>> Live from San Francisco,
It's theCUBE.
Covering Google Cloud Next 2018.
Brought to you by Google Cloud
and it's Ecosystem partners.
>> Hey, welcome back everyone.
We are live in San Francisco,
CUBE coverage for Google Cloud Next 18.
I'm John Furrier, Jeff Frick.
Our next guest is Carol Carpenter,
Vice President of product marketing here at Google Cloud,
and Navid Erfani-Ghadimimi, welcome to theCUBE.
>> Thank you very much.
>> Thanks for coming on.
So data for good has been a topic
we were just talking about here, day three
What do you guys do?
And what's your relationship with Google?
Because big data for good is really,
with cloud computing more relevant than ever before.
Take a minute to explain your project.
>> Sure, so in South Africa we are
a social nonprofit organization.
We try and connect young people that are not employed,
never employed to opportunities.
And we are hosted in Google Cloud,
and we use GCP as our sole provider.
And what we try and do is we use data
to be able to understand young people,
understand the facets that make a young person employable
and match them to opportunities that we find.
So we describe opportunities using different data points.
So all those data points that we have,
we store them in Cloud Sequel,
and we store them in BigQuery.
And then we run analytics and matching
to be able to find how these young people
can contribute to the economy.
>> How's it going so far?
>> So far it's been great.
It's allowed us to think about the 10X strategies.
When we were an on PRAM business,
we very limited by what could provide, bricks and mortar,
and now we're looking and saying,
well how do we provide as much capacity and capability
to these young people using cell service channels?
So it really has just opened up a world of possibilities.
And we're really looking at it.
And we're very excited because we've taken
on some initiatives in Rwanda as well.
And so we're taking on a global
and Africa-wide kind of strategy,
which I think without Cloud we really wouldn't
be able to do.
>> I wonder if you could just drill down,
because what are some of the data points
that you look at and you measure?
And is it identifying the data points
and then finding the match?
Or is it finding the critical ones
that you really need to address as a priority
to get kids to that position where they can get a job?
>> I mean it's really interesting
because what we talk about,
we talk about proxies for competence.
So if you think about when you go apply for a job,
you kind of say hello, here I am
and I've done this job for so many years,
and that's your proxy for competence.
So if you're a young person
that just has a high school education
and you're stepping in,
we need to be able to describe you as a human, right?
So for those things we look and say,
what are your biographic information?
What's your socialization?
What kind of grit and energy do you bring to the job?
So we try and measure those things
and we have as many contact points as we can get
to be able to understand,
who is this individual, really?
And use those data points,
and we have about 155 aspects that we use right now,
and then match them to different entry-level jobs.
>> So you're the Enterprise Architect
of Harambee Youth and Employment Accelerator.
I love that term, accelerator.
>> Yes, right.
>> And I also love the term Enterprise Architect,
because both are indeed of some clout.
One of the themes is digital transformation,
which is kind of a generic term,
the analysts all talk about it.
But really we're talking
about the cloud mobile digital world
and the power that can bring.
Accelerator on the youth side, they need an app.
So you're essentially providing a digital capability,
not the old brick and mortar.
>> That's right.
>> How do you architect all of this?
Because you got to assume there's an app at the edge,
either a downloadable app or website, phone--
>> So we have actually quite an interesting problem to solve,
because for our young people,
they don't have access to apps.
The majority of our young people
are on feature phones, basic phones, not smart phones.
And data in South Africa is very expensive.
So for that young person,
we need to provide as low a touch
at a connection point, to our services,
without making that cost them something, right?
So we built a very basic Mobi site,
no JavaScript, as blank as you can get.
It's very boring if you look at it.
>> So lightweight.
>> Very lightweight.
But it's the tip of an iceberg.
So from there we collect certain information,
but then we have an award-winning contact center
that makes 35 thousand calls every month.
And we engage with a young person
in an up down poll for about 15 minutes.
And it's that 15 minutes that we use
to talk to this young person,
understand about them,
figure out who they are and what they are,
and use that to gather our data points.
We then have assessments that we run.
So we run psychometric assessments,
we have competence assessments,
and we gather all those data points
and we start understanding this young person
in a way that we can go to an employer,
because on that side for the employer,
we need to be able to say you trust us
that when we give you this young person,
that we say this person will do well in your job.
Well you have to have trust in us
to be able to do that.
So we need to provide that data to say
well, this is how we came up with it.
So we take quite a lot of effort in that.
>> You're verifying in a way,
putting your reputation on the line with the candidates.
>> Yes.
>> At the same time, you don't know
when the inbound touch is going to happen,
so you got to have all that material ready to go.
>> That's right.
>> That's where the big data kicks in.
>> That's right.
So the big data, the collection of that information,
and the understanding of it...
And we're on a journey to start figuring out,
how can we use artificial intelligence,
how can we use ML in a way that improves our accuracy,
but at the same time,
leaves out anything that may be biased
toward these young people.
So we're taking a very cautious approach to it.
But it's a lot of big data.
We're trying to consume it as best we can.
Plus, we're trying to think about,
how do we provision our services for the employers?
Because again, it's a demand at business,
so we want to find as many jobs as we can
so we can take young people to those jobs.
So extend our reach to the employers and--
>> The heavy lifting, so that they don't have to.
>> Yeah, so they don't have to.
>> Carol, talk about the dynamic with Google Cloud,
because this is the theme we're hearing all week.
You guys do the heavy lifting,
and at the edge of the user experience,
you take the toil out of it.
The word toil has been--
>> It keeps coming up.
Thinking of that toil,
the hard work, friction out of it.
In this case, the connectivity costs,
being productive at that point of transaction...
>> Exactly.
>> They're doing the back end heavy lifting.
This is kind of like a core theme across.
>> That is what the promise of the Cloud
is supposed to be, right?
Which is to remove all that back end toil,
I love that word too,
the toil, the mundaneness of it all,
so that folks like Harambee can actually focus
on delivering great service
to both potential employers and employees.
So we're trying to automate as much of that infrastructure,
that's what we announced a lot
around serverless, around containers,
this idea of you don't need to worry about it.
You don't have to provision the server now.
You don't have to worry about patches.
You don't have to worry about security.
We'll take care of that for you.
>> I just love your phrase proxy for competence,
and I can't help but think,
I've got kids in college that you know,
that's the whole objective of the application, right?
We've got SATs and PSATs and they take a couple data sets,
but relative to the number of data sets that you describe.
And I would the intimacy of those data sets,
versus an ACT an SAT and a transcript.
You probably have a really interesting insight,
and if you can correlate to the proxies of competency,
this is something that has a much greater
kind of opportunity than just helping these kids
that you need to help and it's really important.
But that's a really interesting take,
to use a much bigger data set,
sophistication, great tools and infrastructure
to do that mapping of competency to that job.
>> Absolutely, and we're very focused on understanding,
how do we use this data to provision a network
for our young people to be able
to describe themselves in entry?
So one of the things we found in South Africa,
and I'm sure it's a fairly universal problem,
is that if you are unemployed,
one of the things that prevents you from finding employment
is you cannot access a network
in which people that have jobs or describe jobs,
you don't have access to that network.
And so the ability to stand up and say,
hey, this is who I am, these people have said,
this is my profile as an individual,
and say Harambee, or whoever it is,
says that I am competent in these things.
That gives them an in,
that gives them some way of entering that network.
And for instance, we've done a certain study that said
that if a young lady takes just a basic CV
that has a stamp on it from Harambee
with a description of who they are
and what their competencies are,
that improves their chances of finding a job by 30%,
up to 30%, and that's significant, right?
And this is not us finding the job for them,
this is them going out and looking for a job,
so it's describing and helping
this person enter that network
by providing, again, a proxy for competence.
>> Talk about the relationship with Google.
What is Google working with you guys on?
And what's next for you guys?
>> Google has helped us immensely.
We receive those credits, and those credits allowed us
to take that first step into the cloud.
They gave us a little bit of breathing room, alright,
so we could take that step.
We also have access to some Googlers,
that have helped talk to us a little bit
about ML and they have been helping us out on that.
In terms of the next steps, it's 10X time.
It's time to grow,
it's time to use this scale,
it's time to use the opportunity that we have
to make the real impact that we've been searching for.
>> Connect those jobs to those folks.
>> Absolutely, because this is not a small problem.
We've got a big problem to solve
and we're really excited to be able to do it.
>> I'm glad you're doing that.
>> Awesome.
>> It's a great, great mission.
Carol, I want to get your thoughts finally,
just to kind of end this segment
and kind of end our time here at Google Cloud.
Good opportunity for someone who's been looking
at the landscape of the products.
What's been the vide of the show,
from your standpoint?
Obviously you've been planning this for months,
it's showtime, it's coming to a close,
we're day three, you heard, it's going to close in 30 minutes.
Are you happy?
>> Yeah, I mean we're thrilled. We're thrilled.
We were just talking earlier,
it's been a tremendous three days of just great interaction
with fantastic customers, partners, developers,
it's just the level of engagement...
Google Cloud is about making the Cloud
available for everyone.
We wanted this to be a place for people
to engage, to make things, to try things, to be hands-on,
to be in sessions with people like Harambee,
to actually understand what the Cloud can do.
And we're super excited.
We've seen that in spades.
The feedback has been tremendous.
I hope you heard that as well.
We're really excited.
We believe that the capabilities we have
around what we're doing in data analytics, machine learning,
on top of this incredibly robust infrastructure,
we really believe that there are amazing problems
we can solve together.
>> We had a couple of our reporters
here earlier saying people who think Google
is far behind is not here at the event.
I got to say, give you guys some props,
you guys are bringing...
We know you've got great technology,
everyone kind of knows that, who knows google,
certainly knows the size
and the scope of the great technology.
But you're making it consumable.
And you're thinking about the enterprise,
versus we're Google.
Use our great stuff because we use it.
You're like Google.
People aren't like Google
because no one has that many servers. (laughs)
Right.
So it's self-awareness.
This has really been a great stride you guys have shown.
And the customers on stage.
>> Oh, they're fantastic.
>> That's the proof in the pudding.
At the end of the day--
>> They're fantastic.
Showing how you can actually apply it,
how you can apply AI, machine learning
to actually solve real world problems,
that's what we were most excited about.
Like you said, lots of great technology.
What we want to do is connect the dots.
>> And Diane Greene I thought of,
my favorite soundbite was security is number one,
worry, AI is the number one opportunity.
>> Absolutely.
>> I think if you look at it from that lens,
everything falls into place.
>> Absolutely.
>> Well thanks for coming on,
thanks for having theCUBE this week, Google.
And congratulations on your great venture,
and good luck with your initiative.
>> Thank you very much.
>> Thank you both.
>> Alright that's theCUBE coverage here,
live in San Francisco.
I'm John Furrier, Jeff Frick,
Dave Vellante went home last night.
He's in our office taking care of some business.
I want to thank everyone for watching.
And that's a wrap here from San Francisco.
Thanks for watching.