>> Live from New York, it's theCUBE
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>> Welcome back to the Big Apple, everybody.
This is theCUBE, the leader in live tech coverage.
My name is Dave Vellante, I'm here
with my cohost Peter Burris, and this
is our week-long coverage of CUBENYC.
It used to be, really, a big data theme.
It sort of evolved into data, AI, machine learning.
Ronan Schwartz is here, he's the senior
vice president and general manager of cloud,
big data, and data integration
at data integration company Informatica.
Great to see you again, Ronan, thanks so much for coming on.
>> Thanks for inviting me, it's a good, warm day in New York.
>> Yeah, the storm is coming and...
Well, speaking of storms, the data center is booming.
Data is this, you know, crescendo
of storms (chuckles) have occurred,
and you guys are at the center of that.
It's been a tailwind for your business.
Give us the update, how's business these days?
>> So, we finished Q2 in a great, great success,
the best Q2 that we ever had, and the third quarter
looks just as promising, so I think the short answer
is that we are seeing the strong demand for data,
for technologies that supports data.
We're seeing more users, new use cases,
and definitely a huge growth in need to support...
To support data, big data, data in the cloud, and so on,
so I think very, very good Q2 and it looks like
Q3's going to be just as good, if not better.
>> That's great, so there's been a decades-long
conversation, of course, about data, the value of data,
but more often than not over the history of recent history,
when I say recent I mean let's say 20 years on,
data's been a problem for people.
It's been expensive, how do you
manage it, when do you delete it?
It's sort of this nasty thing that people have to deal with.
Fast forward to 2010, the whole Hadoop movement,
all of a sudden data's the new oil, data's...
You know, which Peter, of course,
disagrees with for many reasons.
>> No, it's...
>> We don't have to get into it.
>> It's subtlety. >> It's a subtlety,
but you're right about it, and well,
maybe if we have time we can talk
about that, but the bromide of...
But really focused attention on data
and the importance of data and the value of data,
and that was really a big contribution that Hadoop made.
There were a lot of misconceptions.
"Oh, we don't need the data warehouse anymore.
"Oh, we don't need old," you know, "legacy databases."
Of course none of those are true.
Those are fundamental components of people's
big data strategy, but talk about the importance
of data and where Informatica fits.
>> In a way, if I look into the same history
that you described, and Informatica have definitely
been a player through this history.
We divide it into three eras.
The first one is when data was like this thing
that sits below the application, that used
the application to feed the data in
and if you want to see the data
you go through the application, you see the data.
We sometimes call that as Data 1.0.
Data 2.0 was the time that companies,
including Informatica, kind of froze
and been able to give you a single view
of the data across multiple systems,
across your organization, and so on,
because we're Informatica we have the ETL
with data quality, even with master data management,
kind of came into play and allowed an organization
to actually build analytics as a system,
to build single view as a system, et cetera.
I think what is happening, and Hadoop
was definitely a trigger, but I would say
the cloud is just as big of a trigger
as the big data technologies, and definitely
everything that's happening right now
with Spark and the processing power,
et cetera, is contributing to that.
This is the time of the Data 3.0
when data is actually in the center.
It's not a single application like it was in the Data 2.0.
It's not this thing below the application in Data 1.0.
Data is in the center and everything else
is just basically have to be connected to the data,
and I think it's an amazing time.
A big part of digitalization is the fact
that the data is actually there.
It's the most important asset the organization has.
>> Yeah, so I want to follow up on something.
So, last night we had a session Peter hosted
on the future of AI, and he made the point,
I said earlier data's the new oil.
I said you disagreed, there's a nuance there.
You made the point last night that oil,
I can put oil in my car, I can put
oil in my house, I can't do both.
Data is the new currency, people said,
"Well, I can spend a dollar or I can spend
"a dollar on sports tickets, I can't do both."
Data's different in that...
>> It doesn't follow the economics of scarcity,
and I think that's one of the main drivers here.
As you talk about 1.0, 2.0, and 3.0,
1.0 it's locked in the application,
2.0 it's locked in a model, 3.0 now we're opening it up
so that the same data can be shared, it can be evolved,
it can be copied, it can be easily transformed,
but their big issue is we have to
sustain overall coherence of it.
Security has to remain in place,
we have to avoid corruption.
Talk to us about some of the new demands given,
especially that we've got this,
more data but more users of that data.
As we think about evidence-based management,
where are we going to ensure that all of those
new claims from all of those new users
against those data sources can be satisfied?
>> So, first, I truly like...
This is a big nuance, it's not a small one.
(laughs)
The fact that you have better idea actually
means that you do a lot of things better.
It doesn't mean that you do one thing better
and you cannot do the other. >> Right.
I agree 100%, I actually contribute that for two things.
One is more users, and the other
thing is more ways to use the data,
so the fact that you have better data,
more data, big data, et cetera,
actually means that your analytics is going to be better,
right, but it actually means that if you
are looking into hyperautomation and AI
and machine learning and so on, suddenly
this is possible to do because you have
this data foundation that is big enough
to actually support machine learning processes,
and I think we're just in the beginning of that.
I think we're going to see data being
used for more and more use cases.
We're in the integration business
and in the data management business,
and we're seeing, within what our customers
are asking us to support, this huge growth
in the number of patterns of how they want the data
to be available, how they want to bring data
into different places, into different users,
so all of that is truly supporting what you just mentioned.
I think if you look into the Data 2.0 timeframe,
it was the time that a single team that is very,
very strong with the right tools
can actually handle the organization needs.
In what you described, suddenly self-service.
Can every group consume the data?
Can I get the data in both batch and realtime?
Can I get the data in a massive amount
as well as in small chunks?
These are all becoming very, very central.
>> And very use case, but also user and context,
you know, we think about time, dependent,
and one of the biggest challenges that we have
is to liberate the data in the context
of the multiple different organization uses,
and one of the biggest challenges that customers have,
or that any enterprise has, and again,
evidence-based management, nice trend,
a lot of it's going to happen, but the familiarity
with data is still something that's not,
let's say broadly diffused, and a lot of the tools
for ensuring that people can be made familiar,
can discover, can reuse, can apply data,
are modestly endowed today, so talk about
some of these new tools that are going to make it easier
to discover, capture, catalog, sustain these data assets?
>> Yeah, and I think you're absolutely right,
and if this is such a critical asset, and data is,
and we're actually looking into more user
consuming the data in more ways,
it actually automatically create a bottleneck
in how do I find the data, how do I identify the data
that I need, and how am I making this available
in the right place at the right time?
In general, it looks like a problem
that is almost unsolvable, like I got more data,
more users, more patterns, nobody have their budget
tripled or quadrupled just to be able to consume it.
How do you address that, and I think Informatica
very early have identified this growing need,
and we have invested in a product that we call
the enterprise data catalog, and it's actually...
The concept of a catalog or a metadata repository,
a place that you can actually identify all the data
that exists, is not necessarily a new concept--
>> No, it's been around for years.
>> Yes, but doing it in an enterprise-unified way
is unique, and I think if you look into what
we're trying to basically empower any user
to do I basically, you know, we all using Google.
You type something and you find it.
If you're trying to find data in the organization
in a similar way, it's a much harder task,
and basically the catalog and Informatica unified,
enterprise-unified catalog is doing that,
leveraging a lot of machine learning and AI
behind the scenes to basically make this search possible,
make basically the identification of the data possible,
the curation of the data possible,
and basically empowering every user to find the data
that he wants, see recommendation for other data
that can work with it, and then basically
consume the data in the way that he wants.
I totally think that this will
change the way IT is functioning.
It is actually an amazing bridge
between IT and the business.
If there is one place that you can search
all your data, suddenly the whole interface
between IT and the business is changing,
and Informatica's actually leading this change.
>> So, the catalog gives you line-of-sight
on all, (clears throat) all those data sources,
what's the challenge in terms of creating
a catalog and making it performant and useful?
>> I think there are a few levels of the challenge.
I chose the word enterprise-unified
intelligent catalog deliberately,
and I think each one of them is kind of
representing a different challenge.
The first challenge is the unified.
There is technical metadata, this is the mapping
and the processes that move data
from one place to the other,
then there is business metadata.
These are the definition the business is using,
and then there is the operational metadata as well,
as well as the physical location and so on.
Unifying all of them so that you can
actually connect and see them in one place
is a unique challenge that at this stage
we have already completely addressed.
The second one is enterprise, and when talking
about enterprise metadata it means that
you want all of your applications,
you want application in the cloud,
you want your cloud environment, your big data environment.
You want, actually, your APIs,
you want your integration environment.
You want to be able to collect all
of this metadata across the enterprise,
so unified all the types, enterprise is the second one.
The third challenge is actually the most exciting one,
is how can you leverage intelligence
so it's not limited by the human factor,
by the amount of people that you have to actually put
the data together, right? >> Mm-hm.
>> And today we're using a very, very sophisticated,
interesting logarithm to run on the metadata
and be able to tell you that even though you don't know
how the data got from here to here, it actually did get
from here to here. >> Mm-hm.
>> It's a dotted line, maybe somebody copied it,
maybe something else happened, but the data
is so similar that we can actually
tell you it came from one place.
>> So, actually, let me see, because I think there's...
I don't think you missed a step,
but let me reveal a step that's in there.
One of the key issues in the enterprise side
of things is to reveal how data's being used.
The value of data is tied to its context,
and having catalogs that can do, as you said,
the unified, but also the metadata
becomes part of how it's used makes that opportunity,
that ability to then create audit
trails and create lineage possible.
>> You're absolutely right, and I think
it actually is one of the most important things,
is to see where the data came from and what steps
did it go to. >> Right.
>> There's also one other very interesting value of lineage
that I think sometimes people tend to ignore is
who else is using it? >> Right.
>> Who else is consuming it, because that is actually,
like, a very good indicator of how good
the data is or how common the data is.
The ability to actually leverage
and create this lineage is a mandatory thing.
The ability to create lineage that is inferred,
and not actually specifically defined,
is also very, very interesting,
but we're now doing, like, things that
are, I think, really exciting.
For example, let's say that a user is looking
into a data field in one source and he is
actually identifying that this is a certain,
specific ID that his organization is using.
Now we're able to actually automatically understand
that this field actually exists in 700 places,
and actually, leverage the intelligence
that he just gave us and actually ask him,
"Do you want it to be automatically updated everywhere?
"Do you want to do it in a step-by-step, guided way?"
And this is how you actually scale to handle
the massive amount of data, and this is how
organizations are going to learn more and more
and get the data to be better and better
the more they work with the data.
>> Now, Ronan, you have hard news this week, right?
Why don't you update us on what you've announced?
>> So, I think in the context for our discussion,
Informatica announced here, actually today,
this morning in Strata, a few very exciting
news that are actually helping
the customer go into this data journey.
The first one is basically supporting
data across, big data across multi-clouds.
The ability to basically leverage all of these great tools,
including the catalog, including the big data management,
including data quality, data governance,
and so on, on AWS, on Azure, on GCP,
basically without any effort needed.
We're even going further and we're empowering
our user to use it in a serverless mode
where we're actually allowing them full control
over the resources that are being consumed.
This is really, really critical
because this is actually allowing them
to do more with the data in a lower cost.
I think the last part of the news that is really exciting
is we added a lot, a lot of functionality
around our Spark processing and the capabilities
of the things that you can do so that the developers,
the AI and machine learning can use their stuff,
but at the same time we actually empower
business users to do more than they ever did before.
So, kind of being able to expand the amount of users
that can access the data, wanting a more sophisticated way,
and wanting a very simple but still very powerful way,
I think this is kind of the summary of the news.
>> And just a quick followup on that.
If I understand it, it's your full complement
of functionality across these clouds, is that right?
You're not neutering... (chuckles)
>> That is absolutely correct, yes,
and we are seeing, definitely within our customers,
a growing choice to decide to focus their big data
efforts in the cloud, it makes a lot of sense.
The ability to scale up and down
in the cloud is significantly superior,
but also the ability to give more users
access in the cloud is typically easier,
so I think Informatica have chosen as the market
we're focusing on enterprise cloud data management.
We talked a lot about data management.
This is a lot about the cloud, the cloud part of it,
and it's basically a very, very focused effort
in optimizing things across clouds.
>> Cloud is critical, obviously.
That's how a lot of people want to do business.
They want to do business in a cloud-like
fashion, whether it's on-prem or off-prem.
A lot of people want things to be off-prem.
Cloud's important because it's where
innovation is happening, and scale.
Ronan, thanks so much for coming on theCUBE today.
>> Yeah, thank you very much and I did
learn something, oil is not one of the terms
that I'm going to use for data in the future.
>> Makes you think about that, right?
>> I'm going to use something different, yes.
>> It's good, and I also...
My other takeaway is, in that context,
being able to use data in multiple places.
Usage is a proportional relationship
between usage and value, so thanks for that.
>> Excellent. >> Happy to be here.
>> And thank you, everybody, for watching.
We will be right back right after this short break.
You're watching theCUBE at #CUBENYC, we'll be right back.
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