>> Announcer: Live from New York City, it's theCUBE.
Covering IBM Data Science For All.
Brought to you by IBM.
>> Welcome to the big apple, John Walls
and Dave Vellante here on theCUBE
we are live at IBM's Data Science For All.
Going to be here throughout the day with a big panel
discussion wrapping up our day.
So be sure to stick around all day long on theCUBe for that.
Dave always good to be here in New York is it not?
>> Well you know it's been kind of the data science
weeks, months, last week we're in Boston at an event
with the chief data officer conference.
All the Boston Datarati were there,
bring it all down to New York City
getting hardcore really with data science
so it's from chief data officer
to the hardcore data scientists.
>> The CDO, hot term right now.
Daniel Hernandez now joins as our first guest here at
Data Science For All.
Who's a VP of IBM Analytics, good to see you.
David thanks for being with us.
>> Pleasure.
>> Alright well give us first off your take,
let's just step back high level here.
Data science it's certainly been evolving for decades
if you will.
First off how do you define it today?
And then just from the IBM side of the fence,
how do you see it in terms of how businesses should be
integrating this into their mindset.
>> So the way I describe data science simply to my clients
is it's using the scientific method to answer questions
or deliver insights.
It's kind of that simple.
Or answering questions quantitatively.
So it's a methodology, it's a discipline,
it's not necessarily tools.
So that's kind of the way I approach describing what it is.
>> Okay and then from the IBM side of the fence,
in terms of how wide of a net are you casting
these days I assume it's as big as you can
get your arms out.
>> So when you think about any particular problem
that's a data science problem,
you need certain capabilities.
We happen to deliver those capabilities.
You need the ability to collect, store, manage,
any and all data.
You need the ability to organize that data
so you can discover it and protect it.
You got to be able to analyze it.
Automate the mundane, explain the past, predict the future.
Those are the capabilities you need to do data science.
We deliver a portfolio of it.
Including on the analyze part of our portfolio,
our data science tools that we would declare as such.
>> So data science for all is very aspirational,
and when you guys made the announcement
of the Watson data platform last fall,
one of the things that you focused on was collaboration
between data scientists, data engineers,
quality engineers, application development,
the whole sort of chain.
And you made the point that most of the time
that data scientists spend is on wrangling data.
You're trying to attack that problem,
and you're trying to break down the stovepipes
between those roles that I just mentioned.
All that has to happen before you can actually
have data science for all.
I mean that's just data science
for all hardcore data people.
Where are we in terms of sort of the progress
that your clients have made in that regard?
>> So you know, I would say there's two majors vectors
of progress we've made.
So if you want data science for all you need to be able
to address people that know how to code
and people that don't know how to code.
So if you consider kind the history of IBM
in the data science space especially in SPSS,
which has been around for decades.
We're mastering and solving data science problems
for non-coders.
The data science experience really started with
embracing coders.
Developers that grew up in open source,
that lived and learned Jupiter or Python
and were more comfortable there.
And integration of these is kind of our focus.
So that's one aspect.
Serving the needs of people that know how to code
and don't in the kind of data science role.
And then for all means supporting an entire analytics
life cycle from collecting the data you need
in order to answer the question that you're trying
to answer to organizing that information
once you've collected so you can discover it
inside of tools like our own data science experience
and SPSS, and then of course the set of tools
that around exploratory analytics.
All integrated so that you can do
that end to end life cycle.
So where clients are, I think they're getting certainly
much more sophisticated in understanding that.
You know most people have approached data science
as a tool problem, as a data prep problem.
It's a life cycle problem.
And that's kind of how we're thinking about it.
We're thinking about it in terms of,
alright if our job is answer questions,
delivering insights through scientific methods,
how do we decompose that problem to a set of things
that people need to get the job done,
serving the individuals that have to work together.
>> And when you think about, go back to the days
where it's sort of the data warehouse was king.
Something we talked about in Boston last week,
it used to be the data warehouse was king,
now it's the process is much more important.
But it was very few people had access to that data,
you had the elapsed time of getting answers,
and the inflexibility of the systems.
Has that changed and to what degree has it changed?
>> I think if you were to go ask anybody in business
whether or not they have all the data they need
to do their job, they would say no.
Why?
So we've invested in EDW's, we've invested in Hadoop.
In part sometimes, the problem might be,
I just don't have the data.
Most of the time it is I have the data
I just don't know where it is.
So there's a pretty significant issue
on data discoverability,
and it's important that I might have data
in my operational systems,
I might have data inside my EDW,
I don't have everything inside my EDW,
I've standed up one or more data lakes,
and to solve my problem like customer segmentation
I have data everywhere,
how do I find and bring it in?
>> That seems like that should be
a fundamental consideration, right?
If you're going to gather this much more information,
make it accessible to people.
And if you don't, it's a big flaw, it's a big gap is it not?
>> So yes, and I think part of the reason why
is because governance professionals which I am,
you know I spent quite a bit of time trying to solve
governance related problems.
We've been focusing pretty maniacally
on kind of the compliance, and the regulatory and security
related issues.
Like how do we keep people from going to jail,
how do we ensure regulatory compliance with things like
e-discovery, and records for instance.
And it just so happens the same discipline that you use,
even though in some cases lighter weight implementations,
are what you need in order to solve
this data discovery problem.
So the discourse around governance has been historically
about compliance, about regulations, about cost takeout,
not analytics.
And so a lot of our time certainly in R&D
is trying to solve that data discovery problem which is
how do I discover data using semantics that I have,
which as a regular user is not physical understandings
of my data,
and once I find it how am I assured that what I get
is what I should get so that it's,
I'm not subject to compliance related issues,
but also making the company more vulnerable to data breach.
>> Well so presumably part of that anyway
involves automating classification at the point of creation
or use, which is actually was a technical challenge
for a number of years.
Has that challenge been solved in your view?
>> I think machine learning is,
and in fact later on today I will be doing some
demonstrations of technology which will show
how we're making the application of machine learning easy,
inside of everything we do we're applying machine learning
techniques including to classification problems
that help us solve the problem.
So it could be we're automatically harvesting
technical metadata.
Are there business terms that could be automatically
extracted that don't require some data steward
to have to know and assert, right?
Or can we automatically suggest and still have the steward
for a case where I need a canonical data model,
and so I just don't want the machine to tell me everything,
but I want the machine to assist the data curation process.
We are not just exploring the application
of machine learning to solve
that data classification problem,
which historically was a manual one.
We're embedding that into most of the stuff
that we're doing.
Often you won't even know that we're doing it
behind the scenes.
>> So that means that often times
well the machine ideally are making the decisions
as to who gets access to what,
and is helping at least automate that governance,
but there's a natural friction that occurs.
And I wonder if you can talk about the balance sheet
if you will between information as an asset,
information as a liability.
You know the more restrictions you put on that information
the more it constricts you know a business user's ability.
So how do you see that shaping up?
>> I think it's often a people process problem,
not necessarily a technology problem.
I don't think as an industry we've figured it out.
Certainly a lot of our clients haven't figured out
that balance.
I mean there are plenty of conversation I'll go into
where I'll talk to a data science team
in a same line of business as a governance team
and what the data science team will tell us
is I'm building my own data catalog because
the stuff that the governance guys are doing
doesn't help me.
And the reason why it doesn't help me is because
it's they're going through this top down data curation
methodology and I've got a question,
I need to go find the data that's relevant.
I might not know what that is straight away.
So the CDO function in a lot of organizations
is helping bridge that.
So you'll see governance responsibilities line up
with the CDO with analytics.
And I think that's gone a long way to bridge that gaps.
But that conversation that I was just mentioning
is not unique to one or two customers.
Still a lot of customers are doing it.
Often customers that either haven't started a CDO practice
or are early days on it still.
>> So about that, because this is being introduced to
the workplace, a new concept right, fairly new CDOs.
As opposed to CIO or CTO, you know you have these other.
I mean how do you talk to your clients about
trying to broaden their perspective on that
and I guess emphasizing the need for them
to consider putting somebody of a sole responsibility,
or primary responsibility for their data.
Instead of just putting it lumping it in somewhere else.
>> So we happen to have one of the best CDO's
inside of our group which is like a handy tool for me.
So if I go into a client and it's purporting to be
a data science problem and it turns out they have
a data management issue around data discovery,
and they haven't yet figured out how to install
the process and people design to solve that particular issue
one of the key things I'll do is I'll bring in our CDO
and his delegates to have a conversation around them
on what we're doing inside of IBM,
what we're seeing in other customers
to help institute that practice inside of,
inside of their own organization.
We have forums like the CDO event in Boston last week,
which are designed to, you know it's not designed to be
here's what IBM can do in technology,
it's designed to say here's how the discipline impacts
your business and here's some best practices
you should apply.
So if ultimately I enter into those conversations
where I find that there's a need,
I typically am like alright,
I'm not going to,
tools are part of the problem but not the only issue,
let me bring someone in that can describe the people process
related issues which you got to get right.
In order for, in some cases to the tools
that I deliver to matter.
>> We had Seth Dobrin on last weekend in Boston,
and Inderpal Bhandari as well,
and he put forth this enterprise, sort of
data blueprint if you will.
CDO's are sort of--
>> Daniel: We're using that in IBM by the way.
>> Well this is the thing,
it's a really well thought out sort of structure
that seems to be trickling down to the divisions.
And so it's interesting to hear how you're applying
Seth's expertise.
I want to ask you about the Hortonworks relationship.
You guys have made a big deal about that this summer.
To me it was a no brainer.
Really what was the point of IBM having a Hadoop distro,
and Hortonworks gets this awesome distribution channel.
IBM has always had an affinity for open source
so that made sense there.
What's behind that relationship and how's it going?
>> It's going awesome.
Perhaps what we didn't say and we probably should have
focused on is the why customers care aspect.
There are three main by an occasion use cases
that customers are implementing where they are ready
even before the relationship.
They're asking IBM and Hortonworks to work together.
And so we were coming to the table working together
as partners before the deeper collaboration
we started in June.
The first one was bringing data science to Hadoop.
So running data science models,
doing data exploration where the data is.
And if you were to actually rewind the clock
on the IBM side and consider what we did with Hortonworks
in full consideration of what we did prior,
we brought the data science experience
and machine learning to Z in February.
The highest value transactional data was there.
The next step was bring data science to where the,
often for a lot of clients the second most valuable
set of data which is Hadoop.
So that was kind of part one.
And then we've kind of continued that
by bringing data science experience to the private cloud.
So that's one use case.
I got a lot data, I need to do data science,
I want to do it in resident,
I want to take advantage of the compute grid
I've already laid down,
and I want to take advantage of the performance benefits
and the integrated security and governance benefits
by having these things co-located.
That's kind of play one.
So we're bringing in data science experience
and HDP and HDF, which are the Hortonworks distributions
way closer together and optimized for each other.
Another component of that is not all data
is going to be in Hadoop as we were describing.
Some of it's in an EDW and that data science job
is going to require data outside of Hadoop,
and so we brought big SQL.
It was already supporting Hortonworks,
we just optimized the stack, and so
the combination of data science experience and big SQL
allows you to data science against a broader surface area
of data.
That's kind of play one.
Play two is I've got a EDW either for cost
or agility reasons I want to augment it
or some cases I might want to offload some data from it
to Hadoop.
And so the combination of Hortonworks plus big SQL
and our data integration technologies
are a perfect combination there
and we have plenty of clients using that
for kind of analytics offloading from EDW.
And then the third piece that we're doing
quite a bit of engineering, go-to-market work around
is govern data lakes.
So I want to enable self service analytics
throughout my enterprise.
I want self service analytics tools
to everyone that has access to it.
I want to make data available to them,
but I want that data to be governed so that
they can discover what's in it in the lake,
and whatever I give them is what they should have access to.
So those are the kind of the three tracks
that we're working with Hortonworks on,
and all of them are making stunning results
inside of clients.
>> And so that involves actually some serious engineering
as well-- >> Big time.
It's not just sort of a Barney deal or
just a pure go to market--
>> It's certainly more the market texture and just works.
>> Big picture down the road then.
Whatever challenges that you see on your side
of the business for the next 12 months.
What are you going to tackle,
what's that monster out there
that you think okay this is our next hurdle to get by.
>> I forgot if Rob said this before,
but you'll hear him say often and it's statistically proven,
the majority of the data that's available is not available
to be Googled, so it's behind a firewall.
And so we started last year with the Watson data platform
creating an integrating data analytics system.
What if customers have data that's on-prem that they
want to take advantage of,
what if they're not ready for the public cloud.
How do we deliver public benefits to them
when they want to run that workload behind a firewall.
So we're doing a significant amount of engineering,
really starting with the work that we did
on a data science experience.
Bringing it behind the firewall,
but still delivering similar benefits
you would expect if you're delivering it
in the public cloud.
A major advancement that IBM made is run IBM cloud private.
I don't know if you guys are familiar
with that announcement.
We made, I think it's already two weeks ago.
So it's a (mumbles) foundation on top of which we have
micro services on top of which our stack
is going to be made available.
So when I think of kind of where the future is,
you know our customers ultimately we believe
want to run data and analytic workloads
in the public cloud.
How do we get them there considering they're not there now
in a stepwise fashion that is sensible economically
project management-wise culturally.
Without having them having to wait.
That's kind of big picture,
kind of a big problem space we're spending
considerable time thinking through.
>> We've been talking a lot about this on theCUBE
in the last several months or even years
is people realize they can't just reform their business
and stuff into the cloud.
They have to bring the cloud model to their data.
Wherever that data exists.
If it's in the cloud, great.
And the key there is you got to have a capability
and a solution that substantially mimics
that public cloud experience.
That's kind of what you guys are focused on.
>> What I tell clients is,
if you're ready for certain workloads,
especially green field workloads,
and the capability exists in a public cloud,
you should go there now.
Because you're going to want to go there eventually anyway.
And if not, then a vendor like IBM helps you take
advantage of that behind a firewall,
often in form facts that are ready to go.
The integrated analytics system,
I don't know if you're familiar with that.
That includes our super advanced data warehouse,
the data science experience,
our query federation technology powered by big SQL,
all in a form factor that's ready to go.
You get started there for data and data science workloads
and that's a major step in the direction
to the public cloud.
>> Alright well Daniel thank you for the time,
we appreciate that.
We didn't get to touch at all on baseball,
but next time right?
>> Daniel: Go Cubbies.
(laughing)
>> Sore spot with me but it's alright, go Cubbies.
Alright Daniel Hernandez from IBM,
back with more here from Data Science For All.
IBM's event here in Manhattan.
Back with more in theCUBE in just a bit.
(electronic music)