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DAVID SABATER DINTER: Welcome to this Google Cloud Next talk.
My name is David, and I am product manager based
in London for data analytics.
And I'm here with Justyna.
JUSTYNA BAK: Hi, everyone.
My name is Justyna, and I lead product strategy
for Firebase, which is Google's application development
platform.
I am based in San Francisco.
In this talk, David and I will introduce the concept
of design patterns that can shorten
the time-to-value from data analytics
projects at your organization.
If you think that the value of data
is still locked across your company's various spreadsheets,
business applications, and databases, you are not alone.
Only one in four companies agree that they can effectively
turn their data into actionable insights that
drive business outcomes.
At Google, we created data analytics design patterns
to help flip the numbers in the chart.
But first, what is a design pattern?
Over to you, David.
DAVID SABATER DINTER: I'm actually
super excited to share that, working together
with our customers and partners, we're
publishing a set of cross-product technical assets
designed to accelerate customers' path to value
realization.
These are based on customer demand
and fully aligned with industry top use cases.
We call these, as Justyna mentioned, design patterns
or workload accelerators, with examples
such as anomaly detection, log analytics, churn prediction,
and predictive maintenance.
These solutions bring product capabilities
together with design methodology,
open-source deployable code, data models, and reference
architectures, all this to accelerate your business
outcomes.
Let's dive into some examples from these 30+ patterns we have
published so far, reviewing the challenges, the why; solutions,
the what; and the how to implement them,
showing high level architecture.
Justyna will now introduce these examples
before we dive into them.
Back to you, Justyna.
JUSTYNA BAK: Thank you, David.
With design patterns, you get access to more than 30
ready to deploy data analytic solutions.
Design patterns leverage the best
of Google and our rich partner ecosystem,
including technology partners and system integrators.
In this talk, we will cover three examples
on how a design pattern can be applied to a specific industry
to unlock the value of data.
We'll start with media and entertainment industry,
and talk about growing the user base for a mobile app
by better understanding user preferences
with App Analytics design pattern.
Then we'll switch to retail and talk
about maximizing digital shops' revenue with Price
Optimization.
Finally, we'll move over to financial services
and talk about preventing credit card fraud with Anomaly
Detection design pattern.
So let's jump into the first design pattern for today,
how to leverage your data to inform your mobile strategy.
And here I would like to start by sharing a story of how
National Public Radio, an American media company best
known as NPR, partnered with Google to deliver
a great listener experience for their mobile radio app.
NPR wanted to grow the user base for the radio app that
allows users to stream both real-time and on-demand
content.
The first step was for NPR to learn as much as possible
about their existing user base, but that information
wasn't easily accessible as it was
spread across several disparate data sources.
For example, NPR wanted to understand how users listen,
whether it's via headphones or speaker phones.
And then we wanted to understand what the users listen
to from these different devices, whether it
was live radio or perhaps a podcast.
To do this, NPR needed to combine event data
from the app front-end with the listening data
from their own back-end servers.
And that's where App Analytics design
pattern came in really helpful.
It utilizes Google Analytics for Firebase and BigQuery,
and it quickly provided a unified view of data
and painted a full picture of customer journey
that helped really inform the future app strategy.
As a result, NPR increased user engagement
and was able to customize content to listeners'
interests and behavior.
Now let's take a look under the hood of how the App Analytics
design pattern works.
First, if mobile apps are an important part of how
you interact with your customers,
you are probably already using a variety of products,
such as Google Analytics to log events,
Firebase Crashlytics to gather data about app errors,
and most likely you are also using a CRM, a ticketing
system, maybe you even have your own homegrown systems
or spreadsheets.
So the first step into unlocking value
from all these disparate data sources
is to centralize them into a single warehouse,
such as BigQuery.
With unified App Analytics design pattern,
it's really easy to plug all these different data
sources into BigQuery and start analyzing it
with a business intelligence tool such as Looker.
With Looker, you can create dashboards
and gain an executive overview of the business
to create reports, prioritize bugs,
or inform your go-to-market strategy.
For example, let's dive deep into how App Analytics design
pattern can help you prioritize bugs.
Typically, you want to resolve up crashes
for your most valuable users.
Via the Looker interface, you can easily
combine your Crashlytics data with your CRM data
to narrow down the crashes with highest revenue impact.
Then you can go ahead and automate your issue resolution
workflow by creating a role for any future crash that
involves your VIP customers.
As a result, any future crash that impacts those high value
customers will create and assign a high priority JIRA ticket.
With that, I would like to hand it over to David
to talk more about Price Optimization in retail.
DAVID SABATER DINTER: Thanks, Justyna.
So PDPAOLA is an online jewelry company
with hundreds of unique high quality products,
and was looking for an effective way
to uncover glaring insights about this business that
could inform their marketing and pricing strategy.
PDPAOLA's data scientists face a challenge
with many different and scattered data sources,
such as shipping cost and product information,
stored across various spreadsheets, applications,
and databases.
Actually, in a competitive and hectic global marketplace,
strategic pricing matters more than ever.
But often projects are consumed by the tedium of standardizing,
cleaning, and preparing data from transactions, inventory,
demand, among other sources.
So by working with Google Cloud and our partner Trifacta,
PDPAOLA was able to rapidly unify multiple data sources
and create a real-time and ML-powered analysis, leveraging
predictive models to estimate future sales,
ultimately helping to increase their sales.
The impressive increase in sales was possible in part thanks
to the recommendations surfaced by the product optimization
solution, consisting of three main components.
The first one, Dataprep by Trifacta,
integrated different data sources
into a single common data model, or CDM.
Quickly to mention that Dataprep is an intelligent data
service for visually exploring, cleaning, and preparing
structured and unstructured data for analysis, reporting,
and machine learning.
The second one, our serverless cloud data warehouse service,
BigQuery, created and stored pricing models
in a consistent and scalable way.
And finally, with Looker dashboards, our Google Cloud
enterprise-ready BI platform, business teams
surfaced insights and enabled them to take action.
Our last example for this presentation
is actually around finance, anomaly detection.
Let's see this interesting challenge for capital markets.
In this particular industry, it is
critical to optimize investment decisions by answering
the following question.
How do we maximize revenues from investors' money
while keeping risk below a certain threshold?
One way of solving that is through quantitative analysis,
in layman's terms, using maths on timestamped data points.
A critical part of that analysis involves
creating technical indicators for market data,
such as calculating the moving average of a particular stock
price over different time periods.
But can these quantitative, or what we call quant teams,
trust these technical indicators always?
Not really.
They typically need to either detect market opportunities
before anyone else to jump on the market or pull back
their trading exhibition engines if they cannot trust their own
technical indicator just to avoid undesired market risk
exposure.
Actually, for this particular pattern accelerator,
our solution was implemented in partnership with our system
integrator partner Kasna.
This pattern on modeling time series indicators
allows customers in real-time to do the following tasks.
First one, perform common time series functions,
like gap filling, leveraging our data flow service
for stream and batch processing at scale
and leveraging a common open-source framework.
Secondly, calculate various metrics,
like moving averages or Bollinger Bands.
A third one, integrate with the TensorFlow models on the AI
platform or Vertex AI for live inferences and detection.
And last but not least, produce real-time dashboards
in Grafana.
So with that, we've covered these three patterns.
And I will hand it back to Justyna
to close the presentation.
JUSTYNA BAK: There are more than 30 data analytics design
patterns ready for you to use.
So get started today and browse our catalog of design patterns
by using the link on the screen.
We have more than 200 more ideas in the pipeline,
so be sure to check in with us regularly,
as new patterns will be added soon.
Thank you for joining us today.
DAVID SABATER DINTER: Thank you.
Cheers.
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