[MUSIC PLAYING]
MAX KAMENETSKY: Hi, and welcome to the Google Cloud and Nokia
series on the cloudification of telecom provider networks.
Why now and how?
I'm Max Kamenetsky, Group Product Manager
of Cloud Networking at Google Cloud.
JITIN BHANDARI: I'm Jitin Bhandari,
Chief Technology Officer for Cloud
and Network Services at Nokia.
The move to cloud networks presents a unique opportunity
for telco operators to transform the network, the operations,
and the ultimate experience that the 5G value and services will
bring to their networks.
MAX KAMENETSKY: That's right, Jitin.
In fact, I recently chatted with Lester Thomas
at Vodafone Group, about how they're using automation
to drive operational efficiencies into the network.
Today, I'm very pleased to be speaking with Dr. Lester
Thomas, who is Head of New Technology and Innovation
at Vodafone Group.
Lester, it's wonderful to be able to speak to you today.
I would love to hear more about your perspective
as an operator.
And specifically, maybe we can talk a little bit
about analytics.
Vodafone obviously has a very storied history,
and you're not new to analytics.
Can you tell me a little bit about what
you've done in the past, things that worked,
and maybe perhaps some things that were challenges?
LESTER THOMAS: Yeah, thanks.
So prior to us deciding in 2019 to adopt the Google Cloud
platform for analytics, we had invested very heavily
in our own big data platforms.
And what we found, we were sinking too much of our time
and money in addressing the underlying tooling,
and we weren't able to actually develop enough of our own use
cases, devote the time and energy, devote use cases on top
was where the real value is for us.
And because our tooling wasn't really scalable enough,
too many markets were still using legacy analytics
and business intelligence platforms
to drive their business insight.
So despite all of our investment,
we didn't have enough capability or capacity
in our tooling to meet the ambitions
of the different teams of actually driving analytics.
MAX KAMENETSKY: Yeah, that's very interesting.
So it sounds like you were facing a problem where
you had a lot of challenging data
sources in a lot of different geographies,
and somehow you needed to simplify that, to unify that.
How did you go about solving that?
LESTER THOMAS: If you're going to start an analytics journey,
getting the data governance right is a critical part.
We have all these different use cases
that we use the data to drive business insights and value.
The underlying data for it is actually in a common database.
It's all in common data models.
So as we do each use case, we will bring in new data
into our data ocean, but there'll
be standardized and normalized.
There's a lot of work on things like the data quality.
So if you just bring data in, you
have to actually have the right ownership, and people
who actually care about the quality of that data.
You need to have the right lifecycle management.
So we have very strong standards around,
how do you have a consistent management
of high-risk or high-value data?
And how do you manage a full lifecycle from creation
to eventually deleting that data?
There's a whole lot of work around the interoperability
and exchange.
We are bringing data in from all our European markets
into this Google Cloud platform, so there's
a whole lot of work on data interoperability and exchange.
And finally, there's more business standards
around our own data sharing.
So what data can be shared and for what purposes?
So we're very careful with the privacy
that we apply to the data.
And so every piece of data has an owner,
and it also has a definition of what you can use it for
and what levels of customer permission
you use it for different purposes.
MAX KAMENETSKY: That's very interesting to hear.
So it sounds like very much you're
describing a data platform, because you talked
about methods for data intake, for data regularization,
for data ownership, for consolidation,
for defining how you can use this data.
So to me, this sounds quite a bit
like a story of platforms and applications, maybe
similar to what Google's Android story was.
Certainly, you're building this because you're
thinking of certain use cases.
Can you tell me a little bit more
about how you're thinking of using this data?
LESTER THOMAS: You know, our networks--
on network operations today, they
receive thousands of events and alarms off the network.
There's lots of use cases around how you intelligently
process and analyze that data.
There's use cases around network planning.
So when we're investing in new networks,
how do you plan to spend your money
to make the biggest impact to your customers of those network
investments?
Think about more your customer system.
So customers now, they tend to interact with us
through the text messaging and chat bots.
And so we do a lot of work around, can we automate that?
So the first contact in Vodafone is typically
a chat bot we call Toby.
It's fully automated, and maybe you're asking a standard query.
You'll get an automated response.
If you ask a more complex query, at least
you'll be passed on to the person who
is in the area for your query.
And the other thing we do is a lot of that data,
you then analyze because actually our target
is for the customer not to have called us in the first place.
Like, can you get root cause analysis
of the underlying problem that caused that customer to call?
Like, our target is, how many contacts we
have per customer per month.
And then when you think about the 5G and IoT use cases,
often, those use cases themselves are
data and analytics use cases.
So typically, our enterprise customers
who are buying these IoT services-- they
don't actually want connectivity or managing devices.
They want the data.
In fact, if we can get that data for them
and process it and give them a digital twin or even
do data and analytics, and the service they consume from us
is actually an analytics service.
So I think there's lots of use cases
in the actual end-user scenario of the actual value
we add clearly through these 5G and IoT solutions.
MAX KAMENETSKY: Lester, do you believe
it's possible in some of these scenarios
that you're talking about to utilize AI/ML to do
more intelligent detection?
So as an example, do you think it's possible at some point
to be able to first understand when there
is a problem somewhere in the network based on the data
that you're receiving and perhaps some training data
that you've had in the past?
Do you believe it's possible to have a model that
will be able to then tell you what it thinks the root
cause for the problem is and perhaps even to then remediate
that problem?
LESTER THOMAS: Yeah, absolutely.
Like, the anomaly detection use case that we've done so far
is only really about how do you process this data
and analyze the root cause of what's going on in the network.
But we absolutely see that as almost
like the very first part of what we call an autonomous network.
And we actually use in our plans--
we have plans which are following this autonomous car
model of having five levels of autonomy.
And we think that we can go from having manual operation
maintenance, assisted operation, like a partial autonomous
network.
Maybe in the individual domains of our network,
there's a level of closed-loop feedback
to automatically fix certain common problems.
And then you look at there might be a higher level.
You might even be adjusting the topology or network
based on this knowledge.
So we think there are far more use cases we can build on top.
And what we're doing now is only really the start.
MAX KAMENETSKY: This is fascinating.
It sounds like you're embarking on a really, really
interesting journey.
So what are some of the lessons that you've
learned so far in this process?
LESTER THOMAS: I think the one I covered about data governance--
I think that's absolutely critical,
and we've learned that from our experience
before moving to Google Cloud.
I think the thing about the Google Cloud platform itself--
so what we found is it is far more scalable.
Like, you do get this global-scale capability.
We've had to be much more controlling because there's
a risk that we actually spend all our OpEx investment
because almost there is no limit on what you can process.
So more work we go on about, how do we optimize the--
driving these use cases to actually manage the investment
we have on it?
The other thing I say is, like, at least
to your comment around Android, it is a platform.
And one of the things we really like about the Google platform
is we're investing all this money on these applications
and use cases, and it's all based on open standards.
So we feel things like Kubernetes is a standard.
So we feel like we're investing in something,
and there's a huge community and support around it.
And we're not going down a very proprietary route.
So almost like it was a very strategic investment for us,
so that's really critical.
MAX KAMENETSKY: Yeah, yeah, yeah.
Lester, it was fascinating talking to you about this.
I love learning about the fact how now that you've established
this platform, you are free from having
to do all this manual labor to regularize the data,
to curate the data, that now you really have the opportunity
to focus on the use cases, on the business
outcomes of this, which is really what
you want to get out of this.
Thank you again for taking the time to speak with me today.
LESTER THOMAS: Thank you.
JITIN BHANDARI: That was a very interesting conversation.
What's your takeaway, Max?
MAX KAMENETSKY: You know, Jitin, it was great for me
to hear how Dr. Thomas and Vodafone first
focused on establishing the platform,
standardizing the intake, regularizing the data,
ensuring that their owners for all of the key pieces,
such that with this platform they
could then focus on the insights that they could glean from it.
JITIN BHANDARI: Thanks for catching our discussion
on cloudification of networks.
We'll see you next time in the Cloud.
[MUSIC PLAYING]