STEPHANIE WONG: So I think maybe we
can start by giving the audience some context about Quantum
Metric.
Can you tell us a little bit about how
it got started and your push to the cloud?
MARIO CIABARRA: Yeah, absolutely.
So we started in 2015.
And really, the idea--
I started my career in retail, in the early '90s, I think.
It's been a while.
And there's this magic that happens
when you walk into a store and you
are seeking to purchase some specific good, clothing
or jewelry.
I started my career out in the retail clothing industry.
You'd be buying an airline ticket, engaging with a bank,
and there's this special touch that we've
perfected over millennia that, when
I had that physical engagement with the person,
I can understand when they have a need, when
they are looking for help from an associate, or et cetera.
And as we have progressed on to digital, how do we
deliver these incredible unique experiences,
these touches that we have in our physical engagement?
How do we do that well online?
And so that was the challenge that we set out to do.
And you know, there's a picture here
of what it looked like in 2015.
And maybe some of you that are in startups can relate to this.
This is, you know, two guys in a room with a Kanban board,
just chugging away at code and design.
So it was a really fun start back in 2015.
We've gone a long way since then.
And so we've had some incredible success.
So, just to kind of catch up and just in the last year,
we've had 400% growth year over year.
We've been fortunate enough to take $26 million of funding.
And we have 80-plus experts around the world
to help our customers engage in delivering
their best on digital.
STEPHANIE WONG: And that was your series A funding?
That seems like a big number.
MARIO CIABARRA: It was.
STEPHANIE WONG: Can you tell us about that a bit?
MARIO CIABARRA: Yeah.
You know, we've had so much success at enterprises,
engaging with some of the best brands--
United Airlines, Alaska, eBay, Western Union, StubHub,
Ticketmaster, Bed, Bath.
We've had such an incredible engagement
of how we empower those large enterprises at delivering
their best on digital.
And when you get that kind of success with the largest
of brands-- and even being a small company--
there's some excitement.
And so with that, we were fortunate enough
to have-- one of the best venture firms out there
of private equity is Insight Venture Partners.
So, really blessed to have so much success at an early stage.
STEPHANIE WONG: Great.
And is that your team in a cube formation?
How long did it take you to do that?
MARIO CIABARRA: Yeah, so we like to have a lot of fun
at Quantum.
And so this one is just one of our team get-togethers.
And we all-- we threw up a drone.
And it was quite fun.
So I think one of the great things about startups--
and you know, really, anyone's workplace--
it's got to have a lot of fun.
So we do that here at Quantum.
STEPHANIE WONG: Yeah.
So I see you had two people on that 2015 photo.
And now you've grown.
And talk about your push to the cloud
and where you started compared to now.
MARIO CIABARRA: Yeah, so I think we all have our strengths
and weaknesses.
I know my weakness is not-- it's not about,
I don't design infrastructure.
I don't design data centers.
And so I'm embarrassed to show this picture.
But literally, this is just last week our,
quote, "data center" on prem, in our Colorado Springs office.
And the funny story about it recently
is that there was a bombogenesis.
There was a big storm in Colorado Springs.
And we literally had to get someone
to drive a few miles in a crazy storm
to hit a power switch somewhere in this closet
for our development tool.
So as we all worked from home, we got the support we needed.
So this is how I know that we weren't meant
to have an on-prem solution.
But there are some other reasons, too.
We have-- before I jump into some of the other reasons,
I guess our journey really began with the same journey
that most of the folks that have onboarded themselves
onto Google Cloud have engaged with.
And it was really about, we knew as a data analytics company,
we were going to have massive amounts of scale coming in.
And how would we be able to elastically scale?
We didn't have, at the time, tens of millions
of dollars of funding to go build a crazy data center
to handle that level of scale.
And at the end of the day, how do we
get folks that are in the financial services
industries, that are massive retailers-- how
do we get them comfortable with deploying onto our platform
when we talk about security and privacy and GDPR
and really great challenges that we face in technology?
How do we get them comfortable with that?
And knowing that Google literally
has laser beam-protected floors-- like,
we don't have them in our office.
I don't know if you have them in yours.
But also even down to custom-built,
embedded hardware to make sure that the hardware hasn't been
tampered with, that it's not been hacked,
et cetera, which if you've been seeing the news, it happens.
And so how do we get our enterprise customers
comfortable with that level of security?
So that was kind of the beginning of our journey.
And I'm excited to share a little more today about where
we kind of fell into.
There's much more to cloud than just, I would almost say,
these menial things of hey, look.
We can have reduced costs.
We can scale quickly.
These are, maybe, table stakes.
But there are some really cool things
that help us think different about the challenges
that we face and that we solve for our enterprise customers.
STEPHANIE WONG: Right.
And just for the audience, is anyone
familiar with seeing that closet photo in your own offices?
I'm sure it's pretty common.
And we're also familiar with that
need to scale very quickly in a short amount of time.
Can you elaborate more on this amount
of scale of traffic and data that you've seen at Quantum?
MARIO CIABARRA: Yeah, I'm super excited.
I love scale.
I'm passionate about taking on large amounts of data.
And how do we ingest it and actually get insights from it?
So a typical month at Quantum, we
will take in over 600 million sessions.
So to put that in perspective, it's
about 300 million unique visitors.
And that's about one ninth of the internet.
That's one ninth of the folks that are online.
We also take 89 billion HTTPS requests.
And that's, to put in perspective of scale,
about one fifth the number of queries
that Google search handles in a given month.
So-- massive amounts of scale.
We handle 1 and 1/2 petabytes of network traffic,
a half of a petabyte of cloud storage,
and over 14 petabytes of BigQuery analysis.
So this is just an overwhelming amount of data.
It kind of feels like just an onslaught of information.
And you know, what was fun about cloud
is, how do we build an infrastructure, do that?
We don't need to.
We can hit a couple buttons in Google Console and scale.
And we have some incredible retailers I mentioned earlier.
Imagine at Black Friday--
I know [INAUDIBLE] yesterday was talking
about Kohl's and his experience in cloud
and being able to scale so quickly.
I mean, imagine a day that you need
to handle three to five times your normal workload,
and you just hit a switch.
It was pretty exciting on how we can engage in cloud
and have so much success with so much ease.
STEPHANIE WONG: Right.
Yeah, and this can't be an easy challenge.
How are you able to support this amount of data traffic--
over a petabyte?
MARIO CIABARRA: Yeah, so it's--
they have this massive scale.
How many of you have been involved
with big data projects?
And how many of you have had incredible insights coming out
of those big data projects?
So for the viewer audience, not that many, right?
Everyone kind of put their hand down.
And so we collect incredible amounts of data.
But no one cares about what you collect.
I don't care about hoarding information or hoarding things.
I care about, what insights can I deliver from that data?
And so that's where that massive scale analysis really
comes into play.
And so when we talk about us as a company
and how to get great insights from it,
I like to relate it to flying an airline.
I know all of us-- and many of us have flown here.
But many of us fly often.
And when I'm flying to Iceland today, and I'll tell you,
I didn't buy the jet to take that flight to Iceland.
I'm buying a seat, right?
And so with our sporadic workload--
so we have folks coming into our console and our platform
and trying to look at insights and questions
they want to answer.
Are they doing well?
Is their hybrid cloud performing well?
Is their on-prem and their cloud solutions
performing the same or even better in cloud?
They have questions they want to answer, but it's sporadic.
It's not constantly in our platform asking questions.
So it's sporadic.
It's very similar to, I need to take a flight somewhere.
I need a very small amount of compute power--
or, a large amount of compute power
for a small amount of time.
I don't need to take--
I don't need to buy the jet.
I want a seat on the airline.
So we had that same challenge with our workload.
And what's really exciting is how the cloud can empower this.
And we realized quickly that our success was really about,
how do we ingest this data and find
novel ways to present insights from that large amount of data
set?
And that was what was going to differentiate us.
It wasn't about how we collected.
It wasn't about how it looked in our console.
It was, what insights can we deliver for our customers?
STEPHANIE WONG: Right.
Yeah, and this data scale is handled by BigQuery,
as you mentioned.
And a lot of our customers at Google Cloud
are doing very innovative things with BigQuery at the moment.
But as many people know, it's not the only data science
platform.
You know, there are other vendors
that offer other solutions.
You mentioned a really funny story about your son earlier.
Do you mind telling the audience about that story?
MARIO CIABARRA: Yeah, so I was writing an article for Forbes
about our experience in cloud and different takeaways
that we've had at massive scale.
And I was writing about BigQuery and about how
it's been transformational for our business, how we've
been empowering our customers to have new insights that they'd
never even knew that they could ask,
some automation about the processes
that they discover where frustration
is happening online.
And it's hilarious because my 9-year-old son--
his name is Cody--
he seriously says to me, "But Dad, can't Amazon
and Microsoft do this, too?"
And I was kind of, like, blown away
by he knew the names of the other two large cloud
providers.
And you know, it's funny that you ask this, Cody--
absolutely.
Like, I really believe that Amazon and Azure-- right,
the AWS and Azure-- can deliver on massive-scale compute
and massive-scale data analysis.
But the challenge that we have is our workload is sporadic.
And I don't want to spin up, let's
say, 50,000 CPUs to do an analysis that
might take a minute or two to spin up,
run my query for five seconds, and shut it all down.
It's just not financially feasible.
And so, I think one of the really unique differentiations
of Google Cloud has been this fractional lease of analysis,
of big compute, to chug away at massive scale of data.
And I'm going to talk a little bit about some
of the other things that we believe
that massive compute brings to the table
when it comes to thinking different about data analysis.
But for us-- and what I told my son was--
absolutely.
But fractional leasing has been a differentiator.
And I think if you have either used BigQuery,
or if you know folks that are, they'll talk to you about that.
That same fractional lease, that massive amount
of query capabilities for a small period time,
is really one of the unique features of BigQuery.
STEPHANIE WONG: And the title of this session
is Apple's 1997 Promise to Think Different.
Can you talk more about what you mean by that?
How are you thinking different with your teams right now?
MARIO CIABARRA: Yeah, so this is one of the best marketing
campaigns of all time.
And you know, I know from looking at the audience
that there's some of us that probably remember
this campaign very well.
And it was talking about Apple's engineering fortitude,
and likening it to geniuses that were brave enough
to take on the world and change it.
It may be crazy enough--
I think the words were crazy enough
to try to change the world.
And then they actually accomplished it.
But it wasn't about buying an Apple computer,
and all of a sudden, you thought different.
It was simply, like, we think different like these geniuses.
What I thought was amazing-- and where I came up with this
concept was--
when we engaged with BigQuery, we basically took our handcuffs
off, these handcuffs that we had no idea existed,
in terms of massive-scale query analysis.
We can start asking questions that we never
thought about asking because now, we didn't have any rails.
We didn't have indexes.
We didn't have certain types of queries that we could run.
We could run a question across a day, a month, a year,
and start getting insights that we never really knew about.
So my belief is that we are now able to think different about,
how do we solve business problems for our customers,
for our enterprises, in ways that we never really explored?
STEPHANIE WONG: Right.
I think you also talked about machine intelligence a bit too,
right?
MARIO CIABARRA: Yeah, so one of the things that we discovered--
and we're a startup.
We went from just a few years ago from two people
to there are 80-plus today.
As that journey happens, you start
to learn-- for me-- a little bit about that selling process.
And we used to talk.
And I think it's kind of funny, because Apple never really
talked about the performance of the CPUs
and all the metrics of memory, stuff that
techies, that we think about, and we think
about we should share this.
What I learned was, no one really
wants this concept about machine intelligence.
We can talk about AI, machine learning.
No one really cares about that.
They just want intelligence.
They just want answers to their challenges
and their business needs.
And so that was a journey that we went on and started
figuring out that we can help customers automate some
of those human processes, some of the things that they do
and they spend hours or days.
We can take massive-scale compute
and solve them, perhaps, in seconds.
STEPHANIE WONG: Yeah, and machine intelligence
here, how--
can you talk about some of the examples
that you've gone through at your company?
How are you able to implement this type of practice
and thinking?
MARIO CIABARRA: Yeah, so we've been able to take--
some of the things that we've really solved for our customers
have been, when I go to a website
and I'm having a bad experience, how do I know about it?
I don't fill out surveys.
I don't call the company and tell them, hey,
your website's broken.
I just leave.
So how do we figure out people are having a bad experience?
And it turns out we can look at the behavior.
They're clicking reload.
They're hitting the back and forward button.
They're rage clicking the that book a flight
or transact button.
And we can look at those behaviors,
and with BigQuery, we can start aggregating
around those behaviors and discover these lead
to under-conversion.
And so we've automated that process of discovery
and build that sort of human discovery, human analysis
into our product.
So it's been really transformational
for our enterprise customers because they
don't have to spend days wondering is this actually
impacting my business?
They can know about it in seconds.
Another fantastic one was the ability
to understand when did a problem actually start occurring?
And for our enterprise customers,
they had no way to know when the bug was
introduced into their system.
They would spend days looking at logs
and looking at release notes and trying to figure it out.
What we're able to do is spin up tens of thousands
of CPUs in Cloud Functions and Google Compute
to analyze when did this start happening
by replaying every session that visited the website.
When did the indicator of the issue start occurring?
So with massive-scale compute, we
started solving problems that our enterprise customers had
never even broached because they just
didn't have 50,000, 100,000 CPUs lying around
in the data center.
So it's really been amazing how cloud
has enabled us to think different
about some of the challenges that these enterprises
were facing.
And I think another one that's been really a hot topic for us
in our space has been, how do we take the high standard of care
with our customer data?
And a lot of it was left to human configuration.
We know where sensitive information was.
We know where there's PCI information, or PII,
and either encrypt or mask it.
The problem with that is it always
left it up to human error.
And how often was someone looking at this type
of data and discovery?
And that discovery would happen.
We needed to automate that.
So we're now engaging with Google's Sensitive Data API
and rolling this out, hopefully, in the next short time
period, about instead of waiting for humans to discover
that there's misconfiguration, can't we just
have the computer tell us that here's
an opportunity to improve the data that we're collecting?
So that's just really great ways and exciting ways
that we can engage in machine learning,
massive-scale compute, massive-scale query analysis
to automate some of the functions
that we had to do as humans
STEPHANIE WONG: Yeah, and I think a lot of our audience
probably wants to know how they can
implement these practices into their own organizations
as well?
Can you talk about some exemplary use cases for them
to do this?
MARIO CIABARRA: Yeah, I think there's not a prescriptive way
that you can take this and bring it home to all
of your own organizations.
You have to take a look at, what are some of the human processes
that we can automate?
And as you engage in cloud scale and think about
compute in different ways, I do believe
it will help you think differently
about some of the massive problems
that you engage in at your enterprises and startups alike.
And so, here's my thought process.
It's a very simple process.
And it just literally the process
that we took to discover, hey, how can we
automate some of these processes with massive-scale compute?
And it's really about, where are there
tasks that we face that consume a lot of time?
How can we translate that into a set of queries?
How can we take a look at this massive-scale data
and understand, here's how we automate that process.
Identifying key insights-- what will make people
engage in these results?
Like for us, it was about revenue.
If we can talk about, if you do this, you'll make $17 million,
that's when people started saying, OK, these are insights
that I want to listen to.
Versus hey, here's some graphs and charts.
Thank you for the anecdotal evidence.
I'm going to go on about my priorities in my daily job.
So when we can connect that with revenue,
that's where, really, people start
to engage in those results.
And then just engaging across teams--
so we learned a little bit about cross-team, and maybe
the intersection of domains, where
we believe that this is where creativity is really born.
And so we can get product teams and engineering
and DevOps and business teams and executive teams all
aligned on where the biggest impacts are for their business.
We can start to make a--
what we believe, to say the words-- digital transformation.
Those words mean a little bit different for each company,
but a digital transformation on having everyone
aligned on where are my biggest successes for my organization,
and how do we get everyone to take action as opposed
to, maybe, confirmation bias, or everyone
having their own agendas?
So that's been pretty exciting.
So following that recipe to success,
we've had about three or four iterations
on how do we think different about our data.
And I think it's something that the audience could take away.
STEPHANIE WONG: Thank you.
Yeah, thank you so much for sharing
some of those wins that you've gotten with Google Cloud,
and kind of enabling our audience
to be able to achieve their own digital transformation.
MARIO CIABARRA: Awesome.
Well Stephanie, it was a pleasure being here.
And I thank you all for attending.
STEPHANIE WONG: All right, thank you, everyone.
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