>> Narrator: Live from Miami, Florida, it's The Cube.
Covering IBM's Data in AI Forum, brought to you by IBM.
>> Welcome back to Miami, everybody.
You're watching The Cube, the leader in live tech coverage.
We're here covering the IBM Data and AI Forum.
Scott Buckles is here to my right.
He's the business unit executive at IBM
and long time Cube alum, Daniel Hernandez is
the Vice President of Data and AI group.
Good to see you guys, thanks for coming on.
>> Thanks for having us. >> Good to see you.
>> You're very welcome.
We're going to talk about data ops,
kind of accelerating the journey to AI around data ops,
but what is data ops and how does it fit into AI?
Daniel, we'll start with you.
>> There's no AI without data.
You've got data science to help you build AI.
You've got dev ops to help you build apps.
You've got nothing to basically help you
prepare data for AI.
Data ops is the equivalent of dev ops,
but for delivering AI ready data.
>> So, how are you, Scott, dealing with this topic with
customers, is it resonating?
Are they leaning into it, or are they saying, "what?"
>> No, it's absolutely resonating.
We have a lot of customers that are doing a lot
of good things on the data science side.
But, trying to get the right data at the right people,
and do it fast, is a huge problem.
They're finding they're spending
too much time prepping data,
getting the data into the models,
and they're not spending enough time failing fast
with some of those models,
or getting the models that they need to put in production
into production fast enough.
So, this absolutely resonates with them
because I think it's been confusing for a long time.
>> So, AI's scary to a lot of people, right?
It's a complicated situation, right?
And how do you make it less scary?
>> Talk about problems that can be solved with it, basically.
You want a better customer experience
in your contact center,
you want a similarly amazing experience when they're
interacting with you on the web.
How do you do that?
AI is simply a way to get it done,
and a way to get it done exceptionally well.
So, that's how I like to talk about it.
I don't start with here's AI, tell me what problems
you can solve.
Here are the problems you've got,
and where appropriate, here's where AI can help.
>> So what are some of your favorite problems
that you guys are solving with customers.
>> Customer and employee care,
which, basically,
is any business that does business has customers.
Customer and employee care are huge a problem space.
Catching bad people,
financial crimes investigation is a huge one.
Fraud, KYC AML as an example.
>> National security, things like that, right?
>> Yeah.
>> You spend all your time with customers, what else?
>> Well, customer experience is probably
the one that we're seeing the most.
The other is being more efficient.
Helping businesses solve those problems quicker, faster.
Try to find new avenues for revenue.
How to cut costs out of their organization,
out of their run time.
Those are the ones that we see the most.
>> So when you say customer experience,
immediately chat bots jumps into my head.
But I know we're talking more than, sort of a,
transcends chat bots,
but double click on customer experience,
how are people applying machine intelligence
to improve customer experience?
>> Well, when I think of it,
I think about if you call in to Delta,
and you have one bad experience,
or your airline, whatever that airline may be,
that that customer experience could lead to
losing that customer forever,
and there used to be an old adage that
you have one bad experience and you tell
10 people about it, you have a good one,
and you tell one person, or two peoples.
So, getting the right data to have that experience
is where it becomes a challenge and we've seen instances
where customers, or excuse me,
organizations are literally trying to find the data
on the screen while the customer is on hold.
So, they're saying, "can I put you on hold?"
and they're trying to go out and find it.
So, being able to automate finding that data,
getting it in the right hands, to the right people,
at the right time, in moment's notice,
is a great opportunity for AI and machine learning,
and that's an example of how we do it.
>> So, from a technical standpoint, Daniel,
you guys have this IBM Cloud Pak for Data
that's going to magic data virtualization thing.
Let's take an example that Scott just gave us,
think of an airline.
I love my mobile app, I can do everything on my mobile app,
except there are certain things I can't do,
I have to go to the website.
There are certain things I have to do with e-commerce
that I have to go to the website that I can't do.
Sometimes watching a movie,
I can't order a movie from the app,
I have to go to website, the URL,
and order it there and put it on my watch list.
So, I presume that there's some technical debt
in each of those platforms,
and there's no way to get the data from here,
and the data from here talking to each other.
Is that the kind of problem that you're solving?
>> Yes, and in this particular case,
you're actually touching on what we mean by
customer and employee care everywhere.
The interaction you have on your phone
should be the same as the interaction
and the kind of response on the web,
which should be the same, if not better,
when you're talking to a human being.
How do you have the exceptional customer
and employee care, all channels.
Today, say the art is,
I've got a specific experience for my phone,
a specific experience for my website,
a specific, different experience in my contact center.
The whole work we're doing around Watson Assistant,
and it as a virtual assistant,
is to be that nervous system that underpins all channels,
and with Cloud Pak for Data, we can deliver it anywhere.
You want to run your contact center on an IBM Cloud?
Great.
You want to run it on Amazon, Azure, Google,
your own private center, or everything in between, great.
Cloud Pak for Data is how you get Watson Assistant,
the rest of Watson and our data stack anywhere you want,
so you can deliver that same consistent,
amazing experience, all channels, anywhere.
>> And I know the tone of my question was somewhat negative,
but I'm actually optimistic,
and there's a couple examples I'll give.
I remember Bill Belichick one time said,
"Agh, the weather, it can't ever get the weather right,"
this is probably five, six years ago.
Actually, they do pretty well with the weather
compared to 10 or 15 years ago.
The other is fraud detection.
In the last 10 years,
fraud detection has become so much better
in terms of just the time it takes to identify a fraud,
and the number of false positives.
Even in the last, I'd say, 12 to 18 months,
false positives are way down.
I think that's machine intelligence, right?
>> I mean, if you're using business rules,
they're not way down.
They're still way up.
If you're using more sophisticated techniques,
that are depending upon the operational data to be trained,
then they should be way down.
But, there is still a lot of these systems that are
based on old school business rules that can't keep up.
They're producing alerts that, in many cases,
are ignored, and because they're ignored,
you're susceptible to bad issues.
With, especially AI based techniques for fraud detection,
you better have good data to train this stuff,
which gets back to the whole data ops thing,
and training those with good data,
which data ops can help you get done.
>> And a key part to data ops is the people and the process.
It's not just about automating things
and automating the data to get it in the right place.
You have to modernize those business processes
and have the right skills to be able to do that as well.
Otherwise, you're not going to make the progress.
You're not going to reap the benefits.
>> Well, that was actually my next question.
What about the people and the process?
We were talking before, off camera, about our PA,
and he's saying "pave the cow path."
But sometimes you actually have to re-engineer
the process and you might not have the skill set.
So it's people and process, and then technology you lay in.
And we've always talked about this,
technology is always going to change.
Smart technologists will figure it out.
But, the people and the process, that's the hardest part.
What are you seeing in the field?
>> We see a lot of customers struggling
with the people and process side, for a variety of reasons.
The technology seems to be the focus,
but when we talk to customers,
we spend a lot of time saying,
"well, what needs to change in your business process
"when this happens?
"How do those business rules need to change
"so you don't get those false positives?"
Because it doesn't matter at the end of the day.
>> So, can we go back to the business rules thing?
So, it sounds like the business rules are
sort of an outdated, policy based,
rigid sort of structure that's enforced no matter what.
Versus machine intelligence,
which can interpret situations on the fly,
but can you add some color to that
and explain the difference between what you call
sort of business rules based versus AI based.
>> So the AI based ones, in this particular case,
probably classic statistical machine learning techniques,
to do something like know who I am, right?
My name is Danny Hernandez,
if you were to Google Danny Hernandez,
the number one search result is going to be a rapper.
There is a rapper that actually just recently came out,
he's not even that good, but he's a new one.
A statistical machine learning technique
would be able to say, "all right, given Daniel
"and the context information I know about him,
"when I look for Daniel Hernandez,
"and I supplement the identity with that
"contextual information, it means it's one of
"the six that work at IBM." Right?
>> Not the rapper.
>> Exactly.
I don't mind being matched with a rapper,
but match me with a good rapper.
>> All you've got to do is search Daniel Hernandez
and The Cube and you'll find him.
>> Ha, right. Bingo. Actually that's true.
So, in any case, the AI based techniques
basically allow you to
isolate who I am,
based on more features that you know about me,
so that you get me right.
Because if you can't even start there,
with whom are you transacting,
you're not going to have any hope of detecting fraud.
Either that, or you're going to get false positives
because you're going to associate me
with someone that I'm not,
and then it's just going to make me upset,
because when you should be transacting with me,
you're not because you're saying I'm someone I'm not.
>> So, that ties back to what we were saying before,
know you're customer and anti money laundering.
Which, of course, was big, and still is,
during the crypto craze.
Maybe crypto is not as crazy,
but that was a big deal when you had bitcoin
at whatever it was.
What are some practical applications for KYC AML
that you're seeing in the field today?
>> I think that what we see a lot of,
what we're applying in my business is
automating the discovery of data
and learning about the lineage of that data.
Where did it come from?
This was a problem that was really hard to solve
18 months ago, because it took a lot of man power to do it.
And as soon as you did it once, it was outdated.
So, we've recently released some capabilities
within Watson Knowledge Catalog that really help
automate that, so that as the data continues to grow,
and continues to change, as it always does,
that rather than having two, three hundred
business analysts or data stewards
trying to go figure that out,
machine learning can go do that for you.
>> So, all the big banks are glomming on to this?
>> Absolutely.
>> So think about any customer onboarding, right?
You better know who your customer is,
and you better have provisions around anti money laundering.
Otherwise, there's going to be some
very serious downside risk.
It's just one example of many, for sure.
>> Let's talk about some of the data challenges
because we talked a lot about digital, digital business,
I've always said the difference between a business
and a digital business is how they use data.
So, what are some of the challenging issues
that customers are facing, and particularly, incumbents,
Ginni Rometty used the term a couple of events ago,
and it might have even been World of Watson,
incumbent disruptors, maybe that was the first think,
which I thought was a very poignant term.
So, what are some of the data challenges
that these incumbents are facing,
and how is IMB helping solve them?
>> For us, one of them that we see is just understanding
where their data is.
There is a lot of dark data out there
that they haven't discovered yet.
And what impact is that having on their analytics,
what opportunities aren't they taking advantage of,
and what risks are they being exposed to
by that being out there.
Unstructured data is another big part of it as well.
Structured data is sort of the
easy answer to solving the data problem,
>> [Daniel Hernandez] But still hard.
>> But still hard.
Unstructured data is something that almost feels
like an afterthought a lot of times.
But, the opportunities and risks there
are equally, if not greater, to your business.
>> So yeah, what you're saying it's an afterthought,
because a lot of times people are saying, "that's too hard."
>> Scott Buckles: Right. >> Forget it.
>> Scott Buckles: Right. Right. Absolutely.
>> Because there's gold in them there hills, right?
>> Scott Buckles: Yeah, absolutely.
>> So, how does IBM help solve that problem?
Is it tooling, is it discovery tooling?
>> Well, yeah,
so we recently released a product called InstaScan,
that helps you to go discover unstructured data
within any cloud environment.
So, that was released a couple months ago,
that's a huge opportunity that we see where customers
can actually go and discover that dark data,
discover those risks.
And then combine that with some of the capabilities
that we do with structured data too,
so you have a holistic view of where your data is,
and start tying that together.
>> If I could add, any company that has any operating history
is going to have a pretty complex data environment.
Any company that wants to employ AI
has a fundamental choice.
Either I bring my AI to the data,
or I bring my data to the AI.
Our competition demand that you bring your data to the AI,
which is expensive, hard, often impossible.
So, if you have any desire to employ this stuff,
you had better take the I'm going to bring
my AI to the data approach,
or be prepared to deal with a multi-year
deployment for this stuff.
So, that principle difference in
how we think about the problem,
means that we can help our customers apply AI
to problem sets that they otherwise couldn't
because they would have to move.
And in many cases, they're just abandoning projects
all together because of that.
>> So, now we're starting to get into sort of data strategy.
So, let's talk about data strategy.
So, it starts with, I guess,
understanding the value of your data.
>> [Daniel Hernandez] Start with understanding what you got.
>> Yeah, what data do I have.
What's the value of that data?
How do I get to that data?
You just mentioned you can't have a strategy that says,
"okay, move all the data into some God box."
>> Good luck.
>> Yeah. That won't work.
So, do customers have coherent data strategies?
Are they formulating?
Where are we on that maturity curve?
>> Absolutely, I think the advent of the CDO role,
as the Chief Data Officer role,
has really helped bring the awareness
that you have to have that enterprise data strategy.
>> So, that's a sign.
If there's a CDO in the house.
>> There's someone working on enterprise, yeah, absolutely.
>> So, it's really their role, the CDO's role,
to construct the data strategy.
>> Absolutely.
And one of the challenges that we see, though, in that,
is that because it is a new role,
is like going back to Daniel's
historical operational stuff, right?
There's a lot of things you have to sort out
within your data strategy of who owns the data, right?
Regardless of where it sits within an enterprise,
and how are you applying that strategy
to those data assets across the business.
And that's not an easy challenge.
That goes back to the people process side of it.
>> Well, right. I bet you if I asked Jim Cavanaugh
what's IBM's data strategy,
I bet you he'd have a really coherent answer.
But I bet you if I asked Scott Hebner,
the CMO of the data and AI group,
I bet you I'd get a somewhat different answer.
And so, there's multiple data strategies,
but I guess it's (mumbles) job to make sure
that they are coherent and tie in, right?
>> Absolutely.
>> Am I getting this?
>> Absolutely.
>> Quick study.
>> So, what's IBM's data strategy?
(laughs)
>> Data is good.
Bring AI to the data.
>> Look, I mean,
data and AI, that's the name of the business,
that's the name of the portfolio
that represents our philosophy.
No AI without data, increasingly,
not a lot of value of data without AI.
We have to help our customers understand this,
that's a skill, education, point of view problem,
and we have to deliver technology
that actually works in the wild, in their environment,
not as we want them to be, but as they are.
Which is often messy.
But I think that's our fun.
It's the reason we've been here for a while.
>> All right, I'll give you guys a last word, we got to run,
but both Scott and Daniel, take aways from the event today,
things that you're excited about, things that you learned.
Just give us the bumper sticker.
>> For me, you talk about whether people recognize
the need for a data strategy in their role.
For me, it's people being pumped about that,
being excited about it, recognizing it,
and wanting to solve those problems
and leverage the capabilities that are out there.
>> We've seen a lot of that today.
>> Absolutely.
And we're at a great time and place
where the capabilities and the technologies
with machine learning and AI are applicable and real,
that they're solving those problems.
So, I think that gets everybody excited, which is cool.
>> Bring it home, Daniel.
>> Excitement, a ton of experimentation with AI,
some real issues that are getting in the way
of full-scale deployments, a methodology data ops,
to deal with those real hardcore data problems
in the enterprise, resonating,
a technology stack that allows you to implement that
as a company is, through Cloud Pak for Data,
no matter where they want to run is what they need,
and I'm happy we're able to deliver it to them.
>> Great. Great segment, guys.
Thanks for coming.
>> Awesome. Thank you.
>> Data, applying AI to that data, scaling with the cloud,
that's the innovation cocktail
that we talk about all the time on The Cube.
Scaling data your way, this is Dave Vellante
and we're in Miami at the AI and Data Forum,
brought to you by IBM.
We'll be right back right after this short break.
(upbeat music)