>> Narrator: Live from Miami, Florida.
It's theCUBE covering IBM's Data and AI Forum.
Brought to you by IBM.
>> Welcome back to the port of Miami everybody
you're watching theCUBE the leader in live tech coverage
and we're here covering the IBM Data and AI Forum.
Rob Thomas is here.
He's the general manager for data and AI at IBM.
Great to see you again, Rob.
>> Dave, great to see you here in Miami.
Beautiful week here in the beach area, it's nice.
>> Yeah, this is quite an event I mean, I had thought
there was going to be like roughly a 1,000 people.
It's oversold, there's 17,
more than 1,700 people here.
This is a learning event right?
I mean people here, they're here to absorb best practice,
learn technical hands-on presentations.
Tell us a little bit more about
how this event has evolved.
>> It started as a really small training event,
like you said,
which goes back five years
and what we saw with those people,
they weren't looking for the normal kind of conference.
They wanted to be hands-on, they wanted to build something,
they wanted to come here
and leave with something they didn't have when they arrived.
So it started as a small builder conference
and now somehow it continues to grow every year
which we're very thankful for
and we continue to kind of expand, add sessions.
We got to add hotels this year
so it's really taken off.
>> Yeah and your title has two of the three superpowers,
data AI and of course cloud is the third superpower
which is part of IBM's portfolio.
But people want to apply those superpowers
and you used that metaphor in your keynote today
to really transform their business.
But you pointed out that only about,
AI is only four to 10% penetrated within organizations
and you talked about some of the barriers there
but there's a real appetite to learn isn't there?
>> There is.
Let's go talk about the superpowers for a bit.
AI does give employees superpowers
because they can do things now they couldn't do before.
But you think about superheroes.
They all have an origin story.
They always have somewhere where they started
and applying AI in an organization,
it's actually not about
doing something completely different.
It's about extenuating what you already do,
doing something massively better
that's kind of in your DNA already.
So we're encouraging all of our clients this week
like use the time to understand what you're great at
what your value proposition is
and then how do you use AI to extenuate that
because your superpower is only going to last
if it's starts with who you are as a company or as a person.
>> Who was your favorite superhero as a kid?
>> Let's see.
I was kind of into the whole Hall of Justice Superman,
that kind of thing.
>> Okay. >> That was
probably my cartoon.
>> I was a Batman guy and a reason I love Batman
was because of all the combination of tech, you know.
>> Yeah, that's true.
>> It kind of reminds me of what's happening
here today in the marketplace.
People are taking data, they're taking AI,
they're applying machine intelligence to that data
to create new insights
which they couldn't have before.
but to your point,
there's an issue with the quality of data.
>> Yeah.
>> And there's a skills gap as well.
So let's start with the the data quality problem.
Describe that problem and how are you guys attacking it?
>> Your AI is only as good as your data.
I'd say that's the fundamental problem
and organizations we work with,
80% of the projects get slowed down or they get stopped
because the company has a data problem.
That's why we introduced this idea of the AI ladder,
which is all of the steps that a company has to think about
for how they get to a level of data maturity
that's supports AI.
So how they collect their data, organize their data,
analyze their data and then ultimately
begin to infuse AI into business processes.
So every organization needs to climb that ladder
and they're all different spots.
So for some it might be we got to focus on organization
and a data catalog.
For others, it might be we got to do
a better job of data collection, data management.
That's for every organization to figure out
but you need a methodical approach
to how you attack the data problem.
>> So I want to ask you about the AI ladder.
So you could have have these verbs,
the verbs overlay on building blocks.
So I went back to some of my notes
in the original a AI ladder conversation
that you introduced a while back.
It was data and information architecture
at the at the base and then building on that,
analytics, machine learning, AI
and then now you've added the verbs collect,
organize, analyze and infuse.
Should we think of this as a maturity model
or building blocks and verbs
that you can apply depending on where you are
in that maturity model?
>> I would think of it as building blocks and a methodology
which is you've got to decide.
Should we focus on our data collection
and doing that right?
Is that our weakness or is it data organization
or is it the sexy stuff, the AI, the data science stuff.
This is just a tool to help organizations
organize themselves on what's important.
I ask every company I visit,
do you have a data strategy?
You wouldn't believe that looks you get
when you ask that question.
You get either well, "She's got one, he's got one
"so we got seven."
Or you get, "No, we've never had one."
Or "Hey, we just hired a CDO, so we hope to have one."
But we use the AI ladder just as a tool
to encourage companies to think about your data strategy.
>> Should do you think in the context,
I want to follow up on that in the data strategy,
because you see a lot of tactical data strategies.
Well, we use data to for this initiative
or that initiative maybe in sales or marketing
or maybe in RD.
Increasingly, are our organizations developing
and should they develop a holistic data strategy
or should they try to just get kind of quick wins?
What are you seeing in the marketplace?
>> It depends on where you are in your maturity cycle.
I do think it behooves every company
to say we understand where we are
and we understand where we want to go.
That could be the high level data strategy.
What are our focus and priorities going to be?
Once you understand focus and priorities,
the best way to get things into production
is through a bunch of small experiments to your point.
So I don't think it's an either/or
but I think it's really valuable
to have an overarching data strategy.
And I recommend the companies thinking
about a hub-and-spoke model for this.
Have a centralized chief data officer
but your business units also need a chief data officer.
So strategy in one place, execution in another.
There's a best practice to going about this,
>> Dave: That connects to threads.
>> Absolutely. >> That connects the dots.
>> Yeah.
>> You ask a question, what is AI?
You get that question a lot.
And you said it's about predicting,
automating and optimizing.
Can we kind of unpack that a little bit.
What's behind those three items?
>> I think people overreact to hype on topics like AI
and they think well, I'm not ready for robots
or I'm not ready for self-driving vehicles,
like those may or may not happen.
Don't know.
But AI is, let's think more basic.
It's about can we make better predictions as a business?
Every company wants to see a future,
they want the proverbial crystal ball.
AI helps you make better predictions
if you have the data to do that.
It helps you automate tasks,
automate the things that you don't want to do.
There's a lot of work that has to happen every day,
that nobody really wants to do.
Use software to automate that.
And then it's about optimization.
How do you optimize processes to drive greater productivity?
So this is not black magic,
this is not some far-off thing.
We're talking about basics,
better predictions, better automation, better optimization.
>> Now interestingly, you use the term black magic
because a lot of a AI is black box
and IBM's always made a point of
we're trying to make AI transparent.
You talked a lot about taking the bias out
or at least understanding when bias makes sense,
when it doesn't make sense.
Talk about the black box problem
and how you're addressing that.
>> Starts with one simple idea.
AI is not magic.
I say that over and over again.
This is just computer science.
Then you have to look at what are the components
inside the proverbial black box.
with Watson we have a few things
We've got tools for clients that want to build their own AI.
So think of it as a toolbox.
You can choose.
Do you want a hammer, and you want a screwdriver
or do you want a nail?
You go build your own AI using Watson.
We also have applications
so it's basically an end-user application
that puts AI into practice,
things like Watson Assistant to virtually,
create a virtual agent for customer service
or Watson Discovery or things like open pages with Watson
for governance, risk and compliance.
So AI for Watson is about tools.
You want to build your own applications.
You just want to consume an application.
Then we've also got embedded AI capabilities.
So you can pick up Watson
and put it inside of any software product in the world.
>> You also mentioned that Watson
was built with a lot of open source components
which a lot of people might not know.
What's behind Watson?
>> 85% of the work
that happens in Watson today is open-source.
Most people don't know that.
It's Python, it's deploying into tensor flow.
What we've done, where we focused our efforts is
how do you make AI easier to use?
So we've introduced Auto AI and to watch the studio
So if you're building models in Python,
you can use auto AI
to automate things like feature engineering,
algorithm selection, the kind of thing
that's hard for a lot of data scientist.
So we're now trying to create our own language.
We're using open-source but then we make that better
so that a data scientist can do their job better.
>> So again, come back to AI adoption.
We talked about three things; quality, trust and skills.
We talked about the data quality piece.
We talked about the black box challenge.
Let's talk about skills.
You mentioned there's a 250,000 person gap right now,
>> Yes.
>> Data science skills.
How is IBM approaching?
How are customers and IBM approaching closing that gap?
>> So think of that as in basic economic terms.
So we have a supply demand mismatch.
Massive demand for data scientists, not enough supply.
The way that we addressed that is two-fold.
One is we've created a team called Data Science Elite.
They've done a lot of work for the clients
that were on stage with me,
who help a client get to their first big win with AI.
It's that simple.
We go in for four to six weeks.
It's an elite team, it's not a long project.
We're going to get you there for your success.
Second piece is the other way
to solve demand and supply mismatch is through automation.
So I talked about Auto AI.
But we also do things like using AI
for building data catalogs,
metadata creation, data matching,
so making that data prep process automated through AI
can also help that supply demand mismatch.
So the way that you solve this
is we put skills on the field to help clients
and we do a lot of automation in software.
That's how we can help clients navigate this.
>> So the Data Science Elite team,
I love that concept because
we first picked up on it a couple of years ago at least.
It's one of the best freebies in the business.
>> It is.
>> But of course you're doing it with the customers
that you want to have deeper relationships with
and I'm sure it leads to follow on business.
What are the some of the things that you're most proud of
from the Data Science Elite team
that you might be able to share with us?
>> The client stories are amazing.
I talked in the keynote about origin stories.
Royal Bank of Scotland
automating 40% of their customers service now.
Customer stats going up 20%
because they put their customer service reps
on those hardest problems.
That's Data Science Elite
helping them get to a first success.
Now they scale it out.
Add Wonderman Thompson on stage,
part of the big WPP big advertising agency.
They're using AI to comb through customer records.
They're using auto AI,
that's the Data Science Elite team
that went in for literally four weeks
and gave them the confidence
that they could then do this on their own once we left.
We got countless examples
where this team has gone in for very short periods of time
and clients don't talk about this because they have to.
They talk about it because they're like,
"We can't believe what this team did."
So we're really excited by that.
>> The interesting thing about the RBS example to me, Rob
was that you basically applied AI
to remove a lot of these mundane tasks
that weren't really driving value for the organization.
And an RBS was able to shift the skill sets
to more strategic areas.
We always talk about that but I love the example.
So can you talk a little bit more
about really where that shift was.
What did they go from and what did they apply to
and how it impacted their businesses?
You said their improvement,
I think it was 20% improvement in NPS
but give us us a--
>> What I realized is that the inquiries they had coming in
were of two categories.
There were ones that were really easy,
there were ones that were really hard
and they were spreading those equally among their employees.
So what you get is a lot of unhappy customers
and then once they said we can automate all the easy stuff,
we can put all of our people on the hardest things,
customers sales shot through the roof.
Now what does a virtual agent do?
Let's decompose that a bit.
We have a thing called intent classification
as part of Watson Assistant
which is, it's a model that understands customer intent.
and it's trained based on the data
from a Royal Bank of Scotland.
So this model, after 30 days is not very good,
after 90 days it's really good,
after 180 days, it's excellent
because at the core of this is,
we understand the intent of customers engaging with them.
We use natural language processing.
It really becomes a virtual agent
that's done all in software
and you can only do that with things like AI.
>> And what is the role of the human element in that?
How does it interact with that virtual agent?
Is it sort of an attended agent or is it unattended?
What is that like?
>> So it's two pieces.
So for the easiest stuff, no humans needed.
We just go do that in software.
For the harder stuff,
we've now given the RBS customer service agent superpowers
because they've got Watson Assistant at their fingertips.
The hardest thing for a customer service agent
is normally finding the right data to solve a problem.
Watson Discovery is embedded in Watson Assistant.
So they can basically comb through all the data in the bank
to answer a question.
So we're giving their employees superpowers.
So on one hand, it's augmenting the humans.
In another case, we're just automating the stuff
the humans don't want to do in the first place.
>> I'm going to shift gears a little bit,
talk about Red Hat and OpenShift.
Obviously, huge acquisition last year,
34 billion dollars, next chapter kind of an IBM strategy.
A couple of things you're doing with with OpenShift.
Watson is now available on OpenShift.
So that means you're bringing Watson to the data.
I want to talk about that and then cloud pack for data
also on OpenShift
so what has that Red Hat acquisition done for you?
Obviously, you know a lot about M&A.
But now you're in the position of
you've got to take advantage of that
and you are taking advantage of that.
So give us an update on what you're doing there.
>> So look at the cloud market for a moment.
You've got around 600 billion dollars
of opportunity of traditional IT on-premise.
You got another 600 billion that's public clouds,
dedicated clouds and you got about 400 billion
that's private cloud.
So the cloud market is fragmented
between public, private and traditional IT.
The opportunity we saw was
if we can help clients integrate across all of those clouds
that's a great opportunity for us.
What Red Hat OpenShift is, it's a liberator.
It says write your application once, deploy them anywhere
because you build them on Red Hat OpenShift.
Now we've brought cloud pack for data,
our data platform onto Red Hat OpenShift certified on that.
Watson now runs on Red Hat OpenShift.
What that means is you can have the best data platform,
the best AI and you can run it on Google, AWS
as your own private cloud.
You can get the best AI with Watson from IBM
and run it in any of those places.
>> So the reason why that's so powerful
is because you're able to bring those capabilities
to the data without having to move the data around.
I think it was Jennifer showed an example
or no, maybe it was--
>> Rob: Jennifer, yeah.
>> It was Jennifer.
>> She was just showing virtualizing yes.
>> And so, the beauty of that
is I don't have to move any data.
Talk about the importance of not having to move that data
and then I want to understand what the client prerequisite is
to really take advantage of that?
>> This is one of the greatest inventions out of IBM research
in the last 10 years that hasn't gotten a lot of tension
which is data virtualization.
Data Federation, traditional federation
has been around forever.
The issue was it doesn't perform.
Our data virtualization performs 500% faster
than anything else in the market.
So what Jennifer showed in that demo was
I'm training a model and I'm going to virtualize the data set
from Red Shift on AWS and on-premise repository
a MySQL database.
We don't have to move the data.
We just virtualize those data sets into cloud packs for data
and then we can train the model in one place.
Like this is actually breaking down data silos
that exist in every organization and it's really unique.
>> Yeah, it was a very cool demo
because what she did is she was pulling data
from different data stores,
doing joins, it was a healthcare application,
really trying to understand where the bias was,
peeling the onion.
>> Right.
>> Is it bias?
Sometimes bias is okay, right?
You just got to know whether or not it's actionable
and so that was very cool
without having to move any of the data.
What is the prerequisite for clients?
What do they have to do to take advantage of this?
>> Start using cloud pak for data.
We've got something on the web
called cloud pak experiences.
Anybody can go try this in less than two minutes.
I just say go try it
because cloud pak for data will just insert
right on any public cloud you're running
or in your private cloud environment.
You just point to the sources
and it will instantly begin to start to create
what we call schema foldings.
So a schema version of the schema from your source
writing cloud pak for data,
this is like instant access to your data.
>> It sounds like magic.
>> It is. >> All right.
Okay.
Last question, what are the big take aways
you want people to leave this event with?
>> We are trying to inspire clients to give AI a shot.
Adoption is four to 10%.
For what is the largest economic opportunity
we'll ever see in our lives,
that's not an acceptable rate of adoption.
So we're encouraging everybody go try things.
Don't do one AI experiment.
Do a hundred AI experiments in the next year.
And if you do a hundred, 50 of them probably won't work.
This is where you have to change
the cultural idea aspect to come to do it.
Be prepared that half of them aren't going to work
but then for the 50 that do work
then you double down, then you triple down.
Everybody will be successful in their AI
if they have this iterative mindset.
>> Yeah, and I think with cloud it's very inexpensive
to actually do those experiments.
Rob Thomas, thanks so much for coming on theCUBE.
It was great to see you. >> Great to see you.
>> All right, keep right there.
We'll be back with our next guest
right after this short break.
We're here from Miami at the IBM AI Data Forum.
We'll be right back.