Learn to-use-google-data-studio-jan22

Learn to Use Google Data Studio
Written by Himanshu Sharma, Founder of OptimizeSmart.com
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Copyright Optimize Smart 2022 Page 2

About the author
● Founder, OptimizeSmart.com
● Over 15 years of experience in digital
analytics and marketing
● Author of four best-selling books on digital
analytics and conversion optimization
● Nominated for Digital Analytics Association Awards for Excellence
● Runs one of the most popular blogs in the world on digital analytics
● Consultant to countless small and big businesses over the decade
Website: www.optimizesmart.com
Linkedin: https://www.linkedin.com/in/analyticsnerd
Facebook: https://www.facebook.com/optimizesmart

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Table of Contents
1. Introduction
2. Importance of data visualization in digital analytics
3. How to access Google Data Studio
4. Building blocks of Google Data Studio
5. Why you should work with Google Sheets
6. Why I use Google Sheets with Data Studio instead of Microsoft Excel
7. Introduction to data platforms
8. Introduction to connectors
9. Introduction to data sources
10. Introduction to data sets
11. Data source connection
12. Data source fields
13. Types of data source fields
14. Dimensions in Google Data Studio
15. Types of dimensions in Data Studio
16. Metrics in Google Data Studio
17. Types of metrics in Data Studio
18. Configuring the data source (data modeling)
19. How to create and configure data source
20. Introduction to data types
21. Creating reports in Google Data Studio
22. Google Data Studio template gallery
23. How to use a report template to create a new report
24. Components of a report in Google Data Studio

Introduction
I am very excited to teach you how to use Data Studio to visualize and analyze
data.
Data Studio is a powerful tool to visualize data.
It is a cloud-based tool which means you can access it from any device/browser
as long as you have access to a stable internet connection.
Google Data Studio is built on Google Drive. This means you will need a Google
Drive account before you can access it.
Google Data Studio is available in 37 languages and supports number, date,
and time formats in those languages.
In this ebook, I am going to teach you how to use Data Studio to create reports
and interactive dashboards.
We will start with Google Sheets and use its data to create reports and
interactive dashboards.
But before we move forward, let’s go back one step and understand the
importance of data visualization in digital analytics.
Importance of data visualization in digital
analytics
Data visualization is the presentation of data in graphical format.

Data visualization helps in data interpretation and data retention.
It helps to tell meaningful, emotional and engaging stories to key
decision-makers.
If you wish to make your data reporting more meaningful and persuasive then
you need to learn the art of storytelling by visualizing your data.
When you have got a lot of data to analyze (especially big data) you
cannot spend days or weeks analyzing thousands or millions of
rows of data in Excel spreadsheets to find hidden trends and
insight.
You need a tool that allows you to quickly make sense of data and determine
patterns and anomalies which are otherwise extremely hard to detect in a
timely manner.
This is where data visualization tools like Google Data Studio come in handy.
Through this tool, you can greatly speed up your data analysis.
How to access Google Data Studio
If you have never used Google Data Studio before then the best way to access
it is by searching for the keyword ‘google data studio’ on Google.com and then
click on the first search engine listing:

Alternatively, you can access Google Data Studio by clicking on this link:
https://datastudio.google.com/overview
Then click on the ‘Use it for free’ button:

Now log in using your Google email address and password.
If you do not have a Google account then click on the 'create account' link:

If you have never used Google Data Studio before then this is what the home
page of Google Data Studio would look like:

Building blocks of Google Data Studio
Following are the building blocks of Google Data Studio:
● Google Sheets
● Data platforms
● Connectors
● Data sources
● Data sets
● Data source fields
● Dimensions
● Metrics
● Data types
● Reports

● Components (charts)
In order to understand how Google Data Studio works and how to best use it,
you must understand these eleven building blocks.
Why you should work with Google Sheets
A rookie mistake that most Data Studio users make is that they pull
data directly from a data platform into Data Studio and then try to
manipulate it there.
Data studio is not meant for data manipulation. It is not a spreadsheet.
When you manipulate data in Data Studio, it slows down your report. This is
especially true for large data sets.

Manipulating data in a spreadsheet is a lot easier than manipulating data in
Data Studio.
So when you choose to manipulate data in Data Studio, you make it
unnecessarily hard to use.
That's why we first pull the data from a data platform into a spreadsheet (like
Google Sheets or Excel).
Then manipulate the data there, and only after that do we use that data in
Data Studio.
That's why it is very important that you develop a deep understanding of
Google Sheets so that you can work with ease in Google Data Studio.
Why I use Google Sheets with Data Studio
instead of Microsoft Excel
While Google Sheets is not as robust as Microsoft Excel when it comes to
functionality (tools, functions, formulas, etc), there are a couple of reasons I
prefer to use it over Excel when working with Data Studio:
#1 Google Sheets is a Google Product
Being a Google product, Google Sheets natively integrates/works well with
other Google products like Google Analytics, Google Ads, Google Forms, and of
course Google Data Studio.

For example, you can directly pull data from Google Analytics into Google
Sheets via an add-on. This is really one of the biggest advantages of using
Google Sheets over Excel.
#2 Works well for Google Drive users
If, like me, you are a Google Drive user, you more than likely prefer to use
Google Sheets.
#3 You can access Google Sheets from any device/browser.
Unless you are using a cloud-based version, Excel files are stored locally. What
that means is all of your work is tied to one computer/device.
You just can’t access your work from different devices (mobile, tablets, office
PC, etc). This is not the case with Google Sheets as it is cloud-based.
As long as you have access to the internet, you can access Google Sheets from
any device/browser.
#4 For project collaboration, Google Sheets is a better solution.
Since Excel files are stored locally (unless you are using a cloud-based version),
every time you make a change to the spreadsheet, you need to send the latest
version to others.

Even if you use the cloud-based version of Excel, it still doesn’t quite beat the
seamless sharing and real-time collaboration that come with Google Sheets.
So for project collaboration, Google Sheets is a better solution.
#5 Google Sheets is a good enough alternative to Excel
Google Sheets cover most of the basic Excel spreadsheet features and is
constantly adding new functions and formulas.
Unless you need to do complex data manipulation, you are unlikely to miss
working with Excel. Also, you don’t need to save any of your work as it is
automatically saved in Google Sheets.
#6 You can still work with Excel
Even while using Google sheets, you can still work with Excel. So using Google
sheets doesn’t mean that you can no longer use Excel.
You can open and edit Excel files in Google Sheets. You can convert Excel files
to Google Sheets and vice versa.
#7 Google Sheets is free to use
Google Sheets is free to use whereas Microsoft Excel is not.
While this is not a good enough reason for me, it might be for others.

Introduction to data platforms
Before you can use Data Studio, before you can create reports to visualize data,
you would need access to one or more data platforms.
Following are examples of data platforms:
● Google Sheets
● Google Analytics
● Excel Spreadsheet
● Big Query
● Google Ads
● Google Search Console
● YouTube Analytics
● Facebook Ads
● Adobe Analytics etc
In order to pull data from these data platforms into Data Studio, you would
need to use a connector(s).
Introduction to connectors
A connector is a mechanism by which you can connect a specific data platform
to a data source created in Google Data Studio. Any data platform accessible
via the internet can be connected with Data Studio.
There are two broad categories of connectors:

#1 Ready-made connectors
#2 Custom made connectors
#1 Ready-made connectors
These connectors are ready to use. They are either free to use or require
monthly/annual paid subscription.
Google connectors, partner connectors, and open-source connectors are
examples of ready-made connectors.
#2 Custom made connectors
These connectors are developed on demand and are used when ready-made
connectors can’t be used to pull data from a specific data platform.
You can create your own connector by using Google Apps Script.
If you are not a developer, you can hire someone to create a custom made
connector for you.
Introduction to data sources
A data source is a Data Studio file which is used to define how a connector
should provide data from a specific data set to the report(s) in Data Studio.

You create a data source for a specific data set.
So if you want to display data from multiple data sets in your Data Studio
report then you would need to create a separate data source for each data set
and then add each data source to your report.
Introduction to data sets
A data set is a place where your collected data actually resides.
Every data platform is made up of one or more data sets. For example, the
Google Analytics platform is made up of one or more reporting views.
So a specific Google Analytics reporting view is an example of a data set.
Similarly, the Google Sheets platform is made up of one or more spreadsheets.
So a specific Google spreadsheet is an example of a data set.
Data source connection
A data source connection is the connection between your data source and data
set.
Editing a data source connection means changing how a data source connect to
your data set.

When you create or edit a data source connection, you are asked to select the
data set that you want to use:
Data source fields
A data source is made up of a set of fields called data source fields:
Connectors provide fields from a data set to a data source.

Some connectors (like the Google Sheets connector) provide all the fields from
a data set to a data source.
While other connectors (like the Google Analytics connector, Google Ads
connector, etc) provide only a subset of the fields from the data set to the data
source.
Only the fields provided by the connector are the ones available to use in your
reports.
Types of data source fields
There are two broad categories of data source fields:
#1 Regular field - a data source field that doesn’t perform some action(s) on
other field(s) in your data source.
#2 Calculated field - a field that performs some action(s) on other field(s) in
your data source or chart via a formula.
Regular fields can be further categorized into:
#1 Regular dimension - It is the attribute of visitors to your website. It is used
to describe or categorize your data.
#2 Regular metric - It is a number which is used to measure one of the
characteristics of a dimension.

Calculated fields can be further categorized into:
#1 Data Source specific calculated field - the calculated field created in a data
source. When you create a calculated field in a data source, the calculated field
is available in any report that uses that data source. However, you can't use a
data source specific calculated field with blended data.
#2 Chart specific (or chart level) calculated field - the calculated field created
in a specific chart in a report. When you create a calculated field in a chart, the
calculated field is available only in the chart in which you create it. However,
you can use a chart specific calculated field with blended data.
Data source specific calculated field can be further categorized into:
#1 Data source specific calculated dimension - a dimension that performs
some action(s) on other field(s) in your data source via a formula.
#2 Data source specific calculated metric - a metric that performs some
action(s) on other field(s) in your data source via a formula.
Chart specific calculated field can be further categorized into:
#1 Chart specific calculated dimension - a dimension that performs some
action(s) on other field(s) in your chart via a formula.

#2 Chart specific calculated metric - a metric that performs some action(s) on
other field(s) in your chart via a formula.
Dimensions in Google Data Studio
A dimension is the attribute of visitors to your website. It is used to describe or
categorize your data.
For example, let’s say a man aged between 25-34 from London visited your
website after clicking on an organic search listing on Google which he found by
searching for the keyword ‘attribution modeling’.
Let us also assume that he visited your website via a chrome browser which is
installed on a desktop computer that runs windows.
Now following are the attributes of the visitor to your website along with their
values:
Gender – male
Age – 25-34
City – London
Source / Medium – Google / Organic
Keyword – Attribution Modeling
Browser – Chrome

Device Category – desktop
Operating System – Windows
Here Gender, Age, City, Source /Medium, Keyword, Browser, Device Category
and Operating System are all examples of dimensions because they are the
characteristics of your website users.
Dimensions in your data source appear as green fields.
Types of dimensions in Data Studio
There are three types of dimensions in Data Studio:

#1 Regular dimension - the attribute of visitors to your website. It is used to
describe or categorize your data.
#2 Data source specific calculated dimension - a dimension that performs
some action(s) on other field(s) in your data source via a formula.
#3 Chart specific calculated dimension - a dimension that performs some
action(s) on other field(s) in your chart via a formula.
All calculated dimensions appear in the data source with an ‘fx’ symbol:
Metrics in Google Data Studio
A metric is a number that is used to measure one of the characteristics of a
dimension.
A dimension can have one or more characteristics.

For example, the following are the characteristics of the dimension called
‘Source / Medium’:
● Sessions
● % New Sessions
● New Users
● Bounce Rate
● Pages / Sessions
● Avg. Session Duration
● Goal Conversion Rate
● Goal Completions
● Goal Value
Sessions, % New Sessions, New Users, Bounce Rate, Pages / Sessions, etc are all
examples of metrics because they are the characteristics of the dimension
called ‘Source / Medium’

Metrics in your data source appear as blue fields:
Types of metrics in Data Studio
There are three types of metrics in Data Studio:
#1 Regular metric - It is a number that is used to measure one of the
characteristics of a dimension.
#2 Data source specific calculated metric - It is a metric that performs some
action(s) on other field(s) in your data source via a formula.

#3 Chart specific calculated metric - It is a metric that performs some action(s)
on other field(s) in your chart via a formula.
All calculated metrics appear in the data source with an ‘fx’ symbol:
Configuring the data source (data modeling)
Data modeling consist of customizing your data source fields (dimensions and
metrics) so that your reports visualize the data in your desired format.
You configure a data source by:
● Renaming data source fields
● Changing data types (what type of data (like text, numeric) the field
contains)
● Changing aggregations (how numeric fields should be summarized)
● Adding a new field

● Adding a description (description of a field)
The data source fields panel is where you configure the data source. Here is
how this panel looks like:
You see this panel when you create or edit a data source.
How to create and configure data source
In order to create and configure your data source, follow the steps below:
Step-1: Navigate to the home page of your Data Studio account. The home
page is where you create and access all your Data Studio files.
Here is what the home page looks like:

You can navigate to the home page of your Google Data Studio account by
clicking on the Data Studio logo on the top left-hand side:
Step-2: Click on the ‘+ Create’ drop-down menu:

Step-3: Click on the ‘Data Source’ option:

Step-4: Select the connector you want to use. Let’s select the Google Analytics
connector:

Step-5: Create a data source connection by selecting the data set you want to
connect with your data source:
Step-6: Click on the ‘Connect’ button to connect your data source to the
specified data set:

Once you click on the ‘Connect’ button, you will see the data source editor
through which you can do data modeling:

Step-7: Configure your data source by:
● Renaming data source fields
● Changing data types (what type of data (like text, numeric) the field
contains)
● Changing aggregations (how numeric fields should be summarized)
● Adding a new field
● Adding a description (description of a field)
Congratulations. You have now created and configured your new data source.
Step-8 (optional): Click on the ‘Create Report’ button to create a new report
from your newly created data source or navigate back to the Data Studio home
page:

Introduction to data types
The data type of a data source field determines the kind of data to expect (in
the specified data set) when processing the field.
For example, when the data type of a field is ‘Number’, its tells Data Studio to
expect a number when processing the field:

When the data type of a field is ‘Currency (US - Dollars) ’, it tells Data Studio to
expect currency data in US dollars when processing the field:
Similarly, when the data type of a field is ‘Text’, its tells Data Studio to expect
text data when processing the field:
Data type determines which operations are allowed and not allowed on a data
source field.
For example, you can't apply an arithmetic function to a ‘Text’ field, or use a
‘Number’ field as the date range dimension in a report.
If you want to change the data type of a field then just click on the drop-down
menu next to a data type.

Note: Changing the field type can have a considerable impact on how you see
your data in reports.
Google Data Studio supports the following data types:
● Numeric
● Text
● Date and Time
● Boolean
● Geo
● Currency
● URL
Creating reports in Google Data Studio
In Google Data Studio there are three ways you can create a new report:
#1 Via the ‘Create’ drop-down menu:
When you logged into Google Data Studio, you see the ‘Create’ drop-down
menu on the top left-hand side:

Click on the ‘Create’ drop-down menu button and then click on the ‘Report’
link to create a new report from scratch (also known as the blank report):

#2 The second way to create a new report from scratch is by clicking on the
‘Blank Report’ button:
#3 The third way to create a new report is by using an existing template and
then modifying it so that it meets your unique requirements:

This is a recommended method to create a new report because it doesn’t
take a lot of your time.
If you create a new report from scratch then you would need to spend a lot of
time in creating the layout and format of your report.
You would need to spend a lot of time in creating and bringing individual report
components (like tables, scorecards, charts etc) together in a way that is
visually appealing and at the same time make your report meaningful and easy
to understand.
However if you use a report template, then you just need to do two things
(most of the time):
#1 Change the data source
#2 Do some minor cosmetic changes to the report (like: change the name of
the report, add your company logo etc).

Google Data Studio template gallery
The template gallery list ready to use report templates.
You can find the Google Data Studio template gallery at two places:
#1 When you logged into Google Data Studio, you see the template gallery
link on the right-hand side:
When you click on this link, you can see the list of available report templates:

#2 The second place to look for report templates is the Data Studio Report
Gallery: https://datastudio.google.com/gallery
The Data Studio Report Gallery lists report templates under the following
three categories:
#1 Featured (these are the reports templates featured by Data Studio).
#2 Marketing Templates
#3 Community (Data Studio report templates built by the community)

If you want to submit your own report to Data Studio report gallery then click
on the ‘submit your report’ link and then follow the on-screen instructions:

How to use a report template to create a new
report
Follow the steps below:
Step-1: Navigate to Google Data Studio:
https://datastudio.google.com/navigation/reporting
You should now see the available report templates on the right-hand side:
Step-2: Click on one of the report templates say ‘Acme Marketing’:

Step-3: Check whether the template meet your requirements and if it does
then click on the ‘Use Template’ button on the top right-hand side:
Step-4: Select your data source from the ‘New Data Source’ drop-down menu:

Step-5: Click on the ‘Copy Report’ button.
Your new report would look like the one below:
At this point you can do following things with this report:
● Change the name of the report.
● Add your company logo.
● Add/edit/remove individual components of the report.
● Change the layout/formatting to meet your requirements
Needless to say, you need to know Google Data Studio really well before you
can use an existing report template as your report. I will teach you how to do
all that, later in this course.

If you want to use a template from the template gallery
https://datastudio.google.com/gallery then follow the steps below:
Step-1: Navigate to https://datastudio.google.com/gallery
Step-2: Select the template you want to use. Let's say we want to use the ‘GA
Acquisition Overview’ template (listed under the marketing templates gallery):
Step-3: Click on the ‘GA Acquisition Overview’ template. This action will open
the report template in a new tab.
Step-4: Click on the ‘Make a copy of this report’ button:

Step-5: Select your data source from the ‘New Data Source’ drop-down menu:

Step-6: Click on the ‘Copy Report’ button. Your new report would look like the
one below:
At this point you can do following things with this report:
● Change the name of the report.
● Add your company logo.
● Add/edit/remove individual components of the report.
● Change the layout/formatting to meet your requirements
Components of a report in Google Data Studio
A report in Data Studio is made up of one or more pages. By default, a new
report is made up of just one page.

If you want to add another page to your report, then click on the ‘Add a page’
button on the top left-hand side:
Each page is made up of canvas. Here is how the blank canvas looks like:

A canvas is made up of the grid lines.
When we create a report in Data Studio, we add components (like tables,
scorecards, bar charts, etc) to this canvas.
Following is an example of a canvas which contains one table component:
Following is an example of a canvas which contains two components: one
table component and one scorecard:

Each component is made up of one or more data source fields.
You can change the layout and theme of this canvas through the 'Layout and
Theme panel' on the right-hand side:

You are doing Google Analytics all
wrong. Here is why...
I have dealt with hundreds of Google Analytics accounts in my career.
I have seen many issues, from incorrect tracking code, selecting the wrong KPIs,
to analyzing data without using custom reports or advanced segments.
But do you know the biggest issue of all in Google Analytics?....
It is the “misinterpretation of analytics data”.
Many marketers make the mistake of crediting conversions to the wrong
marketing channel.
And they seem to be making this mistake over and over again.
They give the credit for conversions to the last touchpoint (campaign, ad,
search term...).
They can’t help themselves because they believe that the Google Analytics
reports are ‘what you see is what you get’.
But they are actually ‘what you interpret is what you get’.

This has resulted in marketers making wrong business decisions and losing
money.
All the data you see in Google Analytics reports today lies to you unless you
know exactly how to interpret it correctly.
For example, let's talk about direct traffic.
All untagged or incorrectly tagged marketing campaigns, from display ads to
emails, could be reported as direct traffic by Google.
Whenever a referrer is not passed, the traffic is reported as direct traffic by
Google.
Mobile applications don’t send a referrer. Word/PDF documents don’t send a
referrer.
‘302 redirects’ sometimes cause the referrer to be dropped. Sometimes
browsers don’t pass the referrer.
During an HTTP to HTTPS redirect (or vice versa), the referrer is not passed
because of security reasons.
All such traffic is reported as direct traffic by Google.

So on the surface, it may look like most people are visiting your website
directly, but this is not usually the case.
But this analysis does not end here because you are still not looking at the
complete picture.
People do not always access your website directly and then make a purchase
straight away.
They are generally exposed to multiple marketing channels (display ads, social
media, paid search, organic search, referral websites, email, etc.) before they
access your website directly.
Before they make a purchase.
So if you are unaware of the role played by prior marketing channels, you will
credit conversions to the wrong marketing channels.
Like in the present case, to direct traffic.
To get this type of understanding, you need to understand and implement web
analytics.
But you learn data analysis and data interpretation from web analytics and
not from Google Analytics.

The direction in which your analysis will move will determine the direction in
which your marketing campaigns will move.
You get that direction from ‘web analytics’ and not from ‘Google Analytics’.
Web/digital analytics is not about Google Analytics (GA) or Google Tag
Manager (GTM). It is about analyzing and interpreting data, setting up goals,
strategies and KPIs.
It’s about creating a strategic roadmap for your business.
That's why the knowledge of web/digital analytics is so important.
So what I have done is, I have put together a completely free training for you.
This training will teach you what digital analytics really is and how I have been
able to leverage it to generate floods of news sales and customers. And how
you can copy what I have done to get similar results.
You can sign up for the free training here:
Reserve My Seat Now
I hope you find it helpful.
All the best,
Himanshu

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