>> Voiceover: Live from Las Vegas it's the Cube.
Covering IBM Think 2018.
Brought to you by IBM.
>> Welcome back to IBM Think 2018.
This is the Cube, the leader in live tech coverage.
My name is Dave Vellante and this is our third day
of wall-to-wall coverage of IBM Think.
Dinesh Nirmal is here, he's the Vice-President
of Analytics Development at IBM.
Dinesh, great to see you again.
>> I know.
>> We just say each other a couple of weeks ago.
>> I know, in New York.
>> Yeah and, of course, in Big Data SV
>> Right.
>> Over at the Strata Conference. So, great to see you again.
>> Well, Thank you.
>> A little different venue here. We had real intimate
in New York City and in San Jose.
>> I know, I know.
>> Massive. What are your thoughts on bringing
all the clients together like this?
>> I mean, it's great because we have combined
all the conferences into one, which obviously helps
because the message is very clear to our clients
on what we are doing end-to-end, but the feedback
has been tremendous. I mean, you know, very positive.
>> What has the feedback been like in terms
of how you guys are making progress
in the analytics group? What are they like?
What are they asking you for more of?
>> Right. So on the analytics side, the data is growing
you know, by terabytes a day and the questions is
how do they create insights into this massive amount of data
that they have in their premise or on Cloud.
So we have been working to make sure that how can we
build the tools to enable our customers to create insights
whether the data is on private cloud, public, or hybrid.
And that's a very unique valid proposition
that we bring to our customers.
Regardless of where your data is, we can help you
whether it's cloud, private, or hybrid.
>> Well so, we're living in this multi-petabyte world now.
Like overnight it became multi-petabyte.
And one of the challenges of course people have is
not only how do you deal with that volume of data, but
how do I act on it and get insights quickly.
How do I operationalize it? So maybe you can talk
about some of the challenges of operationalizing data.
>> Right. So, when I look at machine learning,
there is three D's I always say and you know,
the first D is the data, the development of the model,
and the deployment of the model.
When I talk about operationalization, especially
the deployment piece, is the one that gets
the most challenging for our enterprise customers.
Once you clean the data and you build the model
how do you take that model and you bring it your
existing infrastructure. I mean, you know,
look at your large enterprises. Right?
I mean, you know, they've been around for decades.
So they have third party software.
They have existing infrastructure.
They have legacy systems.
>> Dave: A zillion data marks and data warehouses
>> Data marks, so into all of that, how do you infuse
machine learning, becomes very challenging.
I met with the CTO of a major bank a few months ago,
and his statement kind of stands out to me.
Where he said, "Dinesh, it only took us three weeks
to build the model. It's been 11 months,
we still haven't deployed it".
So that's the challenge our customers face and
that's where we bring in the skillset.
Not just the tools but we bring the skills
to enable and bring that into production.
>> So is that the challenge? It's the skillsets
or is it the organizational inertia around
well I don't have the time to do that now
because I've got to get this report out or ...
>> Dinesh: Right
Maybe you can talk about that a little.
Right. So that is always there. Right?
I mean, because once a priority is set
obviously the different challenges pull you
in different directions, so every organization faces that
to a large extent. But I think if you take
from a pure technical perspective,
I would say the challenge is two things.
Getting the right tools, getting the right skills.
So, with IBM, what we are focusing is how do we
bring the right tools, regardless of the form factor
you have, whether Cloud, Private Cloud, Hybrid Cloud,
and then how do we bring the right skills into it.
So this week we announce the data science lead team,
who can come in and help you with building models.
Looking at the use cases. Should we
be using vanilla machine learning or
should we be using deep learning.
All those things and how do we bring that model
into the production environment itself.
So I would say tools and skills.
>> So skills wise, in the skills there's at least two paths.
It's like the multi-tool athlete.
You've got the understanding of the tech.
>> Dinesh: Right.
>> You know, the tools, most technology people
say hey, I'll figure that out.
But then there's this data and digital
>> Right.
>> Skills. It's like this double deep skills
that is challenging. So you're saying you can help.
>> Right.
Sort of kick-start that and how does that work?
That sort of a services engagement? That's part of the ...
>> So, once you identify a use case,
the data science lead team can come in,
because they have the some level
of vertical knowledge of your industry.
They are very trained data scientists.
So they can come assess the use case.
Help you pick the algorithms to build it.
And then help you deploy, cleanse the data.
I mean, you bring up a very, very good point.
I mean, let's just look at the data, right.
The personas that's involved in data;
there is the data engineer, there's the data scientist,
there's the data worker, there's the data steward,
there's the CTO. So, that's just the data piece. Right?
I mean, there's so many personas that have to come together.
And that's why I said the skills a very critical piece
of all it, but also, working together.
The collaboration is important.
>> Alright, tell us more about IBM Cloud Private for data.
We've heard about IBM Cloud Private.
>> Danish: Right.
>> Cloud Private for Data is new. What's that all about?
>> Right, so we announced IBM Cloud Private for Data
this week and let me tell you, Dave,
this has been the most significant announcement
from an analytic perspective, probably in a while,
that we are getting such a positive response.
And, I will tell you why. So when you look at the platform,
our customers want three things.
One, they want to be able to build on top of the platform.
They want it to be open and they want it to be extensible.
And we have all three available.
The platform is built on Kubernetes.
So it's completely open, it's scalable, it's elastic.
All those features comes with it.
And then we put that end-to-end so you can
ingest the data, you can cleanse it or transform it.
You can build models or do deep analytics on it.
You can visualize it. So you can do everything
on the platform. So I'll take an example, like block chain,
for example, I mean you have, if I were to simplify it,
Right? You have the ledger, where you are, obviously,
putting your transactions in and then you have a
stay database where you are putting your latest
transactions in. The ledger's unstructured.
So, how do you, as that is getting filled,
How do you ingest that, transform it on the fly, and
be able to write into a persistent place and do analytics
on it. Only a platform can do with that kind of volume
of data. And that's where the data platform brings in,
which is very unique especially on the modern applications
that you want to do.
>> Yes, because if you don't have the platform ...
Let's unpack this a little bit.
You've got a series of bespoke products and then you've got,
just a lot of latency in terms of the elapsed times
to get to the insights.
>> Dinesh: Right.
>> Along the way you've got data consistency issues,
data quality
>> Dinesh: Right
>> maybe is variable. Things change.
>> Right. I mean, think about it, right.
If you don't have the platform
then you have side-load products.
So all of a sudden you've got to get a product
for your governance, your integration catalog.
You need to get a product for ingest.
You got to get a product for persistence.
You got to get a product for analytics.
You got to get a product for visualization.
And then you add the complexity of the different personas
working together between the multitude of products.
You have a mess in your hand at that point.
The platform solves that problem because it brings you
an integrated end-to-end solution that you can use
to build, for example, block chain in this case.
>> Okay, I've asked you this before,
but I've got to again and get it on record with Think.
So, a lot of people would hear that and say
Okay but it's a bunch of bespoke products
that IBM has taken they've put a UI layer on top
and called it a platform. So, what defines a platform
and how have you not done that?
>> Right.
>> And actually created the platform?
>> Right. So, we are taking the functionality
of the existing parts and that's what differentiates us.
Right? If you look at our governance portfolio,
I can sit here and very confidently say
no one can match that, so
>> Dave: Sure.
We obviously have that strength
>> Real Tap
>> Right, Real Tap. That we can bring.
So we are bringing the functionality.
But what we have done is we are taking the existing products
and disintegrated in to micro services
so we can make it cloud native.
So that is a huge step for us, right?
And then once you make that containerized and micro services
it fits into the open platform that we talked about before.
And now you have an end-to-end, well orchestrated pipeline
that's available in the platform that can scale
and be elastic as needed. So, it's not that we are
bringing the products, we are bringing
the functionality of it.
>> But I want to keep on this for a second,
so the experience for the user is different
if you microserviced what you say
because if you just did what I said and put a layer
a UI layer on top, you would be going
into these stovepipes and then cul-de-sac and then
coming back
>> Dinesh: Right.
And coming back. So, the development effort
for that must have been
>> Oh, yeah.
>> Fairly massive. You could have done the UI layer in,
you know, in months.
>> Right, right, right, then it is not really cloud native
way of doing it, right? I mean, if you're just changing
the UI and the experience, that's completely different.
What we have done is that we have completely
re-architected the underlying product suite
to meet the experience and the underlying platform layer.
So, what can happen? How long did this take?
What kind of resources did you have to throw
at this from a development standpoint?
>> So this has been in development for 12-18 months.
>> Yeah.
>> And we put, you know, a tremendous amount of resources
to make this happen. I mean, fortunately in our case
we have the depth, we have the functionality.
So it was about translating that into the cloud native
way of doing the app development.
>> So did you approach this with sort of multiple
small teams? Or was there a larger team?
What was your philosophy here?
>> It was multiple small teams, right.
So if you look at our governance portfolio
we got to take our governance catalog, rewrite that code.
If we look at our master data management portfolio,
we got to take, so it's multiple of small teams
with very core focus.
>> I mean, I ask you these questions because I think
it adds credibility to the claims that you're making
about we have a platform not a series of bespoke products.
>> Right and we demoed it. Actually tomorrow at 11,
I'm going to deep dive into the architecture
of the whole platform itself. How we built it.
What are the components we used and I'm going to demo it.
So the code is up and running and we are going
to put it out there into Cube for everybody to go us it.
>> At Mandalay Bay, where is that demo?
>> It's in Mandalay Bay, yeah.
>> Okay.
>> We have a session at 11:30.
>> Talk more about machine learning and how you've
infused machine learning into the portfolio.
>> Right. So, every part of our product portfolio has
machinery so, I'll take two examples.
One is DB2. So today, DB2 Optimizer is a cost-based
optimizer. We have taken the optimizer and infused
machine learning into it to say, you know, based on the
query that's coming in take the right access path,
predict the right access path and take it.
And that has been such a great experience
because we are seeing 30-50 percent performance
improvement in most of the queries that we run
through the machinery. So that's one.
The other one is the classification, so let's say,
you have a business term and you want to classify.
So, if you have a zip code, we can use in our catalog
to say there's an 80% chance this particular number
is a zip code and then it can learn over time, if you
tell it, no that's not a zip code, that's a post code
in Canada. So the next time you put that in there it
has learned. So every product we have infused
machine learning and that's our goal is
to become completely a cognitive platform pretty soon.
I mean, you know, so that has also been a tremendous
piece of work that we're doing.
>> So what can we expect? I mean, you guys are moving fast.
>> Yeah.
>> We've seen you go from sort of a bespoke product
company division to this platform division.
Injecting now machine learning into the equation.
You're bringing in new technologies like block chain,
which you're able to do because you have a platform.
>> Right.
>> What should we expect in terms of the pace and
the types of innovations that we could see going forward?
What could you share with us without divulging secrets?
>> Right. So, from a product perspective we want
to infuse cognitive machine learning into every aspect
of the product. So, we don't want to, we don't want
our customers calling us, telling there's a problem.
We want to be able to tell our customer a day or
two hours ahead that there is a problem.
So that is predictability, Right?
So we want not just in the product,
even in the services side, we want to infuse total
machine learning into the product.
From a platform perspective we want to make it
completely open, extensible. So our partners can come
and build on top of it. So every customer can take
advantage of vertical and other solutions that they build.
>> You get a platform, you get this fly-wheel effect,
inject machine learning everywhere open API
so you can bring in new technologies like block chain
as they evolve. Dinesh, thank you very much for coming
on the Cube.
>> Oh, thank you so much.
>> Always great to have you.
>> It's a pleasure, thank you.
>> Alright, keep it right there everybody.
We'll be right back with our next guest.
This is the Cube live from IBM Think 2018.
We'll be right back.
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