>> Announcer: Live from the Wigwam in Phoenix, Arizona
it's the CUBE, covering Data Platforms 2017.
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>> Hey, welcome back everybody, Jeff Frick here with the CUBE
along with George Gilbert.
We're at Data Platforms 2017 a the historic 99 years
young Wigwam resort outside of Phoenix
and we're excited to be joined by our next guest,
Alex Sadovsky, he the director of data science
for Oracle Data Cloud.
Welcome.
>> Thanks, thanks for having me.
>> Absolutely, I know so I know we got a short time window,
you're racing off to your next session.
So, for the people that aren't here,
what are you going to be talking about in your session here?
>> So, the Oracle Data Cloud,
what we do is online advertising
and essentially we have lots and lots of data,
customers comet to us
and they have some sort of question in mind.
They want to say, I want to figure out who's going to buy a
mini-van in California next month,
or who's going to get a hotel in Las Vegas,
who's going to buy Kraft macaroni and cheese?
All sorts of different questions.
We have all of that data,
we have to turn it into actionable insights,
into audiences for them so they can advertise
Facebook, Twitter, all over the web.
And so, what this talk is really focusing on is
how do we take all of this data and use it efficiently?
And it's going to talk about the technologies that we've used
specifically Hive, and then moving that technology over
to Spark, just so that we can use more data,
get quicker processing, and essentially make our clients
have a better experience and give 'em a better product.
>> And do the clients execute the results of this process
inside their other Oracle apps,
or is it something that they can use with
any number of apps?
>> So, a lot of the ways that we work,
we actually are interfaced with companies like Facebook
and Twitter directly.
And so, essentially what we're doing is we're partnering
with them so that the client,
all they really need to do is kind of come to us
either onboard some data through maybe other Oracle
applications or onboard data directly through us
and then push it out,
we help push it all the way through the process,
all the way into Facebook, etc.
>> Yes, 'cause we were covering Oracle modern marketing,
which is now Oracle modern customer experience,
I'm sure you guys must be tightly integrated with all that.
>> Yeah, and so for Oracle Data Cloud it's kind of interesting
we're a collaboration of five recently acquired start-ups.
And so it's everything from two to three years ago
all of this coming together.
So, for us, we're really excited because we're just at the
tip of the iceberg of getting into the whole Oracle
ecosystem and having that help build up
our product even better.
>> So, when you say partner with Facebook or Twitter,
that would be for brand or direct response advertising that
one of your B2B clients has signed up for?
Or I should say, B2B, your B the client is B,
and the end customer's C, so it's a B2B2C.
And now okay, so you help them in a consultative way.
You have the data, you have a consultative sales approach,
are you building models for them?
Or are you telling them, sort of running a model?
>> Alex: Yeah.
>> Sort of which is it?
>> So, we will, we run models based upon data.
So, a customer could come to us with,
here are a thousand people that that customer knows
bought their product last month,
and they say, we want to expand our business,
we want to advertise to 20 million people
who might be similar to those thousand.
And so that's where all of our data comes in.
We can look at those thousand people and we can say,
hey did you guys know that most of
your customers are millennials?
Did you know that most of them tend to live
on the west coast or east coast populated cities?
And we're not really consulting that in the sense
of like there's people looking at the data,
it's all machine learning.
And so computers are looking at all of our data
to help get insights from what the customers bringing to us.
>> So, would it be fair to say then that the,
let's say the thousand example that the customer brings in
is the training data.
>> Yes.
>> And then you use your data in your databases,
your consumer databases, to say,
to generate essentially scores,
since they were going to send out to these.
>> That's 100% right.
They come in with a thousand of their customers,
we see how those customers rank up against every single
household in the entire United States.
>> I was going to say, we're going to be at Spark Summit in a
couple weeks or a week, whenever it is.
I can't keep track of all these shows.
So, they can't do the whole thing wiHive to Spark,
but in three minutes or less wiHive to Spark.
>> So, number one reason for us,
and number one reason I think a lot of people are
moving to Spark is just speed.
Without getting into a lot of technical details,
there's just a lot better engine,
a lot better flexible engine underneath Spark
than kind of traditional Hive.
>> And then machine learning models are,
most of the libraries are built in,
which Hive doesn't have.
>> Yeah, machine learning is really built into Spark.
There's, you know, whole projects within Spark
built around that.
And so, for us, we really, Spark considers machine learning
kind of a first class citizen.
And since that's essentially what our business is,
we go 100% into Spark as well.
>> So, let me ask you, what is the scope now
and potentially in the future for these data based
predictive models where customer comes to you with
essentially some labeled data and then you'll come out with
I guess that's the training data
and then right now you have data in what categories?
And then what categories would you like to have?
>> So, we have data everything from what people are doing
on the web, so what they're searching for,
what websites they're going for.
We have grocery store data.
So, what people are buying in the grocery store.
We have retail data.
So, what people are buying in the malls.
Because a lot of what happens is,
even though consumers are spending a lot more time on
the web, 80%-90% of purchases are still made in the store.
So, we have all of this actual real world purchase data
that we've partnered with different retail partners,
including like automotive data, too.
So that's really like the core of our data.
So, really what we try to do is have data sets
strategically placed all around
and that's why the Oracle Data Cloud is made up
of so many different start-ups,
we're really getting expertise from different areas
for different data sets to bring that together.
>> Do you need to buy those sources of data?
Or can you license?
>> Data is everything from licensed to purchased outright
to shared, revenue sharing with other companies.
It's really, there's a huge data market right now.
It's kind of the data gold rush
and we're trying get in anywhere we can,
figure out what's going to help us
and what's going to help our customers make better models.
>> What would you like to see in terms of a,
if you look out a couple years,
where would you like to see your data assets sort of
augment all your Oracle applications?
>> Yeah, so I think...
SO, augmenting Oracle really we have so many different
data assets that everything from like live streaming data,
of what people are searching for on the web,
to historically what someone has bought in the last
three years and so, as we partner more and more with Oracle,
Oracle has different things in healthcare,
in retail, in all sorts of B2B applications.
And our data really can fit almost everywhere.
It's really like a data driven sort of product.
And so, we've been partnering with Oracle left
and right many different groups just trying to figure
out where can this data help augment kind of your services.
>> Alright, Alex, well, we got to leave it there.
That was a good summary.
I know you got to race off to your thing.
I'll let you take a breath and get a glass of water.
So thanks for squeezing us in your busy day.
>> Alex: Thanks so much.
>> Alright, he's Alex, he's George, I'm Jeff,
you're watching the CUBE from Data Platforms 2017.
We'll be right back after this short break.
Thanks for watching.