>> Narrator: Live, from San Jose, it's theCUBE.
Presenting Big Data Silicon Valley.
Brought to you by SiliconANGLE Media
and its ecosystem partners
(mellow electronic music)
>> Welcome back to theCUBE, on day two of our coverage
of our event, Big Data SV.
I'm Lisa Martin, my co-host is Peter Burris.
We are the down the street from the Strata Data Conference,
we've had a great day yesterday, and great morning already,
really learning and peeling back the layers
of big data, challenges, opportunities, next generation,
we're welcoming back to theCUBE an alumni,
the CMO of Kinetica, Dan Raskin.
Hey Dan, welcome back to theCUBE.
>> Thank you, thank you for having me.
>> So, I'm a messaging girl, look at your website,
the insight engine for the extreme data economy.
Tell us about the extreme data economy,
and what is that, what does it mean for your customers?
>> Yeah, so it's a great question,
and, from our perspective, we sit,
we're here at Strata, and you see all the different vendors
kind of talking about what's going on,
and there's a little bit of word spaghetti out there
that makes it really hard for customers to think about
how big data is affecting them today, right?
And so, what we're actually looking at
is the idea of, the world's changed.
That, big data from five years ago,
doesn't necessarily address all the use cases today.
If you think about what customers are going through,
you have more users, devices, and things coming on,
there's more data coming back than ever before,
and it's not just about creating the data driven business,
and building these massive data lakes
that turn into data swamps, it's really about
how do you create the data-powered business.
So when we're using that term,
we're really trying to call out that the world's changed,
that, in order for businesses to compete in this new world,
they have to think about to take data
and create CoreIP that differentiates,
how do I use it to affect the omnichannel,
how do I use it to deal with new things
in the realm of banking and Fintech,
how do I use it to protect myself
against disruption in telco, and so,
the extreme data economy is really this idea
that you have business in motion,
more things coming online ever before,
how do I create a data strategy,
where data is infused in my business,
and creates CoreIP that helps me
maintain category leadership or grow.
>> So as you think about that challenge,
there's a number of technologies that come into play.
Not least of which is the industry,
while it's always to a degree been driven by
what hardware can do,
that's moderated a bit over time,
but today, in many respects,
a lot of what is possible is made possible,
by what hardware can do,
and what hardware's going to be able to do.
We've been using similar AI algorithms for a long time.
But we didn't have the power to use them!
We had access to data, but we didn't have the power
to acquire and bring it in.
So how is the relationship between your software,
and your platform, and some of the new hardware
that's becoming available, starting to play out
in a way of creating value for customers?
>> Right, so,
if you think about this in terms of this
extreme data concept, and you think about
it in terms of a couple of things,
one, streaming data, just massive amounts
of streaming data coming in.
Billions of rows that people want to take and translate
into value. >> And that data coming from--
>> It's coming from users, devices, things,
interacting with all the different assets,
more edge devices that are coming online,
and the Wild West essentially.
You look at the world of IoT
and it's absolutely insane, with the number of protocols,
and device data that's coming back to a company,
and then you think about how do you actually
translate this into real-time insight.
Not near real-time, where it's taking seconds,
but true millisecond response times
where you can infuse this into your business,
and one of our whole premises about Kinetica
is the idea of this massive parallel compute.
So the idea of not using CPUs anymore,
to actually drive the powering behind your intelligence,
but leveraging GPUs, and if you think about this,
a CPU has 64 cores, 64 parallel things
that you can do at a time,
a GPU can have up to 6,000 cores, 6,000 parallel things,
so it's kind of like lizard brain verse modern brain.
How do you actually create this next generation brain
that has all these neural networks,
for processing the data, in a way that you couldn't.
And then on top of that,
you're using not just the technology of GPUs,
you're trying to operationalize it.
So how do you actually bring the data scientist,
the BI folks, the business folks all together
to actually create a unified operational process,
and the underlying piece is the Kinetica engine
and the GPU used to do this, but the power
is really in the use cases of what you can do with it,
and how you actually affect different industries.
>> So can you elaborate a little bit more on the use cases,
in this kind of game changing environment?
>> Yeah, so there's a couple of common use cases
that we're seeing, one that affects every enterprise
is the idea of breaking down silos of business units,
and creating the customer 360 view.
How do I actually take all these disparate data feeds,
bring them into an engine where I can visualize
concepts about my customer and the environment
that they're living in, and provide more insight?
So if you think about things like
Whole Foods and Amazon merging together,
you now have this power of,
how do I actually bridge the digital and physical world
to create a better omnichannel experience for the user,
how do I think about things in terms of
what preferences they have, personalization,
how to actually pair that with sensor data
to affect how they actually navigate
in a Whole Foods store more efficiently,
and that's affecting every industry,
you could take that to banking as well
and think about the banking omminchannel, and ATMs,
and the digital bank, and all these Fintech upstarts
that are working to disrupt them.
A great example for us is the United States Postal Service,
where we're actually looking at all the data,
the environmental data, around the US Postal Service,
we're able to visualize it in real-time,
we're able to affect the logistics
of how they actually navigate through their routes,
we're able to look things like postal workers
separating out of their zones,
and potentially kicking off alerts around that,
so effectively making the business more efficient.
But, we've moved into this world where
we always used to talk about brick and mortar
going to cloud, we're now in this world
where the true value is how you bridge
the digital and physical world,
and create more transformative experiences,
and that's what we want to do with data.
So it could be logistics, it could be omnichannel,
it could be security, you name it.
It affects every single industry that we're talking about.
>> So I got two questions,
what is Kinetica's contribution to that, and then,
very importantly, as a CMO, how are you thinking about
making sure that the value that people are creating,
or can create with Kinetica,
gets more broadly diffused into an ecosystem.
>> Yeah, so the power that we're bringing
is the idea of how to operationalize this in a way
where again, you're using your data to create value, so,
having a single engine where you're collecting
all of this data, massive volumes of data,
terabytes upon terabytes of data,
enabling it where you can query the data,
with millisecond response times, and visualize it,
with millisecond response times,
run machine learning algorithms against it to augment it,
you still have that human ability
to look at massive sets of data, and do ad hoc discovery,
but can run machining learning algorithms
against that and complement it with machine learning.
And then the operational piece
of bringing the data scientists
into the same platform that the business is using,
so you don't have data recency issues,
is a really powerful mix.
The other piece I would just add
is the whole piece around data discovery,
you can't really call it big data if,
in order to analyze the data,
you have to downsize and downsample
to look at a subset of data.
It's all about looking at the entire set.
So that's where we really bring value.
>> So, to summarize very quickly,
you are providing a platform
that can run very, very fast, in a parallel system,
and memories in these parallel systems,
so that large amounts of data can be acted upon.
>> That's right. >> Now, so,
the next question is,
there's not going to be a billion people
that are going to use your tool to do things,
how are you going to work with an ecosystem and partners
to get the value that you're able to create with this data,
out into the engine enterprise.
>> It's a great question, and probably
the biggest challenge that I have, which is,
how do you get above the word spaghetti,
and just get into education around this.
And so I think the key is getting into examples,
of how it's affecting the industry.
So don't talk about
the technology, and streaming from Kafka
into a GPU-powered engine, talk about the impact
to the business in terms of what it brings
in terms of the omnichannel.
You look at something like Japan in the 2020 Olympics,
and you think about that in terms of telco,
and how are the mobile providers going to be able
to take all the data of what people are doing,
and to related that to ad-tech,
to relate that to customer insight,
to relate that to new business models
of how they could sell the data,
that's the world of education we have to focus on,
is talk about the transformative value it brings
from the customer perspective, the outside-in
as opposed to the inside-out.
>> On that educational perspective, as a CMO,
I'm sure you meet with a lot of customers,
do you find that you might be in this role
of trying to help bridge the gaps
between different roles in an organization,
where there's data silos, and there's probably still
some territorial culture going on?
What are you finding in terms of
Kinetica's ability to really help educate
and maybe bring more stakeholders, not just to the table,
but kind of build a foundation of collaboration?
>> Yeah, it's a really interesting question
because I think it means, not just for Kinetica,
but all vendors in the space,
have to get out of their comfort zone,
and just stop talking speeds and feeds and scale,
and in fact, when we were looking at how to tell our story,
we did an analysis of where most companies were talking,
and they were focusing a lot more
on the technical aspirations that developers sell,
which is important, you still need to court the developer,
you have community products that they can download,
and kick the tires with, but we need to extend our dialogue,
get out of our customer comfort zone,
and start talking more to CIOs, CTOs, CDOs,
and that's just reaching out to different avenues
of communication, different ways of engaging.
And so, I think that's kind of a core piece
that I'm taking away from Strata,
is we do a wonderful job of speaking to developers,
we all need to get out of our comfort zone
and talk to a broader set of folks,
so business folks. >> Right, 'cause that opens up
so many new potential products, new revenue streams,
on the marketing side being able to really target
your customer base audience, with relevant, timely offers,
to be able to be more connected.
>> Yeah, the worst scenario is talking to
an enterprise around the wonders of a technology
that they're super excited about,
but they don't know the use case
that they're trying to solve,
start with the use case they're trying to solve,
start with thinking about how this could affect
their position in the market,
and work on that, in partnership.
We have to do that in collaboration with the customers.
We can't just do that alone,
it's about building a partnership and learning together
around how you use data in a different way.
>> So as you imagine, the investments that
Kinetica is going to make over the next few years,
with partners, with customers,
what do you hope Kinetica will be in 2020?
>> So, we want it to be that transformative engine
for enterprises, we think we are delivering something
that's quite unique in the world, and,
you want to see this on a global basis,
affecting our customer's value.
I almost want to take us out of the story,
and if I'm successful, you're going to hear
wonderful enterprise companies
across telco, banking, and other areas
just telling their story, and we happen to be
the engine behind it.
>> So you're an ingredient in their success.
>> Yes, a core ingredient in their success.
>> So if we think about over the course
of the next technology, set of technology waves,
are they any particular applications
that you think you're going to be stronger in?
So I'll give you an example,
do you envision that Kinetica can have a major play
in how automation happens inside infrastructure,
or how developers start seeing patterns in data,
imagine how those assets get created.
Where are some of the kind of practical,
but not really, or rarely talked about applications
that you might find yourselves
becoming more of an ingredient
because they themselves become ingredients
to some of these other big use cases?
>> There are a lot of commonalities
that we're starting to see, and the interesting piece
is the architecture that you implement tends to be the same,
but the context of how you talk about it,
and the impact it has tends to be different, so,
I already mentioned the customer 360 view?
First and foremost, break down silos
across your organization,
figure out how do you get your data into one place
where you can run queries against it,
you can visualize it, you can do machine learning analysis,
that's a foundational element, and,
I have a company in Asia called Lippo that is doing that
in their space, where all of the sudden
they're starting to glean things they didn't know
about their customer before to create,
doing that ad hoc discovery, so that's one area.
The other piece is this use case
of how do you actually operationalize data scientists,
and machine learning, into your core business?
So, that's another area that we focus on.
There are simple entry points, things like
Tableau Acceleration, where you put us underneath
the existing BI infrastructure, and all of the sudden,
you're a hundred times faster,
and now your business folks can sit at the table,
and make real-time business decisions,
where in the past, if they clicked on certain things,
they'd have to wait to get those results.
Geospatial visualization's a no-brainer,
the idea of taking environmental data,
pairing it with your customer data, for example,
and now learning about interactions.
And I'd say the other piece is more innovation driven,
where we would love sit down with different
innovation groups in different verticals
and talk with them about, how are you looking
to monetize your data in the future,
what are the new business models,
how does things like voice interaction
affect your data strategy, what are the different ways
you want to engage with your data,
so there's a lot of different realms we can go to.
>> One of the things you said as we wrap up here,
that I couldn't agree with more, is,
the best value articulation I think a brand can have,
period, is through the voice of their customer.
And being able to be, and I think that's one of the things
that Paul said yesterday is, defining Kinetica's success
based on the success of your customers across industry,
and I think really doesn't get more objective
than a customer who has, not just
from a developer perspective, maybe improved productivity,
or workforce productivity,
but actually moved the business forward,
to a point where you're maybe bridging the gaps
between the digital and physical,
and actually enabling that business
to be more profitable, open up new revenue streams
because this foundation of collaboration
has been established. >> I think that's
a great way to think about it--
>> Which is good, 'cause he's your CEO.
>> (laughs) Yes, that sustains my job.
But the other piece is,
I almost get embarrassed talking about Kinetica,
I don't want to be the car salesman,
or the vacuum salesman, that sprinkles dirt on the floor
and then vacuums it up, I'd rather us kind of fade
to the behind the scenes power where
our customers are out there telling wonderful stories
that have an impact on how people live in this world.
To me, that's the best marketing you can do,
is real stories, real value.
>> Couldn't agree more.
Well Dan, thanks so much for stopping by,
sharing what things that Kinetica is doing,
some of the things you're hearing,
and how you're working to really
build this foundation of collaboration
and enablement within your customers across industries.
We look forward to hearing the kind of cool stuff
that happens with Kinetica, throughout the rest of the year,
and again, thanks for stopping by and sharing your insights.
>> Thank you for having me.
>> I want to thank you for watching theCUBE,
I'm Lisa Martin with my co-host Peter Burris,
we are at Big Data SV, our second day of coverage,
at a cool place called the Forager Tasting Room,
in downtown San Jose, stop by, check us out,
and have a chance to talk with some of our amazing analysts
on all things big data.
Stick around though, we'll be right back
with our next guest after a short break.
(mellow electronic music)