>> Live, from Las Vegas
it's theCUBE.
Covering Informatica World 2019.
Brought to you by Informatica.
>> Welcome back everyone to theCUBE's
live coverage of Informatica World 2019
here in Las Vegas.
I'm your host Rebecca Knight
along with my co-host John Furrier.
We are joined by Anil Chakravarthy.
He is the chief executive officer at Informatica.
Thank you so much for returning to theCUBE.
>> Oh my pleasure, thanks for having me
back on your show here.
>> So, on the main stage this morning
you said that AI and ML need data,
but data needs ML and AI.
>> That's right. >> Can you just elaborate
on that, riff on that a little bit.
>> Yeah, yeah. You know if you look
at AI and ML, hot topic obviously,
every company is trying to take advantage
of new machine learning AI technologies.
One of the key components of making that happen
is the availability of the right data,
because you have to train these
machine learning algorithms, the data scientists
have to be able to find the right data,
and then they have to prepare the right data,
make sure that they have access to the data,
clean it up, and then put it into
their AI models into their AI algorithms and so on.
Because the training of the algorithms
is very sensitive to the quality of the data.
It's really garbage in garbage out.
If you don't feed it the right data,
the results will be skewed.
And so, that's the key part of what we mean by
when we say AI machine learning needs data.
The flip side is in what we do
and help customers, which is
manage their vast complexity and scale of data.
If you look at customers
petabytes of data,
thousands of databases,
hundreds of thousands of cables,
so how do they manage all of the data?
Because the management of data
is not just about availability of data
or the performance of those systems and so on.
All that is super important, but it's also
the security of the data,
the governance of the data,
the availability of the data
to the right users at the right time.
Trying to do all that manually,
you just can't keep up
and that's where you need machine learning and AI
to be able to do that for you in an automated manner.
>> Anil, we've talked in the past
multiple years ago.
Every year, it's the same story.
You guys had on that RightWave data
everyone is now talking about
what you were talking about
four years ago. >> Yep.
You're continuing to talk about it and adding to it.
You also talk about being the Switzerland
the neutral third party, because data
needs to connect around >> Right
from multiple sources.
You had a lot of industry players
up on stage today.
How is that going?
How are you continuing to be
that role in the industry
as more and more people come in?
What's it say about the momentum
and for Informatica strategy?
>> Yeah, I think
it's really because of what customers really want.
Take any customer, any enterprise customer
or government customer, of any scale,
they're usually using a lot of different
both on-premise and cloud, technology offerings.
So it could be multiple software service offerings,
multiple maybe public clouds,
where they're running it as platform of service.
A lot of different on-premise offerings et cetera.
Which means that all of those offerings that they're using
have a data footprint.
From a customer's perspective,
if they're using different tools
to manage the data for each one of those
well they have all the old problems they've always had.
Data inconsistency, inability to manage it,
and just who's going to learn, if you're a data administrator,
are you going to learn four or five
different tools to manage it?
So that's does not really going to work.
That's where customers are demanding
hey, I need a data management platform
that can help me manage the data consistently
and that's where we come in,
that's what helps us be the Switzerland of data.
>> So data feeds machine learning,
machine learning powers AI.
This is the formula you guys talk about all the time.
No data, no AI.
But if data is constrained,
from either infrastructure legacy,
or a regulation, that's going to slow
the feeder concept down >> Yeah.
or maybe incomplete data.
This is really about operationalizing AI
so this is, you've got to solve that data problem first
if you want to scale up operations around AI.
What's the state of the art from Informatica?
What are you guys doing in this area
and where is the customers' progress
in this new operationalizing of AI
with data at the heart of it?
>> Yes, from an operationalization perspective,
what you need is, first of all,
help your data scientists and others using AI
to find the right data.
Finding the right data, you do it through the catalog,
for example, it'll tell you what data you can access
and then what's the metadata around the data,
what you can use the data for.
Maybe there's some data that you say
look, we have the data set but we don't have
the customers opt-in to use that data.
Fine, you can't use that data.
That's the first step, finding the right data.
Then getting access to that data
that's what you get through an integration,
the cloud tools, the big data tools, et cetera.
Then you prepare the data.
We have a number of tools to prepare the data
to make sure that the AI and machine learning models
can use them well.
Then you feed the data.
You run it, you get your result,
but then the explain-ability is a big deal.
Whether it's regulators
or even your own internal executives.
They say, oh
that's the result of running the AI model
but how did it come to that decision?
You know, for instance,
in financial services, if you're using AI
to do, let's say,
a decision on who gets to get a loan or not,
well you have to make sure
that there is no bias in that, right?
In order to explain the result,
you need to know where the source data came from.
That's what we do as well,
through our governance and lineage.
>> Well we'd love talkin about SAS success
you look at the cloud-native, born in the cloud,
great examples how data has really been
driving the new generation of innovation.
The more enterprises we talk to
around digital transformation,
the more that we hear
we want to be consumer-like. >> Yeah.
With a SAS, whether it's an app for banking
or an IoT app, or anything.
SAS is kind of an unique data for that.
How should a enterprise architect that solution?
Because it's harder when you don't have
clean, one cloud native so you got to
bring in some cloud, you got to bring in the on-premise.
Where does the data sit (laughs) in all this?
How do you architect the data on-premise,
in the cloud, or in general,
so that the customers have a really,
road map to a SAS solution?
>> It's a great question, you know.
What you see right now is the focus
on building it through customer data platform.
We obviously just acquired a company,
AllSight, that helps build
the get inside sort of the customer data platform.
The way we think of it at Informatica
is you have a customer data platform,
well then the last mile of how you reach the customer,
keeps changing and evolving.
That last mile could be through a call center.
It could be through a web application.
It could be through a mobile app.
It could be through a sales person,
who is reaching the customer with a live interaction.
It could be a lot of different ones
and it could be all of them.
That's where the omnichannel comes in.
The way to do what you are asking for, John
is to truly focus on building a customer data platform
that can support multiple kinds of last mile
when it comes to actually interacting
with the customer.
That's how you ensure a very good, consistent,
customer experience.
And then you take advantage of whatever
the latest technologies.
Tomorrow, like we were just talking about here,
if there is AI enabled bots or something else
that's a better way of interacting
with the customer, you're still working off of
the same consistent customer data platform.
That's how we see it.
>> I want to ask you about the skills gap.
>> Yeah. >> We know that there is
a great demand for people who are data scientists,
experts in cloud and analytics,
and yet there are so few qualified candidates.
>> That's right. >> I want to hear your thoughts
about it and then also what
Informatica is doing to make sure
you are recruiting and retaining the right employees.
>> Yeah, I think one
I completely agree with you on the skills gap
and obviously that's also a great opportunity as well,
because, in reality
a lot of the younger folks are looking at
what careers they want to pursue.
With the right mindset and the right training
these will be great careers for them.
There's also, the other great thing about this is
this is across the country and across the world
so you don't have to be in a specific location
to have a successful career as a data scientist
or as a data steward, et cetera et cetera.
I think from a training perspective
we are actually working with a number
of different universities.
We actually started working with
Indiana University to build a curriculum
that can then be available online
available to a lot of different folks.
We obviously work with a lot of different
system integrators and consulting partners
who hire hundreds of thousands of people
and they are starting to build
some very very large practices around data science.
That's another avenue for career growth there.
And last, we're also starting
at a much younger age.
Last year we talked about the next 25 program
and tomorrow, when Sally is back on stage,
you will see an update on the next 25 program.
Were trying to get kids at the middle school level
interested in this as a career.
>> Anil, real quick on the follow up on that
is what curriculum specifically do you see in high demand?
Is it machine learning?
Is it analytics?
Is it cognitive?
What specific skills
that you see in demand
and for folks to start thinking about?
>> I think what my advice to folks,
in fact my daughter is a freshman in college too
and I've been giving her the same advice
because I think this is a great way to go,
is when you think of skills development
first think of a broad platform
that will give you the right skills
regardless of the changes in technology,
because technology will keep changing.
So what is that broad platform?
The broad platform is, I think you need
a background in statistics, you need
a background in computer modeling and programming,
and you need a broad platform in overall math.
And again, I don't mean to scare anybody,
it's not calculus level math,
but it's math that helps you understand concepts et cetera.
That's the broad foundation you need.
Then you have a number of different new technologies
whether it's Python, whether it's Math Lab.
There are a lot of different ways
of approaching and doing data science.
But then, once you have that foundation,
it's easy to pick this up.
And the rest of it,
just like in any other job,
once you start doin it,
you're going to pick up the rest of it
and you'll become an expert there.
>> Great. Amil Chakravarthy,
thank you so much for coming back to theCUBE.
>> Perfect, thank you so much for having me.
>> Yeah, thanks for havin us.
>> Thank you.
>> You are watching theCUBE Informatica 2019.
I'm Rebecca Knight, for John Furrier, stay tuned.
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