World leader
Russo: Most people, at least in telecom, know Spirent as a lab vendor. Spirent is one of
the world leaders in the markets for simulating, emulating, and testing devices, network
elements, applications, and services, as they go from the development phase to the launch
phase in their lifecycle. Most of their products focus on that, the lab testing or the launch
testing, making sure that devices are, as we call it, "fit for launch."
Spirent has historically had less of a presence in the live network domain. In the last year
or two, they’ve made a number of strategic acquisitions in that space. They’ve made a
number of internal investments to leverage the capabilities and knowledge base that they
have from the lab side into the live network.
One of those investments, for example, was an acquisition back in early 2014 of DAX
Technologies, a leading customer experience management vendor. That acquisition, plus

some additional internal investments has led to the growth of our Customer Experience
Management (CEM) Business Unit.
Gardner: Tom, tell me some typical use cases where your customers are using Spirent in
the field. Who are those that are interacting with the software? What is it that they're
doing with it? What are some of the typical ways in which it’s bringing value there?
Russo: Basically, we have two user bases that leverage our analytics. One is the
customer-care groups. What they're trying to do is obtain, very quickly, a 360-degree
view of the experience that a subscriber is seeing -- who is calling in and complaining
about their service and the root causes of problems that they might be having with their
services.
HP Big Data Analytics Engines
Meet Complex Enterprise-scale OEM Requirements
Get More Information
If you think about the historic operation, this was a very time-intensive, costly operation,
because they would have to swivel chair, as we call it, between a variety of different
systems and tools trying to figure out whether I had a network-related issue, a
provisioning issue, a billing issue, or something else. These all could potentially take
hours, even hundreds of hours, to resolve.
With our system, the customer-care groups have one single pane of glass, one screen, to
see all aspects of the customer experience to very quickly identify the root causes of
issues that they are having and resolve them. So it keeps customers happier and reduces
the cost of the customer-care operation.
The second group that we serve is on the engineering side. We're trying to help them
identify hotspots of customer dissatisfaction on the network, whether that be in terms of
devices, applications, services, or network elements so that they can prioritize their
resources around those hotspots, as opposed to noisy, traditional engineering alarms. The
idea here is that this allows them to have maximal impact on the customer experience
with minimal costs and minimal resources.
Gardner: You recently rolled out some new and interesting services and solutions. Tell
us a little but about that.
Russo: We’ve rolled out the latest iteration of our InTouch solution, our flagship product.
It’s called InTouch Customer and Network Analytics (CNA) and it really addresses
feedback that we've received from customers in terms of what they want in an analytic
solution.

We're hearing that they want to be more proactive and predictive. Don’t just tell me
what's going on right now, what’s gone on historically, how things have trended, but help
me understand what’s going to happen moving forward, where our customer is going to
complain. Where is the network going to experience performance problems in the future.
That's an increasing area of focus for us and something that we've embedded to a great
degree in the InTouch CNA product.
More flexibility
Another thing that they've told us is that they want to have more flexibility and control
on the visualization and reporting side. Don't just give me a stock set of dashboards and
reports and have me rely on you to modify those over time. I have my own data
scientists, my own engineers, who want to explore the data themselves.
We've embedded Tableau business intelligence (BI) technology into our product to give
them maximum flexibility in terms of report authorship and publication. We really like
the combination of Tableau and Hewlett Packard Enterprise (HPE) Vertica because it
allows them to be able to do those ad-hoc reports and then also get good performance
through the Vertica database.
And another thing that we are doing more and more is what we call Closed Loop
Analytics. It's not just identifying an issue or a customer problem on the network, but it's
also being able to trigger an action. We have an integration and partnership with another
business unit in Spirent called Mobilethink that can change device settings for example.
If we see a device is mis-provisioned, we can send alert to Mobilethink, and they can re-
provision the device to correct something like a mis-provisioned access point name
(APN) and resolve the problem. Then, we can use our system to confirm indeed that the
fix was made and that the experience has improved.
Gardner: It’s clear to me, Tom, how we can get great benefits from doing this properly
and how the value escalates the more data and the more information you get, and the
better you can serve those customers. Let's drill down a bit into how you can make this
happen. As far as data goes, are we talking about 10 different data types, 50? Given the
stream and the amount of data that comes off of a network, what size data we are talking
about and how do you get a handle on that?
Russo: In our largest deployment, we're talking about a couple of dozen different data
sources and a total volume of data on the order of 50 to 100 billion transactions a day. So,
it’s large volume, especially on the transactional side, and high variety. In terms of what
we're talking about, it’s a lot of machine data. As I mentioned before, there is the radio
performance, core network performance, and service performance type of information.
We also look at things like whether you're provisioning correctly for the services that
you're trying to interact with. We look at your trouble ticket history to try and correlate

things like network performance and customer care activity. We will look at survey data,
net promoter score (NPS) type information, billing churn, and related information.
We're trying to tie it all together, everything from the subscriber transactions and
experience to the underlying network performance, again to the outcome type
information -- what was the impact of the experience on your behavior?
Gardner: What specifically is your history with HPE Vertica? Has this been something
that's been in place for some time? Did you switch to it from something else? How did
that work out?
Finishing migration
Russo: Right now, we're finishing the migration to HP Vertica technology, and it will
be embedded in our InTouch CNA solution. There are a couple of things that we like
about Vertica. One is the price-performance aspects. The columnar lookups, the
projections, give us very strong query response performance, but it's also able to run on
commodity hardware, which gives us price advantage that's also bolstered by the
columnar compression.
So price performance-wise and maturity-wise we like it. It’s a field-proven, tested
solution. There are some other features in terms of strong Hadoop integration that we
like. A lot of carriers will have their own Hadoop clusters, data oceans, etc. that they
want us to integrate with. Vertica makes that fairly straightforward, and we like a lot of
the embedded analytics as well, the Distributed R capability for predictive analytics and
things along those lines.
Gardner: It occurs to me that the effort that you put into this at Spirent and being able to
take vast amounts of data across a complex network and then come out with these
analytic benefits could be extended to any number of environments. Is there a parallel
between what you are doing with mobile and telco carriers that could extend to maybe
networks that are managing the Internet of Things (IoT) types of devices?
Russo: Absolutely. We're working with carriers on IoT already. The requirements that
these things have in terms of the performance that they need to operate properly are
different than that of human beings, but nevertheless, the underlying transactions that
have to take place, the ability to get a radio connection and set up an IP address and
communicate data back and forth to one another and do it in a robust reliable way, is still
critical.
We definitely see our solution helping operators who are trying to be IoT platform
providers to ensure the performances of those IoT services and the SLAs that they have
for them. We also see a potential use for our technology going a step further into the
vertical IoT applications themselves in doing, for example, predictive analytics on sensor
data itself. That could be a future direction for us.

Gardner: Any words of wisdom for folks that are starting to do with large data volumes
across wide variety of sources and are looking also for that more real-time analytics
benefit? Any lessons learned that you could share from where Spirent has been and gone
for others that are going to be facing some of these same big data issues?
Automate Data Collection and Analysis
In Support of Business Objectives
With Spirent InTouch Analytics
Russo: It's important to focus on the end-user value and the use cases as opposed to the
technology. So, we never really focus on getting data for the sake of getting data. We
focus more on what problem a customer is trying to accomplish and how we can most
simply and elegantly solve it. That steered us clear from jumping on the latest and
greatest technology bandwagons, instead going with the proven technologies and
leveraging our subject-matter expertise.
Gardner: I'm afraid we'll have to leave it there. We've been exploring the ways that
Spirent Communications advances the use of big data to provide improved user
experiences for their telecommunications operator’s customers. We've identified some
advanced analytics and how they're drawing on more data sources and providing their
telco customers more rapid insights into their networks and operations.
So join me in thanking Tom Russo, Director of Product Management and Marketing at
Spirent Communications in Matawan, New Jersey. Thanks so much.
Russo: Thanks very much, Dana. Thanks for having me.
Gardner: And a big thank you to our audience as well for joining us for this big data
information innovation case study discussion.
I'm Dana Gardner; Principal Analyst at Interarbor Solutions, your host for this ongoing
series of HPE-sponsored discussions. Thanks again for listening, and come back next
time.
Listen to the podcast. Find it on iTunes. Get the mobile app. Sponsor:
Hewlett Packard Enterprise.
Transcript of a discussion on the use of big data to provide improved user experiences
for telecommunications operators' customers. Copyright Interarbor Solutions, LLC,
2005-2015. All rights reserved.

You may also be interested in:
• Powerful reporting from YP's data warehouse helps SMBs deliver the best ad
campaigns
• IoT brings on development demands that DevOps manages best, say experts
• Big data generates new insights into what’s happening in the world's tropical
ecosystems
• DevOps and security, a match made in heaven
• How Sprint employs orchestration and automation to bring IT into DevOps
readiness
• How fast analytics changes the game and expands the market for big data value
• How HTC centralizes storage management to gain visibility and IT disaster
avoidance
• Big data, risk, and predictive analysis drive use of cloud-based ITSM, says panel
• Rolta AdvizeX experts on hastening big data analytics in healthcare and retail
• The future of business intelligence as a service with GoodData and HP Vertica
• Enterprises opting for converged infrastructure as stepping stone to hybrid cloud
• Redcentric orchestrates networks-intensive merger using advanced configuration
management database
• HP pursues big data opportunity with updated products, services, developer
program