>> Voiceover: From San Jose, California,
it's The Cube, covering big
data Silicon Valley 2017.
>> Welcome back, everyone.
Live in Silicon Valley for BigData SV,
BigData Silicon Valley.
This is Silicon Angles,
The Cube's event in Silicon Valley,
with our companion event, BigData NYC,
in conjunction with
O'Reilly, Strata, Hadoop,
Hadoop World, our eighth year.
I'm John Furrier, my co-host Jeff Frick,
breaking down all the action,
and our superguest, Abhi
Mehta, the CEO of Tresata.
He's been on every year since 2010,
and the CEO of very successful Tresata,
building out the vertical approach
in financial net health.
Welcome back, good to see you.
Thank you, John, always good to see you.
>> The annual pilgrimage
to have you on The Cube.
>> Abhi: This is literally
a pilgrimage.
I was exchanging messages
with your co-host here,
and he was pinging me, saying,
"You got to come here, you
got to get to this thing."
I made it.
The pilgrimage is successful.
>> Yeah, a lot's happened, right?
Data's the new oil.
We've heard it over again.
You had the seminal
first interview in 2010,
calling the oil refineries
the data refineries.
Turns out that was true.
We always love to talk
about that prediction
every time you're on, but
it's so much going on now.
You can't believe the shift.
Certainly, Hadoop has got a
nice little niche position
as Batch, but real time processing,
you've seen the convergence
of Batch, and streaming,
and all that good stuff in real time,
with the advances of clouds,
certainly, more compute,
Intel processors are
getting more powerful,
5G over the top, you have
connective cars, smart cities,
on and on, IoT, Internet of things,
all powering this new deep
learning and AI trend.
Man, it is game changes.
I see this as a step-up function.
What's your thoughts?
This is going to create
more data, more action.
>> I agree with you.
I always remind myself, John,
especially when I talk to you guys,
and we were chatting about this
right before we went on air, which is,
as smart as we as humans
are, trends repeat themself.
I'll be talking about AI.
We all went to school, and
did things in AI, you know?
The whole neural networks
thing has not been new.
It's almost like fashion.
Bell bottoms come in
fashion every 20 years.
I will never be seen in them again.
Hopefully, neither will you.
AI seems to be like that.
I think the thing that hasn't changed,
and yes, absolutely agree with you,
that as escrows shift, as you've said,
almost at this point a decade ago,
there's a fundamentally new
technology escrow shift under way,
and escrow shifts take time.
We will look back at this 10 years saying
it was literally the first, second inning
of this new escrow shift.
I think we are entering the second innings
where the conversation around
Batch, real time storage,
databases, the stacks, is
becoming less important,
and AI and deep learnings
are examples of it,
conversations on, how can you leverage
cheaper, better, faster technology
to solve and answer unanswered problems
is becoming interesting.
I think the basics haven't changed though.
What we have spoken with you
for almost eight years remain the same.
The three basics around
every technology trend
remain the same.
I think you guys will agree with me.
Let me just play it by you
and you can either contest
it or agree with me.
Data is the new competitive effort.
It is unequivocally
clear that the new asset,
the most valuable enterprise
asset has become data,
and we've seen it in data companies,
Facebook, Google, Uber, Airbnb,
they're all fundamentally data companies.
Data is the new competitive effort.
The more you have of it,
the better off you are.
I always love people who say,
"Big Data, this is a bad term."
It isn't, because big data, fundamentally,
in those two words,
defines the very pieces
of what we built Tresata on,
which is, the more data you have,
and if you can process and
extract intelligence from it,
borrowing your term, extract
signal from the noise,
you can make a lot of money on it.
I think that fundamental
basic hasn't changed.
>> Big Data, to me, was always about
big storage kind of a view.
We coined the term Fast Data on The Cube,
so that now speaks to the real time.
It's interesting.
I just see that the four main new areas
that are being talked about
outside of the Big Data world
are autonomous vehicles,
smart cities, smart home,
and media and entertainment,
and each one of those, I would say that
the data is the new weaponization.
There's an article that
was great this month
called "Weaponizing AI," and
it had to do with Breitbart,
and the election, and that's
media and entertainment.
You've got Netflix, all
these new companies.
Data is content, content is data.
It's a digital asset.
This AI component fits
into autonomous vehicles,
it fits into media and entertainment,
fits into smart cities, and smart home.
>> You also raise a
very interesting point.
I think that we can
take comfort in the fact
that we have seen this happen.
This is not an idea anymore,
or it's not just a wild idea anymore,
which is, we have seen
massive disruption happen
in consumer industries.
Google has created a brand new industry
in how to market stuff,
could be any stuff.
Facebook created a brand new way
of not just being in touch
with your friends globally,
'cause people have thousands
of friends, not true,
but also, how do you monetize
deep preferences, right?
A twist on deep learning,
but deep, deep preferences.
If I know what Jeff likes,
I can market to him better.
I think we're about to see,
the industries you just mention,
is, where will success come
from in enterprise software?
I always ask myself that question
when I come to any of these conferences,
Strata, others, there's
now an AI conference.
What will the disruption
that we have seen happen
in consumer industries,
we'll just mention automobiles,
media entertainment,
et cetera, what is going to
happen to enterprise software?
I think the time is ripe
in the next five years
to see the emergence of
massive scale creation.
I actually don't think
it'll get disrupted.
I think we will see, just
like with Facebook, Google,
Uber, the creation of brand new industries
in enterprise software.
I think that's going to be interesting.
>> Mark Cuban said at South
by Southwest this week,
where The Cube was with
the AI lounge with Intel,
he was on stage saying,
"The first tech trillionaire
"will come out of deep learning,"
and deep learning is kind
of the underpins for AI,
if you look at all the geek stuff.
To your point that a new
shift of opportunity,
whether it comes in from
the enterprise side,
or consumer, or algorithmic side,
is that there's never been a trillionaire.
>> Abhi: No, there hasn't.
>> I want to push back a little bit,
because I don't think it
always was that way with data.
We used to have sampling.
It was all about
sophistication on sampling,
and data was expensive to
store, expensive to collect,
and expensive to manage.
I think that's where the
significant change is.
The economics of collecting,
and storing, and analyzing
are such that sampling is no
longer the preferred method.
To your point, it's the bigness.
>> Absolutely, you know exactly
where I stand on that.
>> Jeff: Now it's an asset.
>> You know exactly where I stand on that.
I said on The Cube, at this
point, almost a decade ago,
sampling is dead, and it's
for that particular reason.
I think the reality is that
it has become a very tricky area to be in.
Buzzwords aside, whether
it's deep learning, AI,
streaming, Batch, doesn't matter, Flash,
all buzzwords aside, the
very interesting thing is,
are we seeing, as a community,
the emergence of new enterprise
software business models?
I think ours is an example.
We are now six years old.
We announced Tresata on The Cube.
We have celebrated our significant
milestones on The Cube.
We'll announce today that we are now
a valuable member of society in terms of
you pay tax as a company,
another big milestone for a company.
We have never raised venture money.
We had a broad view when we started
that every single thing we have learned
as a industry enterprise
software, the stack,
databases, storage, BI,
algorithms are free.
Dave was talking about
this earlier this week.
Algorithms, analytical
tools, will all become free.
What is this new class
of enterprise software
that creates value that
can then be sold as value?
Buyers, corporations are becoming smart
to realize and say,
"Maybe I can't hire people
"as smart as some of the web industries
"on this side of the coast,
"but I can still hire good
talent, the tool set is free.
"Should I build versus buy?"
It fundamentally changes the conversation.
Databases is a $2 trillion industry.
Where does that value shift
to if databases are free?
I think that's what is going
to be interesting to see, is,
what model creates the new
enterprise software industry?
What is that going to be?
I do agree with Mark Cuban's statement,
that the answer is going to lie in,
if the building blocks
are free and commoditized,
you guys know exactly
where I stand on that one,
if the building blocks are commoditized,
how do you add value
in the building block?
It comes from the point you made,
industry knowledge, data,
owning data and domain knowledge.
If you can combine deep domain expertise
to be an advanced application
that solve business problems,
people don't want to know
if the data is stored
in a free HDFS system,
or in some other system,
or quantum computing, people don't care.
>> I got to get your
take on the data layer
because this is where it's come.
We had a lot of guests on saying,
with the cloud, you can rent
things, algorithms are free,
so essentially,
commoditization has happened,
which is a good thing, more compute,
everything else is all great,
all the goodness around that.
You still own your data.
The data layer seems to
be the LAN grab, metadata.
How do you cross-connect the data layer
to be consistent fabric?
>> Here's how we think of it,
and this is something we
haven't shared publicly yet,
but I believe you see us
talk a lot more about this.
We believe there are three new layers
in the technology fabric.
There is what we call the
hardware operating system.
The battle has been won by a company
that we all like a lot, Red Hat,
I think mostly won.
Then there is what we call
the data operating system,
what you call the data layer.
I think there's a new layer emerging
where people like us sit.
We call it the analytics operating system.
The data layer will commoditize
as much as the hardware operating system,
what I call the layer, commoditized.
The data operating system fight is moot.
Metadata should not be charged for.
Massive data management,
draining the swamp,
whatever you want to call it,
every single thing in
the data operating system
is a commodity where you need volumes,
you all are businessmen,
you need volumes, in the P times V game,
you need volumes to sustain
a profit business model.
The interesting action, in my opinion,
is going to come in the
analytics operating system.
You are now automating
hardcore, what I call,
finding intelligence questions,
whether it's using deep learning, AI,
or whatever other buzzword
the industry dreams up
in the next five years,
whatever the buzzwords may be,
immaterial, the layer that automates
the extraction of intelligence
from massive amounts
of data sitting in the data layer,
no matter who owns it, our opinion is,
Tresata, as an enterprise software player,
is not interested to be a data owner.
That game, I can't play anymore, right?
You guys are a content company, though.
You guys are data owners,
and you have incredible value
in the data you're building.
For us, it is, I want
to be the tool builder
for this next gold rush.
If you need the tools
to extract intelligence from your data,
who's going to give you those tools?
I think all that value sits
in what we call the
analytics operating system.
The world hasn't seen
enough players in it yet.
>> This is an interesting mind
bender, if you think about it.
When you said, "analytics
operating system,"
that rings a few bells
and gets the hair standing
on the back of my head up
because we're in a systems world now.
We kind of talk about this in The Cube
where operating systems
concepts are very much in play.
If you look at this ecosystem
and who's winning, who's
losing, who's struggling,
who's falling away, is,
the winners are nailing
the integration game,
and they're nailing the functional game,
I think, a core functional component
of an operating environment,
AKA, the cloud, AKA data.
>> Agreed.
>> Having those functional systems,
as an operating system game.
What is your view of what an
analytics operating system?
What are some of those components?
I mean, most operating systems
have a linker, loader, filer,
all these things going on.
What's your thoughts on this
analytical operating system?
What is it made of?
>> It's made of three core components
that we have now invested six years in.
The first one is exactly what you said.
We don't use the word integration.
We now call it the same word,
we have been saying it for six years,
we call it the factory,
but it's very similar,
which is, the ability to go to a company
or enterprises with unique data assets,
and enrich, I will borrow
your term, integrate, enrich.
We call it the data factory,
the automation of 90% of the workload
to make data sitting in a
swamp usable data, part one.
We call that creation of a data asset,
a nice twist or separation
from the word data warehousing
we all grew up on.
That's number one, the ability
to make raw data usable.
It's actually quite hard.
If you haven't built a
company squarely on data,
you have to be able to buy it
because building is very hard, number one.
Number two is what I call
the infusion of domain-centric knowledge.
Can industries and industry players
take expert systems and convert
them into machine systems?
The moment we convert expert
systems into machine systems,
we can do automation at very large scale.
As you can imagine, the
ability to add value
is exponentially higher
for each of those tiers,
from data asset to now
infusion of domain knowledge,
to take an expert into a machine system,
but the value trade is
incredibly large as well.
If you actually have the system built out,
you can afford to sell
it for all the value.
That's number two, the
ability to take expert system,
go to machine systems.
Number three is the most interesting,
and we are very early in it.
I use the term on The Cube,
I'm going to be more
forward-thinking over here,
which is automation.
Today, the best we can do
with leveraging incredibly
smart machines, algorithms,
at scale on massive amounts
of data is augmenting humans.
I do fundamentally believe,
just like self-driving cars,
that the era where software will automate
a tremendous amount of business processes
in all industries is upon us.
How long it takes,
I think we will see it
in our lifetimes too.
When you and I have both a
little bit more gray hair,
we're saying, "Remember,
we said about that?
"I think automation's going to come."
I do believe automation will happen.
Currently, it's all about augmentation,
but I do believe that business--
>> John: Cubebots are coming.
We're going to have some Cubebots.
>> We will have Cubebots.
>> John: Automated
Cube broadcasting.
>> John, we'll give them
your magnificent hair,
and they know they'll do it.
I do believe automation of
complex human processes,
the era of enlightenment, is upon us,
where we will be able to take
incredibly manual activities,
like hailing a car today,
to complex activities, looking
at transaction information,
trading information, in split second time,
even quicker than real time,
and making the right trading decision
to make sure that Jeff's
kids go to college
in a robo-advisor-like mode.
It's all early, but the
augmentation will transform
to automation, and that
will take some time
to do them at three tiers in the AOS.
>> Then, if we are successful
at converting the expert
to machine system,
will the value of that expert system
quickly be driven to zero
due to the same factors
that automation has added
to many other things
that have been sucked in?
>> You guys always blow my mind.
You always push my
thinking when I come here.
>> I just love the concept, but then,
will the same economics that have driven
asumtotically approaching zero costs,
then now go to these expert systems?
>> You know the answer.
The answer is absolutely, yes.
The question then becomes,
how long of an era is it?
What we have learned in technology
is escrow shifts take time.
This era of enlightenment,
what I'm calling the era of enlightenment,
that enterprise software
is about to enable,
and leaving aside all other buzzwords,
whether it's deep learning,
AI, machines, chatbot,
doesn't matter, the era of
enlightenment is absolute.
I think there'll be two things.
First of all, it'll take time to mature.
Yes, whether it's 50 years,
40 years, or 30 years,
does it, at some point,
become it's own commodity?
Absolutely.
The marginal value we can
deliver with a machine,
at some point, does go to zero,
because it commoditizes it,
at scale, it commoditizes it, absolutely,
but does that mean the next 30 years
will not be a renaissance
in enterprise software?
Absolutely not.
I think we will see ...
Let's take the enterprise IT market,
what, two to three
trillion dollars a year?
All of it is up for grabs,
and we will see in the
next 20, 30, 40, 50 years
that, as it is up for grabs,
tremendous amount of
value will be re-traded
and recreated in completely
new industry models.
I think that's the exciting part.
I won't live for 50 years, so it's okay.
>> I know we got a minute or so left.
I want to get your thoughts on something
that we're seeing here, The
Cube this year pointed out.
We've kind of teased around it, but again,
Batch and real time process streaming,
all that's coming together.
The center of that's IoT data and AI,
is causing product gaps.
There are some gaps that are developing,
either a pure play Batch player,
or your real time, some people
have been one or the other,
some are integrating in.
When you try to blend it together,
there's product gaps, organizational gaps,
and then process gaps.
Can you talk about how
companies are solving that?
Because one supplier might
have a great Batch solution,
data lake, some might have
streaming and whatnot.
Now there seems to be more
of an integrated approach,
bringing those worlds together,
but it's causing some gaps.
How do companies figure that out?
>> I believe there's only
one way, in the near term,
and then potentially even
moreso in the long term,
to bridge that divide that you talk about.
There absolutely is a divide.
It's been very interesting
for us especially.
I'll use our example to
answer your question.
We have a very advanced
health analytics application
to go after diabetes.
The challenge is, in order to run it,
not only do you need
lots and lots of data,
IoT, streamed, real time from
sensors you wear on your body,
you need that.
Not only do you need the
ability and processing power
to crunch all that data,
not only do you need
the specific algorithms
to find insights that
were not findable before,
the unanswered questions,
but the last point, you
need to be able to then
deliver it across all channels
so you can monetize it.
That is a end-to-end, what
I call, business process
around data monetization.
Our customers don't care about it.
They come to Tresata and they say,
"I love your predictive
diabetes outcomes application.
"I have rented the system from the cloud,"
Amazon, Azure, I think at
this point, only two players.
We don't see Google much in it.
I'm sure they're doing something in it.
We have rented you the
wheels, and the steering,
and the body, so if you
want to put it together
to run your car on the track, you could.
Everything else is containerized by us.
I call them advanced
analytics applications.
They're fully managed.
They run on any environment
that is given to them
because they are resource ready,
whatever environment they play in,
and they are completely backwards
and forwards integrated.
I think you will see the emergence
of a class of enterprise software,
what we call advanced
analytics applications,
that actually take away the pain
from enterprises to
worry about those gaps,
'cause in our case, in that
example I just gave you,
yes, there are gaps,
but we have done it enough
off a automation cycle
on the business process itself,
that we can title with the gaps.
>> Abhi, we got to go.
Glad we could squeeze you in.
>> Abhi: Thank you.
>> Quick 30 seconds, the show this year,
what are you seeing?
What's the buzz coming out of?
What's the meat, what's the
buzz from the show here?
What's the story?
>> I continue to believe
that we are in an era
that will redefine what
we have seen humans do.
The people at the show
continue to surprise me
because the questions they've been asking
over the last eight years
have slightly changed.
I'm done with buzzwords.
I don't pay attention
to buzzwords anymore.
I see a maturation.
I think I said it to you before.
I see more bald heads and big pates.
When I see that in shows like these,
it gives me hope that,
when people who grew up
in a different escrow
have borrowed a new escrow,
the pace would strengthen.
As always, phenomenal
show, great community.
The community's changing
and looking different
in a good way.
>> We feel your pain in the buzzword.
As we proceed down this
epic digital transformation,
over the top, 5G, autonomous
vehicles, Big Data analytics,
moving the needle, all this
headroom, future proofing,
AI, machine learning, thanks for sharing.
>> Abhi: Thank you so much, as always.
>> More buzzwords, more
signal from the noise
here on The Cube.
I'm John Furrier, Jeff Frick,
and George Gilbert will be back
right after this short break.
(electronic music)
