>> [Announcer] Live from Times Square in New York City,
it's theCUBE covering IBM's
Change the Game: Winning with AI,
brought to you by IBM.
>> Hello everybody, welcome to theCUBE's
special presentation.
We're covering IBM's announcements today around AI.
IBM, as theCUBE does, runs of sessions and programs
in conjunction with Strata, which is down at the Javits,
and we're Rob Thomas, who's the General Manager
of IBM Analytics.
Long time Cube alum, Rob, great to see you.
>> Dave, great to see you.
>> So you guys got a lot going on today.
We're here at the Westin Hotel, you've got an analyst event,
you've got a partner meeting, you've got an event tonight,
Change the game: winning with AI at Terminal 5,
check that out, ibm.com/WinWithAI, go register there.
But Rob, let's start with what you guys have going on,
give us the run down.
>> Yeah, it's a big week for us, and like many others,
it's great when you have Strata, a lot of people in town.
So, we've structured a week where, today, we're going to spend
a lot of time with analysts and our business partners,
talking about where we're going with data and AI.
This evening, we've got a broadcast,
it's called Winning with AI.
What's unique about that broadcast is it's all clients.
We've got clients on stage doing demonstrations,
how they're using IBM technology
to get to unique outcomes in their business.
So I think it's going to be a pretty unique event,
which should be a lot of fun.
>> So this place, it looks like a cool event, a venue,
Terminal 5, it's just up the street
on the west side highway, probably a mile
from the Javits Center, so definitely check that out.
Alright, let's talk about, Rob, we've known each other
for a long time, we've seen the early Hadoop days,
you guys were very careful about diving in,
you kind of let things settle and watched very carefully,
and then came in at the right time.
But we saw the evolution of so-called Big Data
go from a phase of really reducing investments,
cheaper data warehousing, and what that did
is allowed people to collect a lot more data,
and kind of get ready for this era that we're in now.
But maybe you can give us your perspective
on the phases, the waves that we've seen
of data, and where we are today and where we're going.
>> I kind of think of it as a maturity curve.
So when I go talk to clients, I say,
look, you need to be on a journey towards AI.
I think probably nobody disagrees that they need
something there, the question is, how do you get there?
So you think about the steps, it's about,
a lot of people started with, we're going to reduce the cost
of our operations, we're going to use data to take out cost,
that was kind of the Hadoop thrust, I would say.
Then they moved to, well, now we need to see more
about our data, we need higher performance data,
BI data warehousing.
So, everybody, I would say, has dabbled in those two area.
The next leap forward is self-service analytics,
so how do you actually empower everybody
in your organization to use and access data?
And the next step beyond that is,
can I use AI to drive new business models,
new levers of growth, for my business?
So, I ask clients, pin yourself on this journey, most are,
depends on the division or the part of the company,
they're at different areas, but as I tell everybody,
if you don't know where you are and you don't know
where you want to go, you're just going to wind around,
so I try to get them to pin down, where are you
versus where do you want to go?
>> So four phases, basically, the sort of cheap data store,
the BI data warehouse modernization, self-service analytics,
a big part of that is data science
and data science collaboration, you guys have
a lot of investments there, and then new business models
with AI automation running on top.
Where are we today?
Would you say we're kind of in-between BI/DW modernization
and on our way to self-service analytics,
or what's your sense?
>> I'd say most are right in the middle
between BI data warehousing and self-service analytics.
Self-service analytics is hard, because it requires you,
sometimes to take a couple steps back,
and look at your data.
It's hard to provide self-service if you don't have
a data catalog, if you don't have data security,
if you haven't gone through the processes
around data governance.
So, sometimes you have to take one step back
to go two steps forward, that's why I see a lot of people,
I'd say, stuck in the middle right now.
And the examples that you're going to see tonight
as part of the broadcast are clients that have figured out
how to break through that wall,
and I think that's pretty illustrative of what's possible.
>> Okay, so you're saying that, got to maybe take a step back
and get the infrastructure right with,
let's say a catalog, to give some basic things
that they have to do, some x's and o's, you've got
the Vince Lombardi played out here, and also, skillsets,
I imagine, is a key part of that.
So, that's what they've got to do to get prepared,
and then, what's next?
They start creating new business models,
imagining this is where the cheap data officer comes in
and it's an executive level, what are you seeing
clients as part of digital transformation,
what's the conversation like with customers?
>> The biggest change, the great thing about
the times we live in, is technology's become so accessible,
you can do things very quickly.
We created a team last year called Data Science Elite,
and we've hired what we think are some of the best
data scientists in the world.
Their only job is to go work with clients
and help them get to a first success with data science.
So, we put a team in.
Normally, one month, two months,
normally a team of two or three people, our investment,
and we say, let's go build a model,
let's get to an outcome, and you can do this
incredibly quickly now.
I tell clients, I see somebody that says,
we're going to spend six months evaluating
and thinking about this, I was like,
why would you spend six months thinking about this
when you could actually do it in one month?
So you just need to get over the edge and go try it.
>> So we're going to learn more about
the Data Science Elite team.
We've got John Thomas coming on today,
who is a distinguished engineer at IBM,
and he's very much involved in that team,
and I think we have a customer who's actually
gone through that, so we're going to talk about
what their experience was with the Data Science Elite team.
Alright, you've got some hard news coming up,
you've actually made some news earlier
with Hortonworks and Red Hat, I want to talk about that,
but you've also got some hard news today.
Take us through that.
>> Yeah, let's talk about all three.
First, Monday we announced the expanded relationship
with both Hortonworks and Red Hat.
This goes back to one of the core beliefs I talked about,
every enterprise is modernizing their data
and application of states, I don't think there's
any debate about that.
We are big believers in Kubernetes and containers
as the architecture to drive that modernization.
The announcement on Monday was, we're working closer
with Red Hat to take all of our data services
as part of Cloud Private for Data,
which are basically microservice for data,
and we're running those on OpenShift,
and we're starting to see great customer traction with that.
And where does Hortonworks come in?
Hadoop has been the outlier on moving to microservices
containers, we're working with Hortonworks to help them
make that move as well.
So, it's really about the three of us getting together
and helping clients with this modernization journey.
>> So, just to remind people, you remember ODPI, folks?
It was all this kerfuffle about, why do we even need this?
Well, what's interesting to me about this triumvirate is,
well, first of all, Red Hat and Hortonworks
are hardcore opensource, IBM's always been
a big supporter of open source.
You three got together and you're proving now
the productivity for customers of this relationship.
You guys don't talk about this, but Hortonworks had to,
when it's public call, that the relationship with IBM
drove many, many seven-figure deals,
which, obviously means that customers are getting
value out of this, so it's great to see that
come to fruition, and it wasn't just a Barney announcement
a couple years ago, so congratulations on that.
Now, there's this other news that you guys announced
this morning, talk about that.
>> Yeah, two other things.
One is, we announced a relationship with Stack Overflow.
50 million developers go to Stack Overflow a month,
it's an amazing environment for developers
that are looking to do new things,
and we're sponsoring a community around AI.
Back to your point before, you said,
is there a skills gap in enterprises,
there absolutely is, I don't think that's a surprise.
Data science, AI developers, not every company
has the skills they need, so we're sponsoring a community
to help drive the growth of skills in and around
data science and AI.
So things like Python, R, Scala, these are the languages
of data science, and it's a great relationship
with us and Stack Overflow to build a community
to get things going on skills.
>> Okay, and then there was one more.
>> Last one's a product announcement.
This is one of the most interesting product annoucements
we've had in quite a while.
Imagine this, you write a sequel query,
and traditional approach is, I've got a server,
I point it as that server, I get the data,
it's pretty limited.
We're announcing technology where I write a query,
and it can find data anywhere in the world.
I think of it as wide-area sequel.
So it can find data on an automotive device,
a telematics device, an IoT device, it could be
a mobile device, we think of it as sequel the whole world.
You write a query, you can find the data anywhere it is,
and we take advantage of the processing power on the edge.
The biggest problem with IoT is,
it's been the old mantra of,
go find the data, bring it all back
to a centralized warehouse, that makes it impossible
to do it real time.
We're enabling real time because we can write a query once,
find data anywhere, this is technology we've had in preview
for the last year.
We've been working with a lot of clients
to prove out used cases to do it,
we're integrating as the capability inside of
IBM Cloud Private for Data.
So if you buy IBM Cloud for Data, it's there.
>> Interesting, so when you've been around as long as I have,
long enough to see some of the pendulums swings,
and it's clearly a pendulum swing back toward
decentralization in the edge, but the key is,
from what you just described, is you're sort of
redefining the boundary, so I presume it's the edge,
any Cloud, or on premises, where you can find that data,
is that correct?
>> Yeah, so it's multi-Cloud.
I mean, look, every organization is going to be multi-Cloud,
like 100%, that's going to happen, and that could be private,
it could be multiple public Cloud providers,
but the key point is, data on the edge is not just
limited to what's in those Clouds.
It could be anywhere that you're collecting data.
And, we're enabling an architecture which performs
incredibly well, because you take advantage
of processing power on the edge, where you can get data
anywhere that it sits.
>> Okay, so, then, I'm setting up a Cloud,
I'll call it a Cloud architecture, that encompasses
the edge, where essentially, there are no boundaries,
and you're bringing security.
We talked about containers before,
we've been talking about Kubernetes all week here
at a Big Data show.
And then of course, Cloud, and what's interesting,
I think many of the Hadoop distral vendors
kind of missed Cloud early on, and then now are sort of
saying, oh wow, it's a hybrid world and we've got a part,
you guys obviously made some moves,
a couple billion dollar moves, to do some acquisitions
and get hardcore into Cloud,
so that becomes a critical component.
You're not just limiting your scope to the IBM Cloud.
You're recognizing that it's a multi-Cloud world,
that' what customers want to do.
Your comments.
>> It's multi-Cloud, and it's not just the IBM Cloud,
I think the most predominant Cloud that's emerging
is every client's private Cloud.
Every client I talk to is building out
a containerized architecture.
They need their own Cloud, and they need
seamless connectivity to any public Cloud
that they may be using.
This is why you see such a premium being put on things
like data ingestion, data curation.
It's not popular, it's not exciting,
people don't want to talk about it,
but we're the biggest inhibitors, to this AI point,
comes back to data curation, data ingestion,
because if you're dealing with multiple Clouds,
suddenly your data's in a bunch of different spots.
>> Well, so you're basically, and we talked about this
a lot on theCUBE, you're bringing the Cloud model
to the data, wherever the data lives.
Is that the right way to think about it?
>> I think organizations have spoken,
set aside what they say, look at their actions.
Their actions say, we don't want to move all of our data
to any particular Cloud, we'll move some of our data.
We need to give them seamless connectivity
so that they can leave their data where they want,
we can bring Cloud-Native Architecture to their data,
we could also help move their data
to a Cloud-Native architecture if that's what they prefer.
>> Well, it makes sense, because you've got physics,
latency, you've got economics, moving all the data
into a public Cloud is expensive and just doesn't make
economic sense, and then you've got things like GDPR,
which says, well, you have to keep the data,
certain laws of the land, if you will, that say,
you've got to keep the data in whatever it is,
in Germany, or whatever country.
So those sort of edicts dictate how you approach
managing workloads and what you put where, right?
Okay, what's going on with Watson?
Give us the update there.
>> I get a lot of questions, people trying to
peel back the onion of what exactly is it?
So, I want to make that super clear here.
Watson is a few things, start at the bottom.
You need a runtime for models that you've built.
So we have a product called Watson Machine Learning,
runs anywhere you want, that is the runtime
for how you execute models that you've built.
Anytime you have a runtime, you need somewhere
where you can build models,
you need a development environment.
That is called Watson Studio.
So, we had a product called Data Science Experience,
we've evolved that into Watson Studio,
connecting in some of those features.
So we have Watson Studio, that's the
development environment, Watson Machine Learning,
that's the runtime.
Now you move further up the stack.
We have a set of APIs that bring in human features,
vision, natural language processing, audio analytics,
those types of things.
You can integrate those as part of a model that you build.
And then on top of that, we've got things like
Watson Applications, we've got Watson for call centers,
doing customer service and chatbots,
and then we've got a lot of clients who've taken
pieces of that stack and built their own AI solutions.
They've taken some of the APIs, they've taken
some of the design time, the studio, they've taken
some of the Watson Machine Learning.
So, it is really a stack of capabilities,
and where we're driving the greatest productivity,
this is in a lot of the examples you'll see tonight
for clients, is clients that have bought into this idea of,
I need a development environment, I need a runtime,
where I can deploy models anywhere.
We're getting a lot of momentum on that,
and then that raises the question of,
well, do I have expandability,
do I have trust in transparency,
and that's another thing that we're working on.
>> Okay, so there's API oriented architecture,
exposing all these services make it very easy
for people to consume.
Okay, so we've been talking all week at Cube NYC,
is Big Data is in AI, is this old wine, new bottle?
I mean, it's clear, Rob, from the conversation here,
there's a lot of substantive innovation,
and early adoption, anyway, of some of these innovations,
but a lot of potential going forward.
Last thoughts?
>> What people have to realize is AI is not magic,
it's still computer science.
So it actually requires some hard work.
You need to roll up your sleeves, you need to understand
how I get from point A to point B,
you need a development environment, you need a runtime.
I want people to really think about this, it's not magic.
I think for a while, people have gotten the impression
that there's some magic button.
There's not, but if you put in the time,
and it's not a lot of time, you'll see the examples tonight,
most of them have been done in one or two months,
there's great business value
in starting to leverage AI in your business.
>> Awesome, alright, so if you're in this city
or you're at Strata, go to ibm.com/WinWithAI,
register for the event tonight.
Rob, we'll see you there, thanks so much for coming back.
>> Yeah, it's going to be fun, thanks Dave, great to see you.
>> Alright, keep it right there everybody,
we'll be back with our next guest
right after this short break, you're watching theCUBE.