>> Live from Las Vegas, it's theCUBE
Covering Informatica World 2019
Brought to you by Informatica.
>> Welcome back everyone to theCUBE's live coverage of
Informatica World 2019 here in Las Vegas. I'm your host,
Rebecca Knight, along with my co-host, John Furrier.
We are joined by Ariel Kelman. He is the VP, Worldwide
Marketing at AWS. Thank you so much for coming on theCUBE.
>> Thanks so much for having me on today.
>> So let's start out just at ten thousand feet and talk
a little bit about what you're seeing as the major cloud
and AI trends and what your customers are telling you.
>> Yeah, so I mean, clearly, machine learning and AI
is really the forefront of a lot of discussions in
enterprise IT and there's massive interest but it's still
really early. And one of the things that we're seeing
companies really focused on now is just getting all their
data ready to do the machine learning training. And as
opposed to also, in addition I mean, training up all their
people to be able to use these new skills. But we're seeing
tons of interest, it's still very early, but you know one
of the reasons here at Informatica World is that getting all
the data imported and ready is, you know, it's almost
doubled or tripled in importance as it was when people were
just trying to do analytics. Now they're doing machine
learning as well. You know, we're seeing huge interest
in that.
>> I want to get into some of the cloud trends with your
business, but first, what's the relationship with
Informatica, and you know we see them certainly
at re:Invent. Why are you here? Was there an announcement?
What's the big story?
>> I mean, we've been working together for a long time and
it's very complementary products and number varies. I think
the relationship really started deepening when we released
Redshift in 2013, and having so many customers that wanted
to get data into the cloud to do data we're housing, we're
already using Informatica in, to help get the data loaded
and cleansed and so really they're one of the great partners
that's fueling moving data into the cloud and helping
our customers be more successful with Redshift.
>> Yeah, one of the things I really admire about you guys
is that you're very customer centric. We've been following
Amazon as you know since their, actually second reinvent,
Cube's been there every time, and just watching the growth,
you know, Cloud certainly has been a power source for
innovation, SAS companies that are born in the cloud have
exponentially scaled faster than most enterprises because
they use data. And so data's been a heart of all the
successful SAS businesses, that's why start ups gravitated
to the Cloud right away. But now that you guys got
enterprise adoption, you guys have been customer centric
and as you listen to customers, what are you guys hearing
from that? Because the data on premises, you've got more
compliance, you've got more regulation, you've got-- news
today-- more privacy and now you've got regions, countries
with different laws. So the complexity around even just
regulatory, nevermind tech complexity, how are you guys
helping customers when they say, you know what, I want
to get to the cloud, love Amazon, love the cloud, but I've
got my, I've got to clean up my on param house.
>> Yeah, I would say like a lot, if you look at a lot of
the professional services work that we do, a lot of it is
around getting the company prepared and organized with all
their data before they move to the cloud: segmenting it,
understanding the different security regulatory
requirements, coming up with a plan of what they need,
what data they're going to maybe abstract up, before they load
it, and there's a lot of work there. And, you know, we've
been focused on trying to help customers..
>> And is there a part in you're helping migrate to
the cloud, is that.. >> Yeah, there's technology
pieces, companies like Informatica helping to extract and
transform and load the data and on data governance policies.
But then also, for a lot of our systems integrator partners,
Cognizant, Accenture, Deloitte-- they're very involved in
these projects. There's a lot of work that goes on; a lot
of people don't talk about just before you can even start
doing the machine learning, and a lot of that's getting
your data ready.
>> So how, what are some of the best practices that have
emerged in working with companies that, as you said, there's
a lot of pre-work that needs to be done and they need to be
very thoughtful about about sort of getting their
data sorted.
>> Well I think the number one thing that I see and
I recommend is to actually first take a step back from
the data and to focus on what are the business requirements
of, what questions are you trying to answer, let's say
with machine learning, or with data science
advanced analytics, and then back out the data from that.
What we see a lot of, you know companies sometimes will
have it be a data science driven project. Okay, here's all
the data that we have, let's put it in one place, when you
may not be spending time proportionate to the value of
the data. And so that's one of the key things that we see,
and to come up-- just come up with a strong plan around what
answers you're, what business questions you're trying
to answer.
>> On the growth of Amazon, you guys certainly have had
great record numbers, growth, even in the double digit
kind of growth you're seeing on top of your baseline has
been phenomenal. Clearly number one on the cloud.
Enterprise has been a big focus. I noticed that on the NHL,
your logo's on the ice during the playoffs; you've got the
Statcast. You guys are creating a lot of aware-- I see a lot
of billboards everywhere, a lot of TV ads. Is that part
of the strategy is to get you guys more brand awareness?
What's the.. >> We're trying, you know,
it's part of our overall brand awareness strategy. What
we're trying to do is to help, we're trying to communicate
to the world how our customers are being successful using
our technology, specifically machine learning and AI.
It's one of these things where so many companies want to do it
but they say, well, what am I supposed to use it for?
And so, you know, one of, if you dumb down what marketing is
at AWS, it's inspiring people about what they can run in
the cloud with AWS, what use cases they should consider us
for, and then we spend a lot of energy giving them
the technical education and enablement so they can be
successful using our products. At the end of the day,
we make money when our customers are successful using
our products.
>> One of the hot products was SageMaker, we see in
that group, AI's gone mainstream. That's a great tail wind
for you guys because it kind of encapsulates or kind of
doesn't have to get all nerdy about cloud, you know,
infrastructure and SAS. AI kind of speaks to many people.
It's one of the hottest curriculums and topics in the world.
>> Yeah, and with SageMaker, we're trying to address a
problem that we see in most of our customers where
the everyday developer is not, does not have expertise
in machine learning. They want to learn it, so we think that
anything we can do to make it easier for every developer
to ramp up on machine learning the better. So that's why
we came up with SageMaker as a platform to really make
all three stages of machine learning easier: getting
your data prepared for training, training in
optimized models, and then running inference to make
the predictions and incorporate that into people's
applications.
>> One of the themes that's really emerging in this
conversation is the need to make sure developers are
ready and that your people are skilled up and know what
they need to know. How are, how is AWS thinking about the
skills gap, and what are you doing to remedy it?
>> Yeah, a couple things. I mean, we're really, like a lot
of things we do, we'll say what are all the ways we can
attack the problem and let's try and help. So,
we have free training that we've been creating online.
We've been partnering with large online training firms
like Udacity and Coursera. We have an ML solutions lab that
help companies prototype, we have a pretty significant
professional services team, and then we're working with
all of out systems integrators partners to build up their
machine learning practices. It's a new area for a lot
of them and we've been pushing them to add more people so
they can help their customers.
>> Talk about the conferences, you have re:Invent, the CORE
conference, we've been theCUBE there. We've just also
covered London, Amazon's Web Services summit, and 22,000
registered, 14,000 showed up. Got huge global reach now.
How do you keep up with this?
I mean it's a... >> Well we're trying to
help our customers keep up with all the technology.
I mean, really, we have about, maybe 25 or so of these
summits around the world-- usually around two days,
several thousand people, free conferences. And what we're
trying to do is >> They're free?
>> The summits are free and it's like,
we introduce so much new technology, new services, deeper
functionality within our exiting services, and our customers
are very hungry to learn the latest best practices and
how they can use these, and so we're trying to be in all
the major areas to come in and provide deep educational
content to help our customers be more successful.
>> And re:Invent's coming around the corner. Any themes there
early on, numbers wise? Last year you had, again, record
numbers. I mean at some point,
is Vegas too small >> Yeah, we had over
50,000 people. We're going to have even more, and we've
been expanding to more and more locations around Las Vegas
and you know we're going to keep growing. There's a lot of
demand. I mean, we want to be able to provide the re:Invent
experience for as many people as want to attend.
>> What's the biggest skill set, you know the folks
graduating this month, my daughter's graduating from
Cal Berkeley, and a lot of others are graduating
>> Congratulations >> high school. Everyone wants
to either jump into some sort of data related field,
doesn't have to be computer science, those numbers are up.
What's your view of skill sets that are needed right now
that weren't in curriculum, or what pieces of curriculum
should people be learning to be successful
if machine learning continues to grow from helping videos
surface to collecting customer data. Machine learning's
going to be feeding the AI applications and SAS businesses.
>> Yeah, I mean look, you just forget about machine learning,
you go to a higher level. There's not enough good
developers. I mean, we're in a world now where
any enterprise that is going to be successful is going to have
their own software developers. They're going to be writing
their own software. That's not how the world was 15 years
ago. But if you're a large corporation and you're
outsourcing your technology, you're going to get disrupted by
someone else who does believe in custom software and
developers. So the demand for really good software
engineers, I mean we deal with all the time, we're hiring.
It is always going to outstrip supply. And so, for young
people, I would encourage them to start coding and to not be
over reliant on the university curriculums, which don't
always keep pace with, you know, with the latest trends.
>> And you guys got a ton of material online too, you
can always go to your site. Okay, on the next question
around, as someone figures out, okay, enterprise versus
pure SAS, you guys have proven with the Cloud that start ups
can grow very fast and then the list goes on: AirBnB,
Pinterest, Zoom Communications, disrupting existing big,
mature markets by having access to the data. So how do you
talk about customers when you say, hey, you know, I want to
be like a SAS company, like a consumer company, leverage
data, but I've got a lot of stuff on premise. So how do I
not make that data constrained? How do you guys feel about
that conversation because that seems to be the top
conversation here, is you know, it's not to say be
consumer, it's consumer-like. Leveraging data, cause if
data's not into AI, there's no, AI doesn't work, right? So
>> Right >> It can't be constrained
by anything.
>> Well, you know, you talk to all these companies and
at first they don't even know what they don't know in terms
of what is that data? And where is it? And what are
the pieces that are important? And so, you know, we
encourage people to do a good amount of strategy work before
they even start to move bits up to the cloud. And of course,
then we have a lot of ways we can help them, from our
Snowball machines that they can plug in, all the way to our
Snowmobile, which is the semi truck that you can drive up
to your data center and offload very large amounts of data
and drive it over to our data centers.
>> One of the things that is trending-- we had Ali from
Data Bricks talk about, he absolutely believes a lot of
the same philosophies you guys do-- data in the cloud.
And one of his arguments was is that there's a lot of data
sets in these marketplaces now where you can really leverage
other people's data, and we see that on cybersecurity where
people are starting to share data, and Cloud is a better
model for that than trying to ship drives around,
and there's a time for Snowball, I get that, and Snowmobile,
the big trucks for large ingestion into the cloud, but
the enterprise, this is a new phenomenon. No one really
shared a lot in the old days. This is a new dynamic. Talk
about that, is it-- >> I mean, sharing, selling,
monetizing data. If there's something that is important,
there will be a market for it. And I think we're seeing
that just the hunger, everything from enterprises to
startups, that want more data, whether it's for machine
learning to train their models, or it's just to run
analytics and compare against their data sets. So I think
the commercial opportunity is pretty large.
>> I think you're right on that. I think that's a great
insight. I mean, no one ever thought about data as a service
from our data set standpoint, 'cause data sets feed
machine learning. All right, so let's do, give the plug
on what's going on with AWS. What's new, what's on your
plate, what's notable. I mean I love the NHL, I couldn't
resist that plug for you being a hockey fan. But what's
new in your world?
>> Um, you know, we're, we're in early planning stages on our
re:Invent conference, our engineers are hard at work on
a lot of new technology that we're going to have ready
between now and our re:Invent show. You know, also we're,
my team's been doing a lot of work with the sports
organizations. We've had some interesting machine learning
work with major league baseball. They rolled out this year
a new machine learning model to do stolen base predictions.
So, you can see on some of the broadcasts, as a runner
goes past first base, we'll have a ticker that will show
what the probability is that they'll be successful stealing
second base if they choose to run. Trying to make a little
more entertaining all those scenes we've seen in the past
of the pitcher throwing the ball back to first, trying
to use AI machine leaning to give a little bit more insight
into what's going on.
>> And that's the Statcast. Part of that's the Statcast
>> That's Statcast, yeah
>> And you got anything new coming around that
besides that new.. >> Yeah, I think that yeah,
major league baseball is hard at work on some new models
that I think will be announced fairly soon.
>> All right, to wrap up Informatica real quick,
an announcement here, news coming I hear. How are you guys
working with Informatica in the field? Is there any, can you
share more about relationship >> Yeah I mean I think we're
going to have an announcement a little bit later today,
I mean it's around the subject we've been talking about:
making it easier for customers to, you know, be successful
moving their data to the Cloud so that they can start
to benefit from the agility, the speed and the cost savings
of data analytics and machine learning in the Cloud.
>> And so when you're working with customers, I mean, because
this is the thing about Amazon. It is a famously innovative,
cutting edge company, and when you talk about the hunger
that you describe, that these customers, isn't it just that
they want to be around Amazon and kind of rub shoulders
with this really creative, thinking four steps ahead kind
of company. I mean how do you let your innovation rub off on
these customers? >> I mean there's a couple ways
We do, one of the things we've done recently is these
innovation workshops. We have this thing we talk about a lot
this working backwards process where we force the engineers
to write a press release before we'll green light
the product because we feel like if you can't clearly
articulate the customer benefit, then we probably shouldn't
start investing, right? And so we, that's one of the
processes that we use to help us innovate better,
more effectively and so we've been walk-- we walk customers
through this. We have them come, you know there's an
international company that I was, part of one of the efforts
we did in Palo Alto last year where we had a bunch of their
leadership team out for two days of workshops where we
worked a bunch of ideas through, through our process.
And so we do some of that but the other area is we try
and capture area where we think that we've innovated in
some interesting way into a service that then customers
can use. Like Amazon Connect I think is a good example
of it. This is our contact center call routing technology
and you know, one of the things Amazon's consumer
business is known for is having great customer support,
customer service, and they spent a lot of time
and energy making sure that calls get
routed intelligently to the right people, that you don't sit
on hold forever, and so we figure we're probably not
the only company that could benefit from that. Kind of like
with AWS, when we figure out how to run infrastructure
securely and high performance and availability, and so
we turn that into a service and it's become a
very successful service for us. A lot of companies have
similar contact center problems.
>> As a customer, I can attest to being on hold a lot.
Ariel, thank you so much for coming on theCUBE.
It's been great talking to you.
>> I appreciate it. Thank you.
>> Thanks for coming out, appreciate it.
>> I'm Rebecca Knight, for John Furrier. You are watching
theCUBE. Stay tuned.
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