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it's theCUBE, covering IBM's Data and AI Forum.
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>> Welcome back to the port of Miami, everybody.
We're here at the InterContinental Hotel.
You're watching theCUBE, the leader in live tech coverage.
Seth Dobrin is here.
He's the vice president of Data and AI,
and the chief data officer of
Cloud and Cognitive Software at IBM.
Seth, good to see you again.
>> Yeah, good to see you, Dave.
Thanks for having me.
>> At the data and AI forum, #dataai.
It's amazing here, 1,700 people,
everybody's got a hands-on appetite for learning.
>> Seth: Yeah.
>> What do you see out in the marketplace?
What's new since we last talked?
>> Well, so, I think if you look at
some of the things that are
really needed in the marketplace,
it's really been around filling the skill shortage
and how do you operationalize and industrialize your AI.
And so there's been a real need for
ways to get more productivity out of your data scientists.
Not necessarily replace them,
but how do you get more productivity.
And we just released a few months ago
something called AutoAI,
which is probably the only tool out there
that automates the end-to-end pipeline,
automates 80% of the work on the end-to-end pipeline,
but isn't a black box, it actually kicks out code
so your data scientists can get then take it,
optimize it further, and understand it
and really feel more comfortable about it.
>> So it's kind of AI for AI, is that right?
>> That's exactly what it is.
AI for AI.
>> So how does that work?
So you're applying machine intelligent
to data to make AI more productive,
pick algorithms, best fit?
>> Yeah, so it does, basically you feed it your data,
and it identifies the features that are important.
It does feature engineering for you.
It does model selection for you.
It does hyperparameter tuning and optimization,
and it does deployment and also monitors for bias.
>> So what's the data scientist do?
(laughs)
>> The data scientist takes the code out the back end
and really, there's some tweaks that,
you know, the model, maybe the AutoAI
maybe not get it perfect.
And really customize it for the business
and the needs of the business that
the AutoAI may not understand.
>> So the data scientist then can, he or she can apply it
in a way that is unique to their business,
that essentially becomes their IP.
>> Seth: Right.
>> So it's not like generic AI for everybody.
It's customized by, and that's where
data scientists complain.
They don't have the time to do this,
because they're wrangling data.
>> Exactly.
And it was built in a combination from IBM research,
so it's a great assets out of IBM research.
Plus some Kaggle masters that work here at IBM
that really designed and optimized
the algorithm selection and things like that.
And then at the keynote today,
Wunderman Thompson was up there talking,
and this is probably one of the most impactful
use cases of AutoAI to date,
and it was also, you know, my former team,
the data science elite team was engaged.
But Wunderman Thompson had this problem
where they had like 17,000 features in their data sets.
And what they wanted to do was
they wanted to be able to have a custom solution
for their customers, and so every time
they get a customer, they have to have a data scientist
that would sit down and figure out
what are the right features and how do they engineer them
for this customer.
It was an intractable problem for them.
You know, the person from Wunderman Thompson
that presented today said he's been
trying to solve this problem for eight years.
AutoAI plus the data science elite team
solved the forum in two months,
and after that two months it went right into production.
So in this case, AutoAI isn't doing the whole pipeline,
it's helping them identify the features
and engineering the features that are important
and giving them a head start on the model.
>> What's the acquisition model for AutoAI?
Is it a licensed software product, is it a SaaS,
how do I get it?
>> It's part of Cloud Pak for Data,
and it's available on IBM Cloud, so it's on IBM Cloud.
You can use it pay-per-use,
or you get a license as part of Watson Studio on IBM Cloud.
If you invest in Cloud Pak for Data,
it can be a perpetual license
or a committed term license,
which is essentially a SaaS model.
>> It's essentially a feature add-on of Cloud Pak for Data?
>> Seth: It's part of Cloud Pak for Data, yep.
>> You're saying it can be usage-based,
so that's key, right?
>> It's consumption-based.
So Cloud Pak for Data is all consumption-based.
>> So people want to use AI for competitive advantage.
I said at my open that, you know,
we're not marching to the cadence of Moore's Law
in this industry anymore.
It's a combination of data, AI, and then cloud for scale.
So people want competitive advantage.
You've talked about some things
that folks are doing to gain that competitive advantage.
But at the same time, we heard from Rob Thomas
that only about four to 10% penetration for AI.
What are the key blockers that you see,
and how are you knocking them down?
>> Well, I think there's a number of key blockers.
So one is access to data, right?
Companies have tons of data,
but being able to even know what data is there,
being able to pull it all together,
and being able to do it in a way
that is compliant with regulation,
cause you can't do AI in a vacuum.
You have to do it in the context of
ever-increasing regulation like GDPR and CCPA
and all these other data privacy regulations
that are popping up.
So that's really two.
So access to data and regulation can be blockers.
The second one, or the third one is really
access to appropriate skills,
which we talked a little bit about,
and how do you retrain, or how do you
upscale the talent you have,
and then how do you actually bring in new talent
that can execute what you want?
And then sometimes in some companies
it's a lack of strategy with appropriate measurement, right?
So what is your AI strategy
and how are you going to measure success?
And you and I have talked about this
on Cube before where it's, you got to measure your success
in dollars and cents, right?
Cost savings, net new revenue,
that's really all your CFOs care about.
That's how you have to be able to measure
and monitor your success.
>> Yeah, so, that last one is probably
where most organizations start.
Let's prioritize the use cases that will give us
the best bang for the buck.
And then the business guys probably get really excited
and say okay, let's go,
but to truly operationalize it,
you got to worry about these other things.
You know, the compliance issues,
and you got to have the skill sets to scale.
>> Yeah, and sometimes that's actually,
the first thing you said is sometimes a mistake.
So focusing on the one that's got the most bang for the buck
is not necessarily the best place to start
for a couple of reasons.
So one is you may not have the right data.
It may not be available,
it may not be governed properly, number one.
Number two, the business that you're building it for
may not be ready to consume it, right?
They may not be either bought in
or the processes need to change so much,
or something like that that it's not going to get used.
And you can build the best AI in the world.
If it doesn't get used, it creates zero value, right?
And so you really want to focus on
for the first couple of projects,
what are the one that we can deliver the best value,
not necessarily the most value, but the best value
in the shortest amount of time
and ensure that it gets into production?
Cause especially when you're starting off,
if you don't show adoption, people are going to lose interest.
>> Seth, what are you seeing in terms of experimentation now
in the customer base?
You know, when you talk to buyers
and you talk about, you know,
you look at the IT spending surveys,
people are concerned about, you know, tariffs,
the trade wars, (laughs) the 2020 election.
They're being a little bit cautious.
But in the last two or three years
have been a lot of experimentation going on.
And a big part of that is AI and machine learning.
What are you seeing in terms of that experimentation
turning into actually production
projects that we can learn from
and maybe do some new experiments?
>> Yeah, and I think it depends
on how you're doing the experiments.
I think there's kind of academic experimentation
where you have data science teams
that come work on cool stuff
that may or may not have business value
and may or may not be implemented, right?
They just kind of latch on.
The business isn't really involved.
They latch on, they do projects.
And I think that's actually bad experimentation
if you let that run your program.
The good experimentation is when you start
having a strategy, you identify the use cases
you want to go after, and you experiment
by leveraging agile to deliver these methodologist.
You deliver value in two week sprints,
and you can start delivering value quickly,
you know, in the case of Wunderman Thompson, again,
eight weeks, four sprints, they got value.
That was an experiment, right?
That was an experiment because it was done
agile methodologies, using good coding practices,
using good, you know, kind of design up front practices.
They were able to take that
and put it right into production.
If you're doing experimentation,
you have to rewrite your code at the end,
and it's a waste of time.
>> So to your earlier point, the moon shots
are oftentimes can be too risky,
and if you blow it on a moon shot,
it could set you back, you know, years.
So you got to be careful, pick your spots,
pick ones that are maybe representative
but are maybe lower-risk, and apply agile methodologies,
get a quick return, learn, develop those skills,
and then build up to the moon shots.
>> Yeah, or you break that moon shot down
into consumable pieces, right?
Cause the moon shot may take you two years to get to,
but maybe there are subcomponents of that moon shot
that you could deliver in three, four months,
and you start delivering those
and you work up to the moon shot.
>> I always like to ask that dog fooding,
and people, you know, vendors don't like when I say--
>> Champagning, champagning.
>> Call it sipping your own champagne.
What have you guys done internally?
When we first met, it was an, I think a snowy day in Boston,
right at the Spark Summit years ago.
And you did a big career switch,
and it's obviously working out for you.
But what are some of the things,
and you were in part brought in
to help IBM internally, as well as Inderpal,
help IBM really become data-driven internally.
>> Seth: Yeah.
>> How has that gone, what have you learned,
and how are you taking that to customers?
>> Yeah, so I was hired three years ago now.
I believe it was that long.
To lead our internal transformation.
Over the last couple years I got,
I don't want to say distracted.
There were really important business things
I needed to focus on like GDPR
and helping our customers get up and running
with data science and AI,
building a data science elite team.
So as of a couple months ago, I'm back, you know,
almost entirely focused on our internal transformation.
And you know, it's really about making sure
that we use data and AI to make appropriate decisions.
And so now we have, you know,
we have an app on our phone that leverages Cognos Analytics,
where at any point Ginni Rometty or Rob Thomas
or Arvind Krishna can pull up and look in what we call EPN,
which is Enterprise Performance Management,
and understand where the business is, right?
What did we do in third quarter which just wrapped up?
What's the pipeline for fourth quarter?
And it's at your fingertips.
We're working on revamping our planning cycle.
So today planning has been done in Excel.
We're leveraging Planning Analytics,
which is a great planning and scenario planning tool,
that with the tip of a button really lets,
click of a button really lets you understand
how your business can perform in the future
and what things you need to do to get it to perform.
We're also looking across all of
cloud and cognitive software, which data and AI sits in,
and within each business unit
in cloud and cognitive software,
the sales teams do a great job of cross-sell upsell.
But there's a huge opportunity
of how do we cross-sell, upsell,
across the five different businesses
that live inside of cloud and cognitive software?
So data and AI, hybrid cloud integration,
IBM Cloud, cognitive applications and IBM security,
there's a lot of potential interplay
that our customers do across there
and providing AI that helps the salespeople
understand when they can create
more value for our customers.
>> You know, it's interesting,
it's the 10th year of doing theCUBE,
and when we first started it was sort of
the beginning of the big data craze.
And a lot of people said oh, okay,
here's the disruption, crossing the chasm,
innovators dilemma, all that old stuff's going away,
all the new stuff's coming in.
But you mentioned, you know, Cognos on mobile.
This is the thing we learned is that
the key ingredients to data strategies
comprised the existing systems, right?
You don't just throw those out.
Those are the systems of record
that were the single version of the truth, if you will,
that people trusted.
You go back to trust, and all of this other stuff
built up around it, which kind of created dissonance.
>> Seth: Yeah.
>> And so it sounds like one of the initiatives
that you at IBM have been working on
is really bringing in the new pieces,
modernizing sort of the existing
so that you've got sort of consistent data sets
that people can work off of.
>> Yeah, and one of the capabilities
that really has enabled this transformation
in the last six months for us internally
and for our clients, inside of Cloud Pak for Data,
we have this capability called IBM data virtualization,
which we have all these independent sources of truth
to some extent, you know,
and then we have all these other data sources
that may or may not be as trusted.
But to be able to bring them together,
literally with the click of a button.
You drop your data sources in,
the AI within data virtualization
actually identifies keys across the different things
so you can link your data.
You look at it, you check it,
and it really enables you to do this at scale.
And all you need to do is say point it at the data.
Here's the IP address of where the data lives,
and it will bring that in and help you connect it.
>> So you mentioned variances in data quality
and the consumer of the data has to have trust in that data.
Can you use machine intelligence and AI
to sort of give you data confidence meter, if you will?
>> Yeah, so there's two things that we
use for data confidence.
I call it dodginess factor, right?
Understanding what the dodginess factor is of the data.
So we definitely leverage AI.
So AI, if you have a data dictionary
and you have metadata, the AI can understand data quality
and it can also look at what your data stewards do,
and it can do some of the remediation
of the data quality issues.
But we all, and Watson Knowledge Catalog,
which again is in Cloud Pak for Data,
we also have the ability to vote up and vote down data.
So as the team is using data internally,
if there's a data set that had a, you know,
had a high data quality score but it wasn't really valuable,
it'll get voted down.
And when you search for data in the system,
it will sort it kind of like you do a search on the internet,
and it'll down rank that one depending on
how many down votes it got.
>> So it's a wisdom of the crowd type of thing?
>> It's a crowdsourcing combined wit the AI.
>> Has that, in your experience,
at all changed the dynamics of politics
within organizations?
In other words, I'm sure we've all been to a lot of meetings
where somebody puts forth some data,
and if the most senior person in the room
doesn't like the data, you know,
doesn't like the implication,
he or she will attack the data source,
and then the meeting's over.
And that might not necessarily
be the best decision for the organization.
>> Yeah, so I think it's maybe not
the up voting down voting that does that,
but it's things like the EPM tool
that I said we have here.
You know, there is a single source of truth
for our finance data.
It's on everyone's phone who needs access to it, right?
When you have a conversation about
how the company or the division or the business unit
is performing financially, it comes from EPM.
Whether it's in the Cognos app,
or whether it's in a dashboard,
a separate dashboard in Cognos
or is being fed into an AI that we're building.
This is the source of truth.
Similarly for product data,
how our individual product's performing, it comes from here.
So the conversation at these senior meetings
are no longer your data is different from my data.
I don't believe it.
You've eliminated that conversation.
This is the data, this is the only data.
Now you can have a conversation
about what's really important.
>> An adult conversation, okay,
now what are we going to do about it?
>> Yeah.
It's not a bickering about my data versus your data.
>> So what's next for you on, you know,
you've been pulled into a lot of different places.
Again, you started at IBM as an internal
transformation change agent.
You got pulled into a lot of customer situations
because you know what you're doing,
so sales guys want to drag you along
and help facilitate activity with clients.
What's new?
What's next for you?
>> So really, I've only been refocused
on the internal transformation for a couple months now.
So really, extending IBM, our cloud and cognitive software,
AI, data and AI strategy, and starting to
quickly implement some of these projects.
So just like I just said, we're starting projects
without even knowing what the prioritized list is.
Intuitively, this one's important,
the team's going to start working on it,
and one of them is an AI project
which is around cross-sell upsell that I mentioned
across the portfolio.
And the other one we just got done talking about
in the senior leadership meeting
for cloud and cognitive software,
how do we all work from a Cognos dashboard
instead of Excel data that's been exported, put into Excel?
The challenge with that is not
that people don't trust the data.
It's that if there's a question, you can't drill down.
So if there's a question about an Excel document
or a PowerPoint that's up there,
we'll get back, next meeting, in a month,
or in two weeks we'll have an email conversation about it.
If it's presented in a real live dashboard,
you can drill down and you can actually
answer questions in realtime.
The value of that is immense,
because now as a leadership team,
you can make a decision at that point
and decide what direction you're going to do based on data.
>> I said last question.
I have one more question.
You're a CDO, but you're a polymath,
and so my question is, what should people look for
in a chief data officer?
What are sort of the characteristics and attributes
given your experience now?
>> Yeah, so that's kind of a loaded question.
Cause there is no good single job description
for a chief data officer.
I don't even think there's a good solid
set of skillsets defined for a chief data officer,
and actually, as part of the chief data officer summits
that you guys attend, we're having sessions
with the chief data officers
kind of defining a curriculum for chief data officers
with our clients so that we can help build
the chief data officer of the future.
But if you look at quality,
so chief data officer is also a chief disruption officer.
So it needs to be someone who is really good at
and really good at driving change,
and really good at disrupting processes
and getting people excited about it.
Change is hard.
People don't like change.
How do you, you need someone who can get people
excited about change.
So that's one thing.
And depending on what industry you're in,
it could be if you're in financial
or a heavy regulated industry,
you want someone that understands governance.
And that's kind of what Gartner and other analysts call
a defensive CDO, very governance-focused.
And then you also have some CDOs
which I fit into this bucket,
which is a more offensive CDO,
which is how do you create value from data?
How do you save money?
How do you create net new revenue?
How do you create new business models
leveraging data and AI?
And now there's kind of a third type of CDO emerging,
which is CDO not as a cost center, but a CDO as a PNL.
How do you generate revenue for the business
directly from your CDO office?
>> Well I like that framework, right?
I mean you--
>> Seth: I can't take credit for it, that's Gartner.
>> Well yeah, but it's good, you know.
It's governance.
They call it defensive, and then offensive,
and then first time I met Inderpal,
he said look, you start with how does data
affect the monetization of my organization?
And that means making money or saving money.
Seth, thanks so much for coming on theCUBE.
It's great to see you again.
>> Yeah, thanks for having me.
It's good seeing you again. >> You're welcome.
All right, keep it right there everybody.
We'll be back at the IBM Data and AI Forum from Miami.
You're watching theCUBE.
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