LEIGHA JARETT: Hi, everybody.
My name is Leigha Jarett, and I'm a product demonstrations
manager at Looker.
Today, I'm going to be talking about how our customers here
at Google Cloud have been using data powered
by BigQuery and Looker to make data-driven responses
to COVID-19.
I'm going to start by talking about the impetus
for creating this data block that I'll go into in a bit.
I'm going to talk about how our customers were coming to us
and needed to analyze all this publicly
available data for COVID-19.
And then I'm going to jump into a very quick overview of Looker
and BigQuery just to give you context on the different tools
we're using.
Next, I'm going to walk through the actual marketplace block
that Looker team has launched with the BigQuery
public datasets team.
And last, I'm going to talk through a couple
of our customers' stories, and how they've actually used
the marketplace block that we've created
to make data-driven responses.
So, like I said, we began getting a lot of requests
from our customers asking how they can use our tools here
at Google Cloud to actually analyze
all the data that was becoming available from COVID-19.
As a product demonstrations manager at Looker,
I'm responsible for creating different demonstrations of how
we can use Looker to analyze various data sets.
So many people on our team were reaching out to me asking,
how do we create a demo to show how we can actually
use Looker in BigQuery to analyze all of this data?
We were getting requests from all different industries.
We had government offices contacting us
who needed data to understand how they can create
different policies and state mitigations to better limit
the spread of the disease.
We had different health care institutions
that were coming to our sales teams and our engineers
asking how they could use data to prepare
for an influx of cases across the world.
And, of course, we had all of our other businesses--
e-commerce companies, real estate companies--
all asking us, how can we really make
use of all the data that's become available
and join it back with our own data
so we can make insightful decisions
and better prepare our businesses for what's
happening in the world?
At the start of COVID-19, different institutions
started releasing data.
You could hop on Google and see tons of different data sources.
Different companies, various biomedical institutions,
and universities were all coming out
with publicly available data sets
showing both testing rates and infection
rates across the world.
It was really overwhelming amount of data.
So our customers were continuously
coming to us trying to figure out what data sources to use,
how to use these data sources.
And to make matters more confusing,
each data source had its own definitions
with its own schema.
So it was really difficult to understand where do we start?
What data sources do we use?
And then what do we actually do with those data sources?
So just want to make a quick note
about the BigQuery public datasets program.
The BigQuery public datasets program
took a large amount of these different data sources,
created definitions so you clearly understood what
those data sources, where they came from,
what the metrics meant.
And, of course, they take care of all of that ETL, the
getting the data into the actual database for you each day,
so that all of our BigQuery customers
can simply query this data and have it available
without needing to figure out what data sources
they should use and how they could get it
into their own databases.
The problem with using this data set
is our analysts at all of our customers still needed
to figure out what to do with this data.
And they also had to create the queries directly in BigQuery
to get answers to their own questions.
So, of course, a query is a database
that Google Cloud powers, but you really
need to understand SQL in order to be able to ask questions
of this data.
A lot of our customers have analysts
who are familiar with using SQL, but that
means we're limiting who actually can ask questions
of the data, and we really need that technical resource
to be the person who's going in and asking
the questions for us.
This is where a Looker comes in.
And just to give everyone a bit of background on Looker
if you don't already know, Looker is a data platform,
and it's going to fit directly on top of your SQL database.
So in this case, we're talking about sitting directly
on top of our public data sets available in BigQuery.
Now, Looker is powered by a Git version-controlled modeling
layer that we call LookML.
What LookML allows us to do is actually create
a data dictionary of all of our different metrics.
We use raw SQL to define those calculations.
And then business users, folks who
are not familiar with writing SQL queries can
jump in into the Looker platform, slice and dice data,
create new reports, export the data, schedule it.
Basically, they're empowered to go in and ask
new questions of this data without needing to rely
on a technical resource.
This was also really important for us,
because like I mentioned before, the data that
was published with COVID-19 was not exactly straightforward.
We had different companies that were defining
metrics in different ways.
We had data that was getting updated
at different frequencies.
And putting this all together, we had a whole team of analysts
here at Looker trying to work on creating that LookML modeling
layer to look at the metrics related to COVID-19.
So to give you a little bit of background,
we had, you know, maybe 10 or 15 of our sales engineers
jumping in, reading up on that data,
learning the data sources, understanding the calculations
that were coming out on places like "New York Times"
or for Johns Hopkins.
And we were creating our own LookML model
to look at those calculations--
things like number of new cases that
were emerging each day, daily growth rates, average growth
rates across different regions.
And we had a pretty robust process
for going through our calculations,
testing things, making sure everything
was working appropriately.
Now, of course, we did all this to basically create
these product demonstrations to show our customers, hey,
this is how you can actually analyze this data.
But after going through this effort,
we realized we should make this model that we've
created available to all of our customers
so that they don't have to go through this same process
that we just went through.
This is where Looker's Marketplace comes into play.
Looker's Marketplace is an in-app marketplace.
So on your Looker instance, you can go up, go directly
into the Marketplace, and install what we call "blocks."
These blocks are really just pre-built pieces of LookML,
so that SQL-based modeling layer, that can help you
be making data decisions, data-driven decisions faster.
What I mean by this is we don't want our customers spending all
their time trying to understand what the different data sets
mean, where they're coming from, how often things are updated,
when they're calculated.
We want our customers to be actually using the data
to make those data-driven decisions for their business,
because that's what they're best at doing.
So our team, after creating this LookML model,
we decided to publish it into the Looker Marketplace.
What this means for our customers is anyone
could go into the Marketplace, click a button,
download the Marketplace block into their local instance,
and have our model that we've created available
for them to use, so that they're really accelerating
their time to value when it came to these public data sets.
So when our customers go and install this block
into their Looker instance, they're
going to get a few things.
First off, they're going to get two dashboards that
are international dashboards.
One, on the left-hand side, is where
you can see an international view of what's
happening across the world.
This data is primarily powered by Johns Hopkins and "The New
York Times" and some other data sources
that show things like testing or government decisions.
On the right-hand side, we can see a dashboard
that's focused on Italy.
Now, this is because the Italian government published
a few different data sets that talk in-depth about the case
spread across Italy.
Next, we have two other dashboards that
focus on the United States.
First, we have a view of the entire United States where
we can see things like hospital bed utilization and infection
rates across the country.
From here, we can drill down into a brand new dashboard
which focuses on a single state, and it shows you
all the way down to county-level information.
We're also able to see what the mitigation and policy response
has been for that state.
And, of course, we can change filters to only narrow down
to look at a couple of counties within that state
or compare multiple states with each other.
One other thing I wanted to mention
is that more recently, we've decided
to publish another block into the Marketplace focused
on the mobility data that's been published by Google.
This data talks about patterns in mobility
across the United States.
And with both of these blocks installed on your Looker
instance, you can see this data side by side
and get a clear view of how mobility patterns have
been correlated with different infection
spread across the country.
Now, when you download this block
into your Looker instance, you get those dashboards.
But even better is you have the Explore environment to actually
go and slice and dice the data and ask brand new questions.
As I mentioned before, any business user
can hop in and ask these questions,
while Looker is simply compiling that SQL query
and sending it back to the query on your behalf.
Now, we notice that this information can be confusing.
And since it's such sensitive data,
it's really important for us to make sure
that our customers understand exactly what these numbers mean
and how they were calculated.
That's why here, you can see some screenshots I've
taken where you have all the different tool tips that
show descriptions of how the metrics are calculated,
descriptions of what kind of questions
you can ask in this data-exploration environment,
and links to all the data sources
so you can go directly to the source
and understand things like how they've collected the data.
And last, of course, you got that Looker modeling layer
that's powering this entire experience.
Like I mentioned, that modeling layer acts as a data dictionary
where we basically have this dictionary of all
of our metrics that are powering both the Explore environment,
and they're powering those dashboards.
But what's really interesting about Marketplace blocks
and Looker is that you can actually extend the block,
and you can change the actual base configuration, so
modify that data dictionary, so that it fits your needs.
And better yet, you can extend it and join it
back on with your own data.
And this is the real power of this block.
And this is kind of our impetus for releasing a block like this
is we want you and all of our customers
to join it back on your data so you can get increased
insights into what's really happening
and how it's affecting your business specifically.
So I hope that after this presentation,
you're going to go ahead and check out
the Looker Marketplace.
We have a lot of documentation available on how
you can use these blocks and how you can modify them
for your own needs.
Additionally, we wanted to make sure
that you clearly understand how the modeling layer is working.
So we had our team of sales engineers
go in and write clear documentation, robust comments
to really make sure that you're understanding each calculation
that's performed.
Now what I want to talk about is a couple of our customers
who are actually using this block that we built
to help them make decisions for their business
during this difficult time.
The first I want to talk about is HCA.
And HCA is one of the nation's leading health care providers.
At the start of COVID, they came to us
with a goal of creating a national response portal.
The goal of this national response portal
was to give health care administrators and government
officials a single place to monitor
the spread of this disease across the country
so that they can make better informed decisions
on both hospital administration tasks like staffing
and utilization of resources and also for our government
officials to be making data-informed decisions
on policies.
So HCA worked with one of our partners, SADA,
to build out this portal that any of the nation's providers
can opt in to and also contribute data to.
What's really powerful about what HCA did
is they took the data that we had worked
with in our block, all this publicly available data that's
in BigQuery.
But they went ahead and joined it
on their own proprietary data from these hospitals.
This data centered around things like ventilator and ICU bed
utilization so that hospitals can log in at any moment,
focus on one geography, and see real-time analytics
about what's going on in that area and, of course,
make better plans for the future based on the information
that they're seeing.
Now, although COVID-19 was what inspired this national response
portal to be created, HCA hopes that this will live on,
because obviously, this framework is going
to be really important for other diseases and just
general public health.
The next customer I'm going to talk about is Diamond Resorts.
I'm sure many of you have heard of Diamond Resorts.
They're a timeshare company with vacation spots
around the whole world.
When the pandemic hit, I'm sure you all
realize that travel and leisure was
one of the industries that was hit hardest from the beginning.
People were probably nervous to travel.
And, of course, there were restrictions
being placed on where you could travel,
where you were going to, and where you could come from.
So Diamond Resorts had an influx of requests
coming from their customers about rescheduling
their vacations, canceling their vacations.
And Diamond Resorts really needed a way
to adapt to this influx of requests.
So what they did was they used this block as well
and used Looker to analyze this COVID-19 data, analyze it
alongside their own data around who is rescheduling,
who's canceling, and tried to predict when are people
going to want to reschedule?
Where will they be rescheduling their trips?
And basically, use all this information to plan
appropriately so that they're helping their customers get
through this difficult time and ensure
that they'll be able to reschedule these vacations when
it's safe to do so and where and when they want to go.
The last customer I'm going to talk about today is Garten.
And Garten delivers healthy food and snacks
to offices around the country.
So, of course, you can imagine that when offices shut down
in most of the major cities in the United States,
Garten needed a way to adapt to this change.
Now that things are beginning to reopen
or we see changes in our society,
Garten needed a way to basically plan for this reopening
and try to understand what offices would be reopening,
what capacity would they be reopening at?
Would they have half of their employees coming back to work?
Would they only have a handful?
And basically, come up with programs
that are going to support these offices as we're going
through this influx of changes.
So Garten went ahead and they used the Looker block as well.
And just like the other companies I talked about,
they were able to look at this data alongside their own data
around what customers they have, how many employees
these customers have, where they are in the United States,
and basically come up with those programs
to fulfill their customers' needs.
So I just want to take time to say thank you
all for joining us here today and listening to how
our customers are using all this technology and data to make
these data-driven responses.
I really encourage you to go understand more
about BigQuery public datasets program, also, the Looker
Marketplace.
And you can find really useful information about this block
that we've created in this link that I've posted here.
One of our friends here a Looker has
decided to create a blog post that
goes into detail on why we decided
to create this block, what data we used for our block,
and also has a link to a publicly accessible dashboard
and Explore environment so that if you want
to be able to analyze the data yourself,
you can go to this website and create your own reports,
drill into data, get a feel for what you would be experiencing
with Looker.
So thank you.