>> Narrator: Live from San Jose, it's theCUBE.
Presenting big data silicon valley,
brought to you be Siliconangle Media
and its ecosystem partner.
>> Welcome back to theCUBE we are live in San Jose,
at Forger Eatery, super cool place.
Our first day of our two days of coverage
at our event called Big Data SV.
Down the street is the Strata Data Conference,
and we've got some great guests today
that are going to share a lot insight
and different perspectives on Big Data.
This is our 10th big data event on theCUBE,
our fifth in San Jose.
We invite you to come on down to Forger Eatery
and we also invite you to come down this evening.
We've got a party going on
and we've got a really cool breakfast presentation
on the analysis site in the morning.
Our first guest is, needs no introduction to theCUBE,
he's a Cube Alumni, Murthy Mathiprakasam,
did I get that right?
>> Murthy: Absolutely.
>> Murthy, awesome, as we're going to call him.
The director of product marketing for Informatica,
welcome back to theCUBE, it's great to have you back.
>> Thanks for having me back,
and congratulations on the 10 year anniversary.
>> Yeah!
So, interesting, exciting news from Informatica
in the last two days, tell us about a couple of those
big announcements that you guys just released.
>> Absolutely, yes.
So this has been very exciting year for us
lots of, you know product, innovations and announcements,
so just this week alone, actually there's one announcement
that's probably going out right now as we speak,
around API management, so one of the things,
probably taking about before we started interviews
you know around the trend toward cloud,
lots of people doing a lot more data integration
and application integration in the cloud space.
But they face all the challenges
that we've always seen in the data management space.
Around developer productivity, and hand coding,
just a lot of complexity
that organizations have around maintenance.
So one of the things at Informatica always brought
to every domain that we cover is this ability
to kind of abstract the underlying complexity,
use a graphical user interface,
make things at the logical level
instead of the physical level.
So we're bringing that entire kind of paradigm
to the API management space.
That's going to be very exciting,
very game changing on the kind of
app-to-app integration side of things.
Back on the data world of course,
which is what we're, you know,
mainly talking about here today.
We're doing a lot there as well.
So we announced kind of a next generation
of our data management platforms for the big data world,
part of that is also a lot of cloud capabilities.
'Cause again, one of the bigger trends.
>> Peter: Have you made a big bet there?
>> Absolutely, and I mean this is the investment,
return on investments over like 10 years, right?
We were started in a kind of cloud game about 10 years ago
with our platform as a service offering.
So that has been continuously innovated on
and we've been engineering, re-imagining that,
to now include more of the big data stuff in it too,
because more and more people are building data lakes
in the cloud.
So it's actually quite surprising,
you know the rate at which the data lake
kind of projects are now either migrating
or just starting in the cloud environments.
So given that being the trend,
we were kind of innovating against that as well.
So now our platform is service offerings supports
the ability to connect to data sources
in the cloud natively.
You can do processing and gestion in the cloud.
So there's a lot of really cool capabilities,
again it's kind of bringing the Informatica ease of use,
and kind of acceleration that comes to platform approach
to the cloud environment.
And there's a whole bunch of other announcements too,
I mean I could spend 20 minutes,
just on different innovations,
but you know bringing artificial intelligence
into the platform so we can talk more about that.
>> Well I want to connect what you just announced
with the whole notion of the data lake,
'cause it's really Informatica strength
has always been in between.
And it turns out that where a lot
the enterprise problems have been.
So the data lake has been there,
but it's been big, it's been large,
it was big data and the whole notion is
make this as big as you can
and we'll figure out what to do with it later.
>> Murthy: Right.
>> And now you're doing the API which is just a indication
that we're seeing further segmentation and a specificity,
a targeting of how we're going to use data,
the value that we create out of data
and apply it to business problems.
But really Informatica strength is been in between.
>> Murthy: Absolutely.
>> It's been in, knowing where you data is,
it's been in helping to build those pipelines
and managing those pipelines.
How have the investments that you've made
over the last few years, made it possible
for you to actually deliver an API orientation,
that will actually work for Enterprises?
>> Yeah, absolutely, and I would actually phrase it
as sort of platform orientation, but you're exactly right.
So what's happening is,
I view this as sort of maturation
of a lot of these new technologies.
You know Hadoop was a very very, as you were saying
kind of experimental technology
four or five years ago.
And we had customers too
who were kind of in that experimental phase.
But what's happening now is,
big data isn't just a conversation with
data engineers and developers, we're talking to CDO's,
and Chief Data Officers, and VP's of data infrastructures
about using Hadoop for Enterprise scale projects,
now the minute you start having a conversation
with a Chief Data Officer,
you're not just talking about simple tools
for ingestion and stuff like that.
You're talking about security,
you're talking about compliance,
you're talking about GDPR if you're in Europe.
So there's a whole host of sort of
data management challenges,
that are now relevant for the big data world,
just because the big data world has become main stream.
And so this is exactly to your point,
where the investments that I think
Informatica has been making and bringing our kind of
comprehensive platform oriented approach
to this space are paying off.
Because for Chief Data Officer,
they can't really do big data without those features.
They can't not deal with security and compliance,
they can't not deal with not knowing what the data is.
'Cause they're accountable
for knowing what the data is, right?
And so, there's a number of things
that by virtue of the maturation of the industry,
I think that trends are pointing toward,
the enterprises kind of going more toward
that platform approach.
>> On that platform approach
Informatica's really one of the only vendors
that's talking about that, and delivering it.
So that clearly is an area of differentiation.
Why do you think that's nascent.
This platform approach verses a kind of
fit-for-purpose approach.
>> Yeah, absolutely.
And we should be careful with even the phrase
fit-for-purpose too,
'cause I think that word gets thrown around a lot
as it's one of those buzz words in the industry.
Because it's sort of the positive way
of saying incomplete, you know?
And so, I think there are vendors who have tried
to kind of address, know you one aspect of sort of
one feature of the entire problem,
that a Chief Data Officer would care about.
They might call it fit-for-purpose,
but you have to actually solve a problem
at the end of the day.
The Chief Data Officer's are trying to build
enterprise data pipelines.
You know you've got raw information
from all sorts of data sources, on premise, in the cloud.
You need to push that through a process,
like at manufacturing process of being able to ingest it,
repair it, cleanse it, govern it, secure it, master it,
all the stuff has to happen in order
to serve all the various communities
that a Chief Data Officer has to serve.
And so you're either doing all that or you're not.
You know, that's the problem, that way we see the problem.
And so the platform approach
is a way of addressing the comprehensive set of problems
that a Chief Data Officer, or these kind of of Data Executives
care about, but also do it in a way,
that fosters productivity and re-usability.
Because the more you sort of build things in a kind of
infrastructure level way,
as soon as the infrastructure changes you're hosed, right?
So you're seeing a lot of this in the industry now too,
where somebody built something in Mapreduce three years ago,
as soon as Spark came out,
they're throwing all that stuff away.
And it's not just, you know, major changes like that,
even versions of Spark, or versions of Hadoop,
can sometimes trigger a need to recode and throw away stuff.
And organization can't afford this.
When you're talking about 40 to 50% growth
in the data overall.
The last thing you want to do is make an investment
that you're going to end up throwing away.
And so, the platform approach to go back to your question,
is the sort of most efficient pathway
from an investment stand point,
that an enterprise can take,
to build something now that they can actually reuse
and maintain and kind of scale in a very very pragmatic way.
>> Well, let me push you on that a little bit.
>> Murthy: Yeah.
>> 'Cause what we would say is that,
the fit-to-purpose is okay so long as you're true
about the purpose, and you understand what it means to fit.
What a lot of the open source, a lot of companies have done,
is they've got a fit-to-purpose
but then they do make promises that they say,
oh this is fit-to-purpose, but it's really a platform.
And as a consequence you get a whole bunch of,
you know, duck-like solutions,
(laughing)
That are, you know, are they swimming,
or are they flying, kind of problems.
So, I think that what we see clients asking for,
and this is one of my questions,
what we see clients asking for is,
I want to invest in technologies
that allow me to sustain my investments,
including perhaps some of my mistakes,
if they are generating business value.
>> Murthy: Right.
>> So it's not a rip and replace,
that's not what you're suggesting,
what you're suggesting I think is,
you know, use what you got,
if it's creating value continue to use it,
and then over time, invest the platform approach
that's able to generate additional returns on top of it.
Have I got that right?
>> Absolutely.
So it goes back to flexibility,
that's the key word, I think that's kind of on the minds
of a lot of Chief Data Officers.
I don't want to build something today,
that I know I'm going to throw away a year from now.
>> Peter: I want to create options for the future.
>> Create options. >> When I build them today.
>> Exactly.
So even the cloud, you're bringing up earlier on, right?
Not everybody knows exactly what their cloud strategy is.
And it's changing extremely rapidly, right?
We had almost,
we were seeing very few big data customers
in the cloud maybe even a year or two ago?
Now we're close to almost 50%
of our big data business is people deploying off premise,
I mean that's amazing,
you know in a period of just a year or two.
So Chief Data Officers are having to operate
in these extreme kind of high velocity environments.
The last thing you want to do is make a bet today,
with the knowledge that you're going to end up
having to throw away that bet in six months or a year.
So the platform approach is sort of like your
insurance policy because it enables you to design
for today's requirements,
but then very very quickly migrate or modify
for new requirements that maybe be six months,
a year or two down the line.
>> On that front,
I'd love for you to give us an example of a customer
that has maybe in the last year,
since you've seen so much velocity, come to you.
But also had other technologies
and their environment that from a cost perspective,
I mean but at Peter's point
there's still generating value, business value.
How do you help customers that have
multiple different products maybe exploring different
multi-calibers, how to they come
and start working with Informatica
and not have to rip out other stuff,
but be able to move forward and achieve ROI?
>> So, it's really interesting kind of how
people think about the whole rip and replace concept.
So we actually had a customer dinner last night
and I'm sitting next to a guy,
and I was kind of asking very similar question.
Tell me about your technology landscape,
you know where are things going,
where have things gone in the past,
and he basically said there's a whole portfolio
of technologies that they plan to obsolete.
'Cause they just know that, like they're probably,
they don't even bother thinking
about sustainability, to your point.
They just want to use something just to kind of try it out.
It's basically like a series
of like three month trails of different technologies.
And that's probably why we such proliferation
of different technologies,
'cause people are just kind of trying stuff out,
but it's like, I know I'm going to throw this stuff out.
>> Yeah but that's, I mean, let me make sure I got that.
'Cause I want to reconcile a point.
That's if they're in pilot and the pilot doesn't work.
But the minute it goes into production
and values being created they want
to be able to sustain that stream of value.
>> This is production environment.
I'm glad you asked that question.
So this is a customer that,
and I'll tell you where I'm going to the point.
So they've been using Informatica for over four years,
for big data which is essentially almost
the entire time big data's been around.
So the reason this customers making the point is,
Informatica's the only technology
that is actually sustained precisely
for the point that you're bringing up,
because their requirements have changed wildly
during this time.
Even the internal politics of who needs access to data,
all of that has changed radically over these four years.
But the platform has enabled them to actually make
those changes, and it's you know,
been able to give them that flexibly.
Everything else as far as, you know, developer tools,
you know, visualization tools,
like every year there's some kind of new thing
that sort of comes out.
And I don't want to be terribly harsh,
there's probably one or two kind of vendors
that have also persisted in those other areas.
But, the point that they were trying to make
to your original point is,
is the point about sustainability.
Like, at some point to avoid complete and utter chaos,
you got to have like some foundation
in the data environment.
Something actually has to be something
you can invest in today,
knowing that as these changes internally
externally are happening, you can kind of count on it
and you can go to cloud you can be on Premise,
you can have structured data, unstructured data,
you know, for any type of data, any type of user,
any type of deployment environment.
I need something that I can count on,
that's actually existing for four or more years.
And that's where Informatica fits in.
And meanwhile there's going to be a lot of other tools that,
like this guy was saying,
they're going to try out for three month or six months
and that's great, but they're almost using it with the idea
that they're going to throw it away.
>> Couple questions here;
What are some of the business values that you were,
stating like this gentlemen,
that you ere talking to last night.
What's the industry that's he in and also,
are there any like stats or ranges you can give us.
Like, reduction in TCO, or new business models opening up.
What's the business impact
that Informatica is helping these customers achieve.
>> Yeah, absolutely, I'll use this example,
he's, I can't mention the name of the company
but it's an insurance company.
>> Lisa: Lot's of data.
>> Lots of data, right.
Not only do they have a lot of data,
but there's a lot of sensitivity around the data.
Because basically the only way they grow
is by identifying patterns in consumers
and they want to look at it if somebody's
using car insurance in, maybe it for so long
they're ready to get married, they need home insurance,
they have these like really really sophisticated
models around human behavior.
So they know when to go and position new forms of insurance.
There's also obviously security government
types of issues that are at play as well.
So the sensitivity around data is very very important.
So for them, the business value is increased revenue,
and you know ability to meet kind of regulatory pressure.
I think that's generally,
I mean every industry has some variant of that.
>> Right.
>> Cost production, increase revenue,
you know meeting regulatory pressures.
And so Informatica facilitates that,
because instead of having to hire armies of people,
and having to change them out
maybe every three months or six months
'cause the underlying infrastructures changing,
there's this one team, the Informatica team
that's actually existed for this entire journey.
They just keep changing, used cases, and projects,
and new data sets, new deployment models,
but the platform is sort of fixed
and it's something that they can count on it's robust,
it enables that kind of.
>> Peter: It's an asset.
>> It's an asset that delivers that sustainable value
that you were taking about.
>> Last question, we've got about a minute left,
in terms of delivering value,
Informatica not the only game in town,
your competitors are kind of going with this
MNA partnership approach.
What makes Informatica stand out,
why should companies consider Informatica?
>> So they say like, what there's a quote about it.
Imitation is the most sincere from of flattery.
Yeah!
(laughing)
I guess we should feel as a little bit flattered,
you know, by what we're seeing in the industry,
but why from a customers stand point should they,
you know continue to rely on Informatica.
I mean we keep pushing the envelope on innovations, right?
So, one the other areas that we innovated on
is machine learning within the platform,
because ultimately if one of the goals of the platform
is to eliminate manual labor,
a great way to do that is to just not have
people doing it in the first place.
Have machines doing it.
So we can automatically understand the structure
of data without any human intervention, right?
We can understand if there's a file
and it's got costumer names
and you know, cost and skews, it must be an order.
You don't actually have to say that it's an order.
We can infer all this,
because of the machine learning them we have.
We can give recommendations
to people as they're using our platform,
if you're using a data set
and you work with another person,
we can go to you and say hey,
maybe this is a data set that you would be interesting in.
So those types of recommendations, predictions,
discovery, totally changes the economic game
for an organization.
'Cause the last thing you want
is to have 40 to 50% growth in data
translate into 40 to 50% of labor.
Like you just can't afford it.
It's not sustainable, again,
to go back to your original point.
The only sustainable approach
to managing data for the future,
is to have a machine learning based approach
and so that's why, to your question,
I think just gluing a bunch of stuff together
still doesn't actually get to nut of sustainability.
You actually have to have,
the glue has to have something in it, you know?
And in our case it's the machine learning approach
that ties everything together
that brings a data organization together,
so they can actually deliver the maximum business value.
>> Literally creates a network of data
that delivers business value.
>> You got it.
>> Well Murthy, Murthy Awesome,
thank you so much for coming back to theCUBE.
>> Thank you!
>> And sharing what's going on the Informatica
and what's differentiating you guys.
We wish you a great rest of the Strata Conference.
>> Awesome, you as well.
Thank you.
>> Absolutely, we want to thank you for watching theCUBE.
I'm Lisa Martin with Peter Burris,
we are live in San Jose at the Forger Eatery,
come down here and join us, we've got a really cool space,
we've got a part-tay tonight, so come join us.
And we've got a really interesting breakfast presentation
tomorrow morning, stick around and we'll be right back,
with our next guest for this short break.
(fun upbeat music)