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NIKHIL GAEKWAD: Hi, everyone.
Thank you for joining us, and welcome to our session.
Today, we're going to learn about how Televisa Univision is
using Analytics Hub to securely exchange and share data.
My name is Nikhil Gaekwad.
I lead product management for Analytics Hub in Google Cloud.
Joining me today is Kelly Sitarski,
the Director of Data and Content Partnerships at Google.
And also, we have a special guest speaker
today, Sandro Pugliese, from Televisa Univision, who
is the VP of Data Engineering.
Before we hear from Kelly and Sandro,
I want to take a step back and talk about the world of data
sharing.
Now more than ever, customers are telling us
that shared data is critical for all lines of business.
Organizations are leveraging data from partners, suppliers,
and competitors to drive innovation and uncover
new insights.
It's becoming more and more important
for companies to not only produce, but also consume data
from third party sources.
That said, we also hear from customers
that data sharing is not easy.
Many of them are still hamstrung by traditional methods
of data sharing that comes with a number of challenges.
Number one, traditional data sharing techniques require
batch data pipelines that extract data from databases,
stored in flat files, transfer them to customers,
where they're then ingested into another database.
Not only are these pipelines expensive to run,
all the changes that's required to the source data
can cause them to break.
Sharing of flat files also result
in multiple copies of that data, which in turn,
results in unnecessary costs, especially when
you have multi petabyte data sets as we do today.
The time that's required for batch of pipelines
also means that the data is already
stale by the time it reaches the customer.
Again, this is not going to help businesses
make timely decisions that's required today.
Lastly, traditional data sharing techniques
also bypass data governance processes.
So as a data provider, you'd want to know
who's using your data, how is it being used.
And all of that is lost in the mix with traditional ways
of sharing data.
What happens at the end?
There is no doubt that these challenges makes
it difficult for organizations to realize
the true potential of data assets,
and how they can be used to solve their business
challenges.
To overcome these limitations and make
it easier for data sharing, we introduced Google Cloud,
Analytics Hub.
Analytics Hub is a new service that
helps organizations share data and insights at scale
in a privacy safe and secure way.
With Analytics Hub, you can create a rich data ecosystem
that allows you to publish and subscribe to data assets that
are analytics ready.
Because the data is shared in place,
data providers are able to control and monitor
how that data is being used.
More importantly, Analytics Hub provides data customers
a self-service way to discover and access valuable and trusted
data assets.
And this includes data that's provided by Google.
Finally, Analytics Hub provides an opportunity
for organizations to monetize their data assets.
Our goal is to enable data providers
to offer their data to customers in an efficient way,
and manage the entire data delivery process.
Here's how Analytics Hub works behind the scenes.
Starting on the left, as a data publisher,
you bring your data into BigQuery,
and you create what we call shared data
sets, which contain the tables and views you want to share.
Then you publish these data sets into an exchange
with additional information about the data,
such as documentation, sample queries, et cetera.
And these become your data set listings.
Exchanges can either be private or public
depending on your use case.
And finally, on the right, as a data subscriber,
you come in to BigQuery and Analytics Hub,
you search through all the listings across the exchanges
that you have viewer subscribed access to.
And then you subscribe to the data
set that you're interested in.
This then creates a linked data set
in your project that can either be queried or joined
with other data, super intuitive and easy.
With that, I want to take this moment
to make a very special announcement.
Today, we are officially announcing
the general availability of Analytics Hub.
We've come a long way since we announced this product
last year.
And I want to thank all of you for your support and feedback
throughout the way.
And here's what's new with the release.
First, the product now supports over 30 different regions
and multi regions in BigQuery.
This enables data publishers to deliver shared data sets
to the regions that's requested by their subscribers.
Number two, data publishers can now view and manage
subscribers for all their shared data in Analytics Hub
If necessary, they can also revoke access
to subscriptions at any time.
Number three, to govern share data,
administrators can now have access
to tools such as audit logging, information schema
to track some job statistics about the usage of the product
within the organization.
Last, but not least, we've also made improvements
in our search and discovery experience that
make it easy for subscribers to filter, sort, and find
relevant data.
All right.
Now that we've learned a lot about the product
and its capabilities, let's hear from one
of our early adopters of Analytics Hub,
and learn about how they've overcome their data sharing
challenges with this ecosystem.
Kelly, over to you.
KELLY SITARSKI: Thank you, Nikhil.
So we're joined today by Sandro, who
is the Vice President of Data and Engineering
at Televisa Univision.
So we know Televisa Univision as this powerhouse in the industry
across broadcasting different types of media.
I'd like to hear from you about your journey there,
and what brought you to Televisa Univision.
ALESSANDRO PUGLIESE: Absolutely.
And first off, thanks for having me.
So I think before I talk about Televisa Univision,
I'd like to talk about the community it serves.
So the Hispanic community in the United States
is about 60 million people.
That's about 20% of the US population.
The Hispanic community is projected
to represent about 50% of the population growth
in the United States going forward, which is huge.
And then globally, there are about 600 million people
who speak Spanish.
That's a huge, huge community.
And I think I was shocked when I dug
in as part of my onboarding process
here to find out how underserved they actually are.
So to me, the opportunity was to come into an organization
and actually help drive change for an underserved community.
That was important to me.
Televisa Univision itself has undergone transformation
with new ownership and new leadership
over the last couple of years.
I was brought in to lead the data engineering efforts
as part of that transformation, because we all
know that data is always in the mix,
and always critical to actually help organizations meet
their new, aggressive goals.
So that that's how I landed here.
Thinking about data strategy, you come into an organization
and you have to get the lay of the land, what exists.
Well, Univision is a legacy broadcast company
that has been transforming to digital.
We've launched VIX and VIX plus, which
is a free and subscription based app and service
with a tremendous amount of original Spanish first content.
So thinking about how can I serve the new needs, right?
We have AI, ML data science teams coming up with models.
We have marketing teams, finance teams, BI teams,
each of those stakeholder groups have
different needs on when and how they want to access data.
So my first goal was, well, the existing systems
were not up to the challenge.
So we took it upon ourselves to develop a new platform.
And when you do that, then you need
to start thinking about aggregating your data sources,
and aggregating and migrating your data consumers.
And I was thinking about which one I wanted to pursue first.
And leadership always cares about driving value.
That's really what it comes down to.
Then it's reducing costs.
We all know that.
So how could I migrate the consumers
and drive value faster?
So that's where Analytics Hub first presented itself to me
as an option on how can I migrate data, and more
importantly, migrate consumers to a new platform that
provides all of the benefits that we're looking for.
KELLY SITARSKI: So that's a great segue
to what we're here to talk about, which is Analytics Hub.
So can you share a little bit about how you're
leveraging Analytics Hub today?
What are some of the use cases?
ALESSANDRO PUGLIESE: There's two use cases.
The first one I want to talk about is a data migration.
So when I think about a new service or product,
I don't jump in to the deep end right away, right?
So for the purposes of this, I think about it
as honestly, it's like an aperture, right?
I start the aperture small, and then I
widen it as my confidence in it, the, team's confidence in it,
leadership's confidence in it all grow.
So the first use case was data migration.
So in a standard pattern, and you
can see in this very, very high level diagram I put together,
in a standard pattern, you would need
to actually bring in the data to your new platform.
Then you'd need to reconcile it with the existing systems
and get buy in from the stakeholders.
Then you would need to migrate your consumers.
And then eventually, you'd realize value.
I mean, when you're talking about 20, 30,
50 data ingestions, and tens or hundreds of consumers,
that could be a six to 18 month process, right?
I don't know any businesses that want to wait
18 months to realize value.
So when you look at the bottom portion of this screen,
it kind of flips the narrative, right?
So what we did is we identified key data sets
that existed in existing systems,
and we configured an Analytics Hub share to our new platform.
And I think it took all of about five minutes per data set.
Then all of a sudden, they were available in the new platform
and we could immediately start migrating consumers,
which means that we get to value realization earlier.
And then all of that heavy lifting, which usually
is predecessor work, now becomes debt
that we need to remediate on our own timeline and our own budget
while the business is already driving value.
So like, from my perspective, that was huge.
And it easily bought me six months on my timeline,
and saved costs.
KELLY SITARSKI: That's great.
ALESSANDRO PUGLIESE: Win and win.
KELLY SITARSKI: For sure.
So looking forward, we just heard from Nikhil
some of the new features that are going to be
available in Analytics Hub.
What's next?
Where do you go from here on your journey?
ALESSANDRO PUGLIESE: So before we get to that,
I'd love to talk about the second use case.
As I talked about the aperture, I started narrow.
And as we started developing confidence and trust
that the product actually did what it said it did--
no offense to you--
pre GA products give us all a little bit
of the jitters in prod.
I had a second use case where now all of a sudden,
I had vendors.
And they were within our GCP ecosystem,
but they had their own siloed projects.
So they were managed.
They were separated, but they were still controlled
by our Cloud ops team.
They had a need for a specific data set.
It's a decent sized data set coming in on a daily basis.
And they all needed the exact same data set.
So in a traditional pattern, you would potentially
do views, which didn't work for us for a variety of reasons,
or you would actually ship them the data, which
means all of a sudden I 5x the processing and 5x the storage.
So again, here, Analytics Hub presented itself.
We configured the shares, and that data set now
became available to all five vendors within minutes.
And again, we maintain control, and we're not
propagating and duplicating data, so again, cost savings,
we're not having to maintain fivee additional pipelines
which are subject to breaks, and prod, and things like that.
So again, that was a huge win to be
able to enable these vendors, which
are a critical part of one of our primary stakeholders
initiatives.
So again, a win.
KELLY SITARSKI: That's great.
That's exactly what we want to hear.
So let me ask you then, as you look forward
on your journey, what's next?
Where do you go from Analytics?
Where do you go with Analytics Hub from here?
ALESSANDRO PUGLIESE: Absolutely.
So not to beat the dead horse, but widen the aperture further,
right?
We started with just our own internal data.
Then it was internal plus with vendors,
but still in our ecosystem.
The next step to me, are external data exchanges, so
third party partners, vendors, you name it.
And that's both ingestions and exports, right?
Third party exchanges are my nightmare.
They are the single source of, or a single biggest
source of prod support breaks.
They are my single biggest cost in managing.
They are also my single biggest source of delays.
So anything that I can do to kill file-based data transfers
is my dream.
And thinking about all of my ingestions,
how great would it be to just basically subscribe
to data sets that then become available
and my consumers can start right away?
I mean, we're not talking sprints or months.
We're talking hours, right?
Once the paper is signed and the deal is consummated,
then the data becomes available, and it's real time,
and it's live, and it shouldn't break, right?
So that's my hope.
That would be widening the aperture, right?
KELLY SITARSKI: That's great.
And that is a perfect segue to the next topic
we're going to cover.
But before I jump to it, I want to thank you, Sandro, so
much for sharing your story.
ALESSANDRO PUGLIESE: It was my pleasure.
Happy to be here.
And good luck, everybody.
KELLY SITARSKI: So we heard about the power
of data sharing.
Now, let's switch gears and talk about the different data
types that are available in Analytics Hub,
because that's where a lot of the power lies,
the rich data ecosystem, and combining different data
sets to derive unique insights.
We have public data sets for climate, cryptocurrency,
health care, and transportation, as well as
data that only Google can provide, like Google Search
trends.
From an industry perspective, we're
working with our industry teams to enable customers
to do things like benchmarking against standardized industry
metrics so you can see where you stack up in your industry.
For internal sharing, we've seen a lot of momentum
around customers building their own internal exchanges.
We know that data sharing can be challenging
for larger organizations.
So if your marketing team wants to share
with your finance, or your data science teams,
Analytics Hub can really help.
The last two categories, private and commercial exchanges
are ones I want to spend a little bit of time on.
First, is private exchanges.
Private exchanges, or B2B exchanges, this
is where data is shared between companies,
so like a retailer with a distributor,
or a media company with a brand.
We have customers and partners doing this at scale today.
For example, NGIS, a global leading provider
of geospatial and sustainability solutions
is using Analytics Hub to provide first mile sustainable
sourcing data sets for its customers.
These data sets deliver traceability and transparency
in global supply chains, to sustainably sourcing
new materials, helping customers monitor their supplier's
deforestation, and contributing to global net zero initiatives.
We're also working with SoundCommerce, a retail data
and analytics platform that offers
a unique way of connecting marketing, merchandising,
and operations data all within a retail context.
They're using the Analytics Hub to make
it easy to land and maintain complex data sets,
so brands and retailers can drive profitable shopper
experiences with every decision.
So within our catalog, we have hundreds of public high value
data sets, as well as commercial data sets.
These are the ones that require a license from a third party
data provider.
We have data sets available in our catalog today
for you to subscribe to from across industries,
and across use cases, from leaders like Dun
& Bradstreet and FactSet.
And we will continue to build out
this catalog with our partners.
These data sets are critical to our customers
to execute highly processed intensive analytics workloads
for trading and risk calculations.
So to close this out, I want to thank you all for your time.
I want to encourage you to check out Analytics Hub today.
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