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BRUNO AZIZA: Hi, everyone.
This is Bruno Aziza, and welcome to another edition
of Data Journeys.
This is where you come to learn from leaders
in data and analytics, people that
have had amazing growth, incredible results, who tell us
about their journey, also what we need to do
and what we need to avoid doing today.
I've got the honor to be here with Joe from Telus.
Joe, thank you so much for taking the time
to talk to us today.
Tell us a little bit about Telus.
JOSEPH BETTRIDGE: Perfect.
Thanks for having me, Bruno.
So I work at Telus.
Telus is the third largest telecommunications company
in Canada.
We represent wireless/wireline systems.
We also have some interesting divisions
in health, agriculture, and IoT.
Our wireless division represents 9 and 1/2 million subscribers
across Canada.
So that's a fairly large sample of people within the country
of 36 million.
And my group specifically is Telus Insights.
Our division was founded in 2015,
and we're really focused on trying to commercialize data
in a privacy-preserving manner.
So that means taking data that we have as a company
and bringing it out to market in a way
that the privacy of the individuals or the devices
that the data comes from isn't being impacted negatively
by anything we do, and everything
is looking at aggregates.
We really see this as a market differentiator
compared to other data monetization
groups at other companies.
And we've seen this lead to a lot of the success
with our product to date.
BRUNO AZIZA: That's excellent.
So how to monetize data in a secure manner?
You also have amazing scale.
Tell us a bit about the volume of data
that you're dealing with and the types of inputs
that you're getting.
JOSEPH BETTRIDGE: Yeah, so our primary data set we focus on
is our wireless subscriber location.
So it's location coming off of our network.
We get network events any time a cell phone talks to network.
So it sends a signal when it makes phone call, text message
as it migrates between towers or any time
it's been inactive for about 30 minutes or so.
And so with that, we're building about 74 billion location
records every month, and we have about 275,000 unique points
of interest on the map that we split up
into about 5,000 different unique polygons.
And that number of polygons continues
to grow month over month as more users start using our system
and upload their own custom areas to look at.
And this is a result and that's analyzing over 1.2
petabytes of data this year.
BRUNO AZIZA: That's quite incredible.
So the products involved here are
BigQuery, of course, Dataflow, Pub/Sub, Composer,
and a whole host of other products.
You also analyze data in Data Studio.
Tell us a little bit about the first use case
here that you've been primarily involved in,
is the data for good use case.
JOSEPH BETTRIDGE: Yeah, so when the pandemic hit in mid-March
in Canada, we said as an organization,
there's something we need to do about this,
and we need to provide data out in a way that reiterates
our stance in the market towards privacy,
so really focuses on what we can and can't do with data
but also gives access to that data to government
organizations and researchers who have a real need
and the desire to use data to inform their decision-making
and help them make the right decisions at the right time.
So we opened up to this concept.
Our CEO signed off on it.
Our chief privacy officer, chief data and trust officer,
I should say, was really backing this and supporting it.
And so we've managed to, in three weeks,
roll out a new platform on top of GCP
that really allowed us to provide
these insights and information to our government customers.
We signed up 26 customers, or 27 now,
since the pandemic started and have been able to provide them
with information that can help them understand
mobility patterns, what communities are interconnected,
what health regions are interconnected, how far are
people traveling on an average day,
how well are people receiving public health messaging,
and doing all of this with large aggregate data samples
so that nothing's about individuals.
It's all about communities and how communities are
interacting with each other.
And this has resulted in some really positive effects
for various health groups across the country, which is
something we're very proud of.
And from doing this, we actually won the HPE-IAPP 2020
Innovation and Privacy Award.
So it's a very big deal for us, and we're
very excited to have won that.
It really shows that privacy differentiator
is core to our product and really core to what we do.
And it's really helped us gain traction
with these government agencies.
BRUNO AZIZA: Well, first of all, congratulations,
and then it's amazing.
Thank you for doing this for the community.
You're providing a great service to everyone else.
You have been at Telus for nine years,
and you've had a ton of experiences.
I want to kind of step back and take a look at, overall,
why did you choose Google?
And what has been the improvement
you've seen compared to maybe on-prem systems?
And then we're going to go into the dos and don'ts.
But first, why Google for you?
JOSEPH BETTRIDGE: Yeah, so before Google,
we were trying to analyze this data using an on-premise Hadoop
cluster.
What it was-- it tried to be powerful enough.
We continued to expand that cluster.
It was getting very costly to do so.
We were seeing results to understand something
like maybe a tourism region's visitorship was taking us
about a month to produce a month worth of information.
So we were kind of running at the leading edge, where
if we got one more data point, we wouldn't hit
our deadlines for customers.
So we're getting this very concerned kind
of mode of working, where it wasn't right,
and we knew there was something better out there.
So we started researching what different things were
out there, and we came across BigQuery.
And BigQuery had a lot of benefits towards GIS data
specifically.
So all of this location data, lot of the analysis
and a lot of the complex analytics
really require standard geography libraries.
And so what Google has put into BigQuery there
for standard geography was a really strong differentiator
for us.
And then the general support we saw from the Google teams--
when we engaged Google, they were
very good about providing specialists
who would help us with architecture
and would help our teams give us training, really get us up
to speed and comfortable.
And finally, the fact that BigQuery runs off of ANSI SQL
meant that a lot of the stuff we've built in the past
could be ripped out of the system it was in and placed up
into the cloud quite quickly and quite successfully.
And we saw that when we first migrated
about a year ago a large chunk of our data,
and we were able to move some of our algorithms within days
and got some very fast results, I
think we had a 25-times speed increase.
So what was taking a month was taking a day to run.
So it's a significant difference in just that caliber of result.
And with that, we're also able to really, truly use
our human capital as well as possible.
So part of what we're running is consulting,
and you ideally want to reduce the number of man hours taken,
because when I'm sitting to pay a data scientist to watch
a circle spin on their screen while Hadoop's chugging away
at a result, I'm still paying for their time,
whether or not that result's coming through.
And so BigQuery has also allowed us to do more
with a small team, and we couldn't
have done the things we've done around COVID
without the technology there.
BRUNO AZIZA: We hear this a lot from customers,
is typically big data yields to big questions,
but the ability to go through the cycle faster
enables you to ask bigger questions but also maybe better
questions and maybe more questions.
So it invites people to innovate faster
without having to worry so much about the operational aspect
of it.
Now, you have learned a ton through your experience,
and I want to know-- first, let's
start with the positive side of the equation--
as you're learning what it means to become data driven
and be modernized in the cloud, what are maybe
two or three best practices you've learned in this process?
JOSEPH BETTRIDGE: I think the number one
best practice is to keep on top of what's happening.
You guys are launching new tools and new ways
of looking at data, new pieces constantly,
and the rollout is quick.
There's new stuff happening, and keeping
on top of that can really save you a lot of time.
We found that sometimes it's faster
to come to the Google product team and say, hey,
we really want to be able to do x.
And oftentimes, you guys are already working on that
and can give us a date when it's ready.
So it's faster than us building it from scratch ourselves
and then having to replace it a few months later.
So really keeping on top of update logs,
being involved in community forums,
understanding what's happening with the product
and how it's evolving-- that's a really big
do and has saved us a lot of headaches and a lot of time.
Getting close to the Google team kind of rolls
into that, but understanding what the product team's doing,
being close to your sales agents,
understanding what they're providing
and what they can help with--
the team has been incredibly supportive.
It's been one of the better experiences we've had.
I've been in the software industry for about 10 years
now, and I would say it's second to none
in terms of the quality of service we've received.
So really, really happy with that
and I think the closer you can get with the people
you're working with from Google, the more
you can gain from that.
You guys are experts in your platforms and your tools,
and you're willing to share that expertise,
and you're very good at sharing that expertise
in a very genuine way that helps us move forward as a business.
We've been really happy with that.
And the third one's something that I think
is true for any data science team, regardless
of where you're working, but give people the opportunity
to experiment.
Things don't always work the same in BigQuery
as traditional database technologies.
Things don't always do what you want them to do.
You really want to give your people enough time
to experiment and play and do R&D work if you really
want to see the true success and drive
those really big measures.
If you don't give people time to kind of explore,
they won't flourish as much as they really can
with these technologies.
There's a bit of a paradigm shift in terms
of the thinking of how you have to look at data in BigQuery
and how you want to query it, how you want to look at it,
and giving people time to understand that, play with it,
and experiment can really lead to some astonishing results.
It can really improve how you're working with it.
As much as things are ANSI SQL, and you can rip and replace,
you can do significantly better if you
take the time to look at how to optimize
and how to play around with it.
BRUNO AZIZA: Thank you for the feedback.
We're happy that you're happy, and it's true
if you're in the community.
We have roles dedicated to your success,
from our customer engineers through our developer relations
team.
And we have people really focused
on making sure you're successful.
I'm assuming you're also reading newsletters and other forms.
But what's your favorite form?
I'm curious.
JOSEPH BETTRIDGE: So Chad from your GIS group
has a community forum on Google Chat.
He's set up a really great forum there
that has a lot of the leading people in the community
and being able to see what people are posting about,
see messaging-- really get on top of that.
And the other thing is the sales engineers.
We have a lot of meetings with them
to regularly keep up to date on what products are coming out,
what's new and exciting.
And I find both of those are the best
ways of getting information for me because they're
very knowledgeable on the product,
more so than what I've seen from most sales engineering groups.
They really know what's coming up and what it will do.
And they also have taken the time to understand our problems
and know where we're coming from.
So they know if a product a good fit for us,
and they're often very on top of saying, hey, we
have this new thing launching.
You should check it out, and they know
who to go to within our team.
So they've been really, really good to work with.
BRUNO AZIZA: That's great to hear.
And, yeah, this community you're talking about
is what we call the DACC, the Data Analytics Customer
Council, which is a meeting that we have.
We used to have this once a year,
but now we have this month, so it gives you more opportunities
to connect with the roadmap and our developer organization,
engineering organization.
And then, of course, there's a group
where you don't have to wait for the meeting.
You can interact with us directly
and the rest of the community.
That's what it means to be part of the data analytics
community at Google.
So now, that was for the positive.
Let's talk about some of the worst practices, some
of maybe the mistakes that you've learned along the way
that you would tell someone getting
started today to try to avoid them.
JOSEPH BETTRIDGE: Yeah, the number one mistake
is know what data you have duplicated where,
how things are set up in terms of retention policies.
We made a stupid mistake of one our teams had accidentally
set a default to retain data for three years after deletion.
So we thought we were deleting data,
and we're watching our bills go up and up and up
and couldn't figure it out.
We reached out.
We submit support tickets, and someone from Google
realized what was going on and corrected it for us.
But it really taught us a big lesson about cloud expenses
can go way up.
I mean, relative to the human capital costs, again,
that we were seen before on our old infrastructure,
this just pales in comparison.
Like, we really didn't pay nearly as much
for these mistakes as we were paying to have
people sit at a computer.
So it was a good lesson to learn,
but I would suggest people are careful with things
like retention policies.
Make sure you have the right data
and the right environments.
Don't over-replicate your data if you don't need to.
Use things like views to allow data to be
accessed across environments.
Use the tools that are there to be successful.
It's not traditional database management.
You have to treat it a little bit differently.
So that would be my number one one
because that has some cost associated with it.
And it rolls into my second one, which is really don't
be afraid to fail at this.
Like, things are going to go wrong.
You'll be able to correct them.
You'll be able to stand it back up.
Again, we launched a major product
in three weeks that was meeting a very serious need
in the market for data around COVID.
It's not something we could have done on our old infrastructure.
A lot of us spent some very late nights
in those three weeks getting that up and running.
It was probably closer to six to eight weeks of actual work.
But still, it's something that can
be done with this kind of tool behind you,
and you can't do that on traditional on-prem things.
Just ordering hardware alone would take you
longer than that.
So really, the power is there.
It just needs to be utilized.
And you have to be not afraid to take that first step,
and I think a lot of people are afraid to take
that first step because they might get it wrong.
And it's not a big deal, because things are moving faster.
You get something wrong, you can course
correct much easier than you could
in traditional hardware-type database development.
So the other one is don't expect this
to magically fix everything.
I think some of our team, especially
at our executive level, were expecting you go to the cloud,
and suddenly there's no more problems.
Everything's better-- brush your hands, walk away kind of thing.
And it's really not that.
This will fix some of your problems.
It will introduce some new problems.
You still need the support teams.
You still need the infrastructure people there.
Security development still needs to be there.
Their roles might shift slightly in what they're
doing versus managing services versus managing hardware,
but they're still needed, and they're still a necessary part
of those teams.
You're not going to save costs on those kind of things,
but where you will save costs are on the actual people doing
the work, and you'll be able to do
more work with less people, which
is really a bigger benefit.
But don't expect just suddenly to be one data scientist
spinning up an entire environment,
fixing everything and solving all the problems,
where you're going to need teams to support
these kind of things, and that's something
that's just necessary regardless of where you go.
BRUNO AZIZA: That's great.
You gave us a lot to think about.
I mean, there's a lot here.
It's a very rich interview.
Thanks for giving us the time today.
I have two things I think I want to highlight--
this first aspect of staying in touch with the Google team,
either through the field resources
you have for your account or the many opportunities
to forums and newsletters and groups
that we have created for you.
I really want to encourage everyone
to do that if you're part of the community today.
And then the second aspect, you touched
on financial governance.
You have to watch out for how you set up
your system, of course.
But the good news here is because you're in the cloud,
it's more than just your team watching out,
which I think is what you're talking about here, Joe, is
that we managed to pick that up and then tell you about it,
which is a great way to make sure that everybody's paying
attention too how are you utilizing and paying
for resources.
Again, thank you so much for the time today.
I hope people are going to reach out to you.
You've got a lot to share, and we
appreciate you taking the time.
I'll talk to you soon.
JOSEPH BETTRIDGE: Perfect.
Thanks so much, Bruno.
Take care.
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