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MATTEO GIARDINI: Welcome back to the "Technical Guide
for Startups," a video series where
we help startups to start, build, and grow
their businesses successfully and sustainably
on Google Cloud.
My name is Matteo, and I'm a customer engineer here
at Google Cloud.
I'm here with you today to introduce you
to Looker, how this technology works,
and the value it can bring to your organization.
After helping you with starting your journey to the Cloud
and optimizing your deployments in the Start and Build series,
today, we're kicking off the Growth Series,
where we will help you meet the needs of new customers
and expand to new markets.
We are right at the beginning of your journey
to grow your business on Google Cloud.
And today, we want to show you how
to become a data-driven company by uncovering
insights with Looker.
Here is what we will cover today.
First, we will look at a different products included
in the Looker family such as Looker, Looker
Studio, and Looker Studio Pro.
Then we will dive deeper into what Looker really
is, what traditional BI look like,
and how Looker solves the problems
that traditional BI systems cause.
Further, we will cover Looker as architecture
and how Looker actually works with a technical demo focused
on Looker's main features, namely LookML, Explores,
and Dashboards.
We will then conclude by showing you
how startup customers are successfully
utilizing Looker to uncover insights from their data.
Here is what Looker really is in a nutshell.
Looker is Google's unified, intelligent, and open
BI platform.
And here, I would like to highlight
a few of the features that make Looker special.
First of all, it's universal semantic layer,
which enables complex data to be simplified
for end users in curated catalogs
with predefined business metrics and built-in data
transformations.
Secondly, it's API connectivity, meaning that just about
anything that you can do with the Looker application,
you can also do through API.
For example, you can provision new user accounts, run queries,
or schedule reports.
Looker also has a powerful integration
with ML and AI tools such as BigQuery ML but also Jupyter
Notebooks if you would like to build your custom
models on your data.
Lastly, through self-service visualizations,
you can empower all your employees
to extract insights from your data
and improve your company-wide decision-making processes.
The Looker product family includes Looker, Looker Studio,
and Looker Studio Pro.
Looker Studio is a self-service visualization tool
where you can create dashboards directly
from your spreadsheets.
Here, no centralized data model is required.
And you can import data from a variety of sources
and start working with your data in a few minutes.
Additionally, you can collaborate
with your colleagues and share your dashboards
with whoever you would like.
Looker, on the other hand, is our multi-cloud data platform
that gives you governance of your data at scale.
It allows you to explore your data
on a universal semantic layer.
Looker Studio and Looker work hand in hand
to provide a complete, unified, and comprehensive platform
for self-service and centralized business intelligence
to help you work with your data.
Before diving into Looker, I would
like to quickly focus on Looker Studio Pro, the paid
version of Looker Studio, our self-serve business
intelligence reporting tool.
The solution will gain increasing importance
within the Looker ecosystem.
And on top of allowing you to visually analyze
data and design impactful reports,
it will give you an enhanced visibility
of your data across your organization
through integration with Dataplex.
It will give you an improved team collaboration experience
with team workspaces, providing better access
and content management functionalities.
And it will give you access to 24/7 support channels
and a 99.9 availability SLA.
And let us now dive deeper into how
Looker innovates the business intelligence landscape.
With traditional business intelligence tools,
in order to create a dashboard for a specific business
purpose, it is usually necessary to first find the right data
amongst the data available to the company.
As data sits in different sources,
data analysts need to extract the data
by using connectors or writing SQL queries.
Then the data is aggregated into siloed workbooks,
which represent different subsets of the overall data.
Finally, it is visualized into a variety of different dashboards
and reports that are then consumed by business users.
This causes two main issues.
Firstly, if new information has to be added to the report,
we need to go back to the data analyst every time.
As you might imagine, this process
can become a bottleneck as it is rather
time-consuming for the data analysts
to accommodate the request of such
a high number of stakeholders.
Secondly, in case we want to update or create a new metric,
data analysts need to go back into every report
or every workbook to update the metric, creating data chaos
and severe inconsistencies across dashboards.
Looker solves both problems.
With Looker, you can have the trustworthiness
that comes with everyone using standardized definitions
of your business metrics without losing
the ability for people to self serve to answer
their own questions with data.
Fundamentally, Looker will enable everyone
to make better decisions with data.
And this is only going to happen if everyone can ask questions
on the data and get reliable and correct answers
without bottlenecks or chaos.
Let's now take a look at the different components
and mechanisms that make Looker such a unique business
intelligence platform.
At its core, Looker's in-database architecture
allows customers to avoid having to extract data every time they
need to build a report.
Instead, data is queried directly
from the underlying database, avoiding
any unnecessary movement of data.
Secondly, Looker will speak SQL to your database.
But everything will first go through a code-based semantic
modeling layer called LookML.
This will effectively work as a single source of truth
that avoids any inconsistencies in your data
and allows you to define standard metrics
across your business and maintain them
from a single location.
Last but not least, Looker is cloud native
and fully web-based.
Everything is hosted and maintained by Looker.
Thanks to its API-first approach,
Looker allows users to push data to business applications
or automate workflows based on data.
For example, if your marketing campaign is not
performing as planned, and Looker detects
that your conversion rate is below certain thresholds,
you can configure it as such that your campaign is
stopped automatically.
Pretty cool, no?
Here's how Looker changes the BI scene.
Looker connects directly to your SQL database,
giving you a complete picture of your company data.
Looker provides end users with a business intelligence tool
and interactive dashboards which can be easily updated
by non-technical users.
It allows you to integrate your data to third-party tools using
dynamic alerting or exporting your customer
segment to your marketing tool.
Finally, you can build with Looker
by prioritizing your data and metrics
into third-party software.
Lu will now run you through a demo
to show you how to go from database to insights, focusing
on Looker's three main features--
LookML, Explores, and Dashboards.
LU LI: Hi, everyone.
This is Lu.
I'm a customer engineer from Google Cloud.
Today, I'm going to show you how to deploy Looker
in your organization by generating and customizing
a reusable data model, exploring the data through that model,
and creating a Looker dashboard.
The first step is to connect Looker to your database.
Looker work with real-time data.
We do not store or extract data in Looker.
The data stay in the database.
I'm going to develop and select Manage LookML Project.
And from here, I'm going to create a brand new project.
Let's call this ecommerce since it's an e-commerce data set.
A project is something that is going to stick with us.
My coworker and I are able to build business logic
into that, which allow us to collaborate on.
Once the project is created, the first step
is to create a View file.
Click Create View From Table.
I will need to select the database
connection and the tables that I will need.
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I'm going to use the database called ecomm
and select the table, inventory_items,
and order_items, and click Create Views.
Looker is now scanning through and generating all the columns
that exist in this table.
For example, we can see an ID column
in the other order_items table.
It also found the column called created and delivered
and automatically broke it into different time frames.
These are going to be very flexible for me to use.
A dimension is a column coming straight from the database,
like sales_price dimension.
And measures are defined in Looker
like this average_sale_price.
This is the unique location where the metrics are defined.
There are two reasons why we define metric like this.
Number one, this is how we enforce Looker's single source
of truth.
Everyone would be using the same definition of the metric.
Number two, this makes Looker very scalable.
To update a metric, you update here.
And it will be updated everywhere
in every dashboard, which is using the metric.
Before I can explore this data, I will firstly
need to create a model file.
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First, added the connection name to looker-private-demo.
Then specify the table that I want to explore.
In this case, it will be order_items.
Now, we are good to go.
Let's start with our first explore.
I want to know what the average sales price is.
So what I need to do is just to click the Average Sales Price
on the left and click Run.
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It looks great.
But I hope to format it to US dollar.
So I'm going back to order_items view
file and edit the average_sales_price.
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Let's now go back to Explore page and refresh the web page.
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With that, I get my average sales price in US dollar.
Now, it comes to the question.
What if I need to use the information from other tables?
We will firstly need to let Looker
know how these two table join together in the model file.
Here, I specify the view name, which is "Inventory Items."
It will join the order_items using the key item_id.
In the inventory table, we have the information
of product cost.
And in the order_items table, we have the information
about the sales_price.
Now, to get the gross margin and from there,
using the sum of gross margin to calculate
the total gross margin.
I can simply do a calculation like this.
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With that, I'm going to refresh the Explore page,
remove the current filters, and explore the total gross margin.
I'm going to click Created Date from the inventory table
and Total Gross Margin from the order_items table.
Click Run.
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From here, I want to filter the date to the past 30 days
and click Run again.
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With that, I got the total gross margin for the past 30 days.
MATTEO GIARDINI: As we saw, LookML
will support your scalability needs as your company grows.
It will serve as a single source of truth for all your company
data and metrics.
Additionally, any changes to business logic
are always tracked thanks to its integration to GitHub.
In the next part, Lu will show you
how nontechnical people will use a self-service layer
to explore data.
LU LI: In the first part, we get the result
of total gross margin for the past 30 days.
Let me explain what happened in this Explore page.
Anytime users select metric on the left,
Looker is writing SQL automatically
on behalf of the user.
When you press the Run button, Looker
is sending this query to the database, which
understand exactly what was requested
and returns the results.
This will free up a huge amount of time for your data analyst
who will not need to constantly write
SQL queries again and again.
At the same time, you are sure that the result
are consistent because all the metrics are
defined in one location.
Once you have created your visualization,
you can do three different things.
Number one, you can share it with a link.
As Looker is being fully web-based,
the user you share it with will have the same width
with the same filters.
Number two, you can save it as a new dashboard.
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And here you are.
I have just created a new dashboard on my own
without having to go to a technical person.
The third thing I can do is to scatter this report
and send the data to a third party too.
MATTEO GIARDINI: Through the Explore function,
business users can get answers to business questions
in seconds without any technical knowledge required.
Data analysts can save substantial amount of time
and focus on value-add activities.
Finally, your numbers and results
are consistent across the entire organization
as metrics are defined and accessed
from a single location.
LU LI: In part 3, we will cover a few examples
about the dashboard.
In part 2, we show you how to simply create
a dashboard to show total gross margin for the past 30 days.
Let's take a look at the dashboard page
and have a better understanding on what else
we can do as Looker has access to all the data
that sits in your database, which means
I can see every transaction.
And not only can I see this transactional data,
I can also take action on it.
By clicking Average Order Sales Price,
I'm able to see the raw data and send email
straight from Looker.
All the data is in real time.
Looker does not store any data.
So I can also set a lot if any metric exceeds
certain threshold such as if average sales price is
less than 25 and specify the filters
unless a configure to send their specified recipient's
notification to email or Slack when a list condition are met
or exceed.
MATTEO GIARDINI: Finally, we saw the power of Looker dashboards,
which allow users to browse data in real time,
define metrics once, and use conditional alerts to monitor
KPI and take action accordingly.
And finally, access underlying transactional data and really
understand the why behind the metrics they see.
Before concluding, we would like to show you
how one of our customers operating in the insurance
industry, Simply Business, transform the insurance
experience with Looker.
As Simply Business grew to over 700 employees
in the UK and USA, it faced a few challenges
as it did not have a centralized data platform.
It had information silos, which led to inconsistent reporting
and mistrust in data, which caused delay
in decision making.
By integrating Looker in its day-to-day operations.
Simply Business was able to get real-time access to data
and accelerate its reporting processes,
transform their customer experience by speeding up
their contact center analytics, and finally implement
a centralized analytics hub to manage your business metrics.
To conclude, today, we saw how Looker
can enable your organization to unlock data for every employee
by being your trusted data platform,
enabling you to make informed decisions
and help you deliver pleasant data experiences
to your organization.
In the description below, we have
compiled a few resources you can use to get started with Looker.
We really hope this video has been informative.
And do not wait anymore and start
exploring with Looker to find a true power in your data.
But first, do not forget to subscribe to our YouTube
channel and stay tuned for our next video
on our sustainability strategy to help you understand how
Google Cloud can support you and your company
on your sustainability journey.
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