>> Announcer: Live, from Berlin, Germany
it's theCUBE.
Covering NetApp Insight 2017.
Brought to you by NetApp.
>> Welcome back to theCUBE's live coverage
of NetApp Insight here in Berlin, Germany.
I'm your host, Rebecca Knight,
along with my co-host Peter Burris.
We are joined by Deepak Visweswaraiah.
He is the senior vice president
for data fabric manageability at NetApp.
Thanks so much for coming on the show, Deepak.
>> Thank you, thank you.
Let's talk about the data fabric,
and why modern IT needs it
to do what it needs to do.
For acceleration.
>> I think anyone attending the conference,
I thought the keynote that happened yesterday
Kenneth Corky from Economist
actually talked about
how data actually is growing
and then how much of that is becoming more and more
important to companies.
Not only just from an ability
to be able to actually handle data,
but how they make their decisions
based on the amount of data that they have today.
The fact that we have that technology,
and we have the mindset to be able
to actually handle that data,
I think gives that unique power
to customers who actually have that data
and within their capacity.
So, if you look at it in terms of
the amount of data growing and
what companies are trying to do with that,
the fact is that data is not all in one place,
it's not all in one format,
it's not all just sitting in some place.
In terms of the fact that we call it,
you know, data being diverse,
data being dynamic
and then what have you.
This data, for any CIO,
if you talk to an IT organization and ask them in terms of
"Do you even really know where all your data lives?"
They probably, you know,
80% of the time they don't know where it is all.
And they do not know who is accessing what data.
Do they actually really have the access
or the right people accessing the right data?
And what have you.
So in being able to look at all of this data
in different silos that is there,
to be able to have visibility across these,
to be able to actually handle the diversity of that data,
whether it is structured, unstructured,
comes from, you know, the edges of the network,
whether it is streaming in different types of,
media for that matter,
whether it is streaming, video, audios,
what have you.
With that kind of diversity in the data,
and the fact that it lives in multiple places,
how do you handle all of that
in a seamless fashion?
Having a ability to view all of that
and making decisions on leveraging
the value of that data.
So, number one,
is really to be able to handle that diversity.
What you need is a data fabric
that can actually see multiple end points
and can bring that together
in one way, in one form
with one view for a customer.
That's the number one thing, if you will.
The second thing is in terms of being able to
take this data and do something that's valuable
in terms of their decision making.
How do I decide to do something with it?
I think one of the examples
you might have seen today for example is that,
we have 36 billion data points
coming from our own customer base,
that we bring back to NetApp,
and help our customers to understand
in the universe of the storage end points
with all the data collected, we can actually tell them
what may, proactively tell them, what maybe going wrong
what can actually they do better.
And then how can they do this.
This is really what that decision making capability is
to be able to analyze.
It's about being able to provide that data,
for analytics to happen.
And that analytics may happen
whether it happens in the cloud,
whether it happens where the data is,
shouldn't really matter,
and it's our responsibility to provide
or serve that data in the most optimized way
to the applications that are analyzing that data.
And that analysis actually helps make
significant amount of decisions
that the customers are actually looking to.
The third is, with all of this there is
underlying infrastructure that provides the capability
to handle this large amount of data,
not only, and also the diversity that I talked about.
How do you provide that capability for our customers,
to be able to go from
today's infrastructure in their data center,
to be able to have and handle
a hybrid way of doing things
in terms of their infrastructure that they use
within their data center,
whether they might actually have infrastructure
in the cloud and leveraging the cloud economics
to be able to do what they do best,
and/or have service providers and call locators,
in terms of having infrastructure that may be.
Ability to be able to seamlessly look all of that
providing that technology to be able
to modernize their data center
or in the cloud seamlessly.
To be able to handle that with our technology
is really the primary purpose of data fabric.
And then that's what I believe we provide to our customers.
>> People talk about data as an asset.
And folks talk about what you need
to ensure the data becomes an asset.
When we talk about materials, we talk about inventory
we talk about supply chain,
which says there's a linear progression,
one of the things that I find fascinating
about the term fabric
even though there's a technical connotation to it,
is it does suggest that in fact
what businesses need to do is literally
weave a data tapestry that supports
what the business is going to do.
Because you cannot tell with any certainty
it's certainly not a linear progression,
but data is going to be connected in a lot of different ways
to achieve the goals of the business.
Tell us a little bit about the processes
the underlying technologies and how that informs
the way businesses are starting to think about
how data does connect?
>> Can you repeat the last part?
>> How data connects,
how businesses are connecting data from multiple sources
and turning it into a real tapestry for the business.
>> Yeah, so as you said,
data comes in from various different sources
for that matter.
In terms of,
we use mobile devices so much more in the modern era,
you actually have data coming in
from these kind of sources,
or for example in terms of let's say
IoT,
in terms of sensors that are all over the place
in terms of how that data actually comes along.
Now, let's say,
in terms of if there is a customer
or if there is an organization
that is looking at this kind of data
that is coming from multiple different sources
all coming in to play.
The one thing is just the sheer magnitude of the data.
What typically we have seen is that
there is infrastructure at the edge,
even if you take the example of Internet of Things.
You try and process the data at the edge
as much as you can,
and bring back only what is aggregated
and what is required back to your data center
or a cloud infrastructure or what have you.
At the same time, just that data is not good enough
because you have to connect that data with the internal data
that you have about,
Okay, who is this data coming from
and what kind of data,
what is that metadata that connects
my customers to the data that is coming in?
I can give you a couple of examples in terms of let's say,
there is an organization that provides
weather data to farmers
in the corners of a country
that is densely populated,
but you really can never
get into with a data center infrastructure
to those kind of remote areas.
There are at the edge,
where you have these sensors
in terms of being able to
sample the weather data.
And sample also the data
of the ground in itself,
in terms of being able to,
the ultimate goal is to be able to help the farmer
in terms of when is the right time
to be able to water his field?
When is the right time to be able to sow the seeds?
When is the right time for him to really, cut the crops,
when is the most optimized time?
So, when this data actually comes back
from each of these locations,
it's all about being able to understand
where this data is coming from, from the location,
and being able to connect that to the weather data
that is actually coming from the satellites
and relating that and correlating that
to be able to determine
and tell a farmer on his mobile device,
to be able to say
"Okay, here is the right time,
"and if you don't actually cut the crops
"in the next week, you may actually lose the window
"because of the weather patterns that they see,"
and what have you.
That's an example of
what I could talk about
as far as how do you connect that data
that is coming in from various sources.
Another great example, I think,
was at the keynote yesterday
about a Stanford professor I think
talking about the race track.
It's really about that race track
and not just about any race track
that where the cars are actually making those laps.
To be able to understand and predict correctly
in terms of when to make that pit stop in a race.
You really need the data from that particular race track
because it has characteristics that
have an impact on the wear and tear of the tires,
for example.
That's really all about being able to correlate that data.
>> So it's having the understanding
of the greater context but the specific context too.
>> Absolutely, absolutely. >> Great.
You also talked about
you talked about the technology that's necessary,
but you also mentioned the right mindset.
Can you unpack that a little for our viewers?
>> The mindset I talked about earlier,
was really more in terms of
can we actually, if you think some time before,
we couldn't have attacked some of the problems
that we can afford to today.
It's really having the mindset of being able to
from that data I can do things that I could never do before.
We can solve things in the nature
of being able to,
being able to impact lives if you will.
One of our customers let's say Mercy technology,
has built a outcare platform,
that has a number of healthcare providers coming together.
Where they were actually able to make a significant impact
where they could actually determine 40%
of the patients coming into their facilities,
really were prevented from coming back into
with a sepsis kind of diagnosis.
Before then, they reduce that sepsis
happening in 40% of the time.
Which is a significant, significant impact,
if you will, for the human.
Just having that mindset in terms of
you have all the data
and you can actually change the world with that data,
and you can actually find solutions to problems
that you could never have before
because you have the technology
and you have that data which was never there before.
So you can actually make those kind of improvements.
>> It's all about extracting those insights.
>> Absolutely.
>> Thank you so much for coming on the show, Deepak.
It was a pleasure having you.
>> Thank you for having me.
Thank you very much.
>> I'm Rebecca Knight, for Peter Burris,
we will have more from NetApp Insight
in just a little bit.
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