Welcome to my Google Data Studio Tutorial. I am very excited to teach you how to use Google Data Studio to visualize and analyze data.
What is Google Data Studio?
Google Data Studio (or GDS) is a tool used 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. Data Studio is a completely free visualization tool that allows you to build great dashboards and reports.
Note: Google Data Studio is built on Google Drive. That means you would need a Google Drive account before you can access it.
Why use Google Data Studio?
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….. can be extremely hard to detect in a timely manner. This is where data visualization tools like Data Studio comes in handy. Through this tool, you can greatly speed up your data analysis.
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. Data Studio can make your data reporting much more meaningful and persuasive.
What are the benefits of using Google Data Studio?
Google Data Studio is a great visualization tool since it is very versatile, easy to create, share and collaborate with zero cost.
Following are some of the main benefits of using Google Data Studio as a visualization tool.
Multiple data sources:
You can connect Google Data Studio to multiple data sources and collect and combine data in one single report. This way you can measure your marketing activities across platforms and channels and generate cross-platform and multi-channel insights.
Unlimited customization:
You can fully customize the Data Studio reports. You can use a blank canvas to design your own report from scratch or you can use a Google Data Studio template. This makes Google Data Studio a very convenient and hassle-free way to design reports and dashboards for various data platforms like Google Analytics, Google Ads, YouTube ads, etc.
Dynamic and real-time reporting:
Google Data Studio reports are very dynamic in nature. That means you can apply any sort of filter on a data source and narrow down the data based on date range, time, users, device category, country, etc. Also, these reports can be made in real-time by pulling the data in real-time as it is available in the corresponding data source.
Report sharing and collaboration:
Google Data Studio reports can be shared in multiple formats by scheduling email delivery, by creating a link for a report or downloading the report as a PDF. You can also add your peers to the report for editing and reading purpose which makes collaboration painless across the team.
Zero cost:
One of the big advantages of using Google Data Studio is that it is completely free to use. Also, there is no limit to the number of users per data studio account which makes it more meaningful in a large organization.
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:
Why you should pull data into Data Studio via a spreadsheet
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. But 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.
When you choose to manipulate data in Data Studio, you make it unnecessarily hard to use.
That is why we first pull the data from a data platform into a spreadsheet (like Google Sheets or Excel) and manipulate the data there, and only after that use that data in Data Studio.
How to visualize data in Google Sheets via Google Data Studio
Step-1: Prepare your Google Sheet Data for Data Studio.
Before you upload data from Google Sheets to Google data studio, you need to make sure two things:
You pulled data correctly into Google Sheets.
The data you pulled into Google Sheets is in the correct format.
Because this is going to affect your data visualization and data analysis in Data Studio.
If you feed garbage to data studio then you are going to get garbage.
Garbage in, Garbage out.
Following is an example of incorrectly formatted data:
The data is not formatted correctly because it has got empty rows, empty columns, totals.
The column headers are missing. The data is not using correct data types.
So for example, the field ‘order date’ is not of type ‘date’. The ‘Revenue’, ‘Tax’ and ‘Shipping’ fields are not of type currency.
Following is an example of correctly formatted data:
By correct format, I mean:
There are no empty rows or columns.
Each column has headers and these headers are meaningful, unique and self-explanatory.
There should not be subtotals or grand-totals in your data source.
You also need to make sure that you are using the correct data types. So for example, dates are of type ‘date’, texts are of type ‘text’, numbers are of type ‘number’, the currency is of types ‘currency’.
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 through which you can pull data from a specific data platform. Any data platform accessible via the internet can be connected with Data Studio.
There are two broad categories of connectors:
Ready-made connectors
Custom made connectors
#1 Ready-made connectors
These connectors are ready to use. They are either free to use or require a 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.
Google Data Studio Connector Gallery
The Google Data Studio Connector Gallery lists all the connectors supported by Google Data Studio. Here you can see a list of all the currently available connectors. If you build your own connector, you can choose to publish and promote it in the connector gallery.
Google connectors are the connectors built and maintained by Google.
At present Google provides 18 different types of connectors in Data Studio:
All of the Google connectors are free to use as long as you have access to the right Google product. For example, if you do not have access to GA 360 then the Display and Video 360 connector is of no use to you:
Following are examples of Google database platform connectors:
1. Google BigQuery – connect Data Studio to BigQuery tables.
2. Google Cloud Spanner – connect Data Studio to Cloud Spanner databases.
3. Google Cloud SQL for MySQL – connect Data Studio to Google Cloud SQL databases.
4. MySQL – connect Data Studio to MySQL databases.
5. PostgreSQL – connect Data Studio to PostgreSQL based databases.
File upload connector
Use the file uploader connector to upload data from any data source (via a CSV file) to Data Studio that is not supported by a specific connector. Use this connector if you want to connect your data studio account with Microsoft Excel spreadsheets.
If you prefer using Excel with Data Studio then you will find yourself using this connector a lot. I prefer to use Google sheets because it natively integrates with Data Studio and other Google products like Google Analytics and Google Ads.
Extract data connector
Extract data connector is used to pull only a subset of data from an existing data source (i.e. data source which is already available in your data studio account).
The subset of data from an existing data source is called the ‘Extracted Data Source‘.
Thus by using the Extract data connector, you can create an extracted data source in Google Data Studio.
The advantage of using the Extract data connector is that when you extract only a subset of data (instead of all of the data) from a data source (to create reports and explorations), it can make your data studio reports and explorations load faster.
Not only that but your reports and explorations also become more responsive when applying filters and date ranges.
Note(1): Extracted data sources can contain up to 100MB of data.
Note(2): By default, the extracted data sources contain static data. If you want the extracted data source to automatically update then you would need to turn on the ‘Auto Update’ feature while creating the data source:
Introduction to partner connectors
Partner connectors are connectors built and maintained by third parties like Supermetrics, Funnel, etc.
Note: Partner connectors are usually not free to use and would require a monthly/annual paid subscription.
Why do you need partner connectors?
Google does not provide any connector for non-Google products. If you want to pull data from a non-Google data platform (like Facebook Ads, Adobe Analytics, Bing Ads, etc) then you need to use/purchase a partner connector.
Partner connectors are required when you want to consolidate data from multiple data platforms and visualize them in Data Studio. Through partner connectors, you can unite data from multiple marketing platforms into Data Studio.
For example, you can report and compare Google Ads, Facebook, Instagram, Twitter, LinkedIn and Bing Ads campaigns in a single Data Studio report.
Partner connectors make cross-platform reporting possible in Data Studio.
Supermetrics partner connectors
Supermetrics is a great tool that you can use to get partner connectors. They provide connectors for the following data platforms:
Facebook Ads
Hubspot
Microsoft Advertising
Twitter Ads
Instagram Insights
LinkedIn Ads
YouTube
Adobe Analytics
MailChimp, etc.
Supermetrics for Data Studio is a Google Sheet add-on that lets you use several connectors for pulling data from multiple non-Google data platforms.
I use this add-on to pull data from Google Analytics into Google Sheets. Its free version is also available and is good enough, as long as you are using only Google Analytics as a data source.
Difference between the Supermetrics for Google Sheets and Google Analytics Spreadsheet add-ons
Both are Google Sheet add-ons and both are free to use. Both are used to pull Google Analytics data into a Google Sheet. The main difference is in the ease of use.
You can create your own community connector by using the Apps Script. If you are not a developer then you can hire one to create a custom made connector for you. Any data platform accessible via the internet can be connected with Data Studio.
Introduction to data sources
A data source is a Data Studio file that is used to define how a connector should pull data from a specific data set and then send it to the report(s) in Data Studio.
When you log in to GDS (Google Data Studio), Google gives you the option to create a new data source or edit an existing data source:
But this name ‘data source’ (in the context of GDS) is a misnomer. You are not creating a data source in GDS. What you are actually creating is data source schema which defines how a connector should pull data from a specific data set and then send it to one or more reports.
The data set that you use is your actual data source. You create a data source schema for a specific data set.
So if you want to display data from a data set in your Data Studio report then you would need to create a data source schema for that data set and then add the data source schema to your report.
Introduction to data source schema fields
A data source schema is made up of a set of fields called data source schema fields:
A connector pulls these fields from the data source.
Only the fields provided by the connector are the ones available to use in your schema and reports.
A dimension is the attribute of your website visitors. It is used to describe or categorize your data. Dimensions in your data source schema appear as green fields:
Introduction to metrics in Google Data Studio
A metric is a number that is used to measure one of the characteristics of a dimension. Metrics in your data source schema appear as blue fields:
Note: In Google Analytics, a dimension cannot be used as a metric and vice versa. Whereas, in Google Data Studio, a dimension can be used as a metric and vice versa.
A parameter is a data source schema field whose value is supplied by a report user. It is used in calculated fields and report components just like a dimension or a metric.
Through parameters, you can pass user-supplied data to calculated fields and connectors. So if you want your report to display results based on user input then you use the parameter field.
While editing a data source schema, you get the option to add one or more parameter fields:
The data type of a data source schema field determines the kind of data to expect (in the connected data set) when processing the field.
For example, when the data type of a field is ‘Number’, it 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:
The data type determines which operations are allowed and not allowed on a data source schema 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.
Google Data Studio supports the following data types:
Reports in data studio are used to tell stories with data. Every report you create should have some purpose. The purpose can be to provide actionable insight, give recommendations or persuasion.
Following are some best practices for creating a report in Data Studio:
Understand who your report is meant for
Keep it short and simple
Use a report template whenever you can to create a new report.
Avoid pulling data directly from a data platform into your reports.
Avoid charting data for the current day in your reports.
Avoid using functions and calculated fields in your reports.
A report in Google Data Studio is made up of one or more chart components like:
Table
Scorecard
Time Series
Bar Chart
Pie Chart
Google Maps
Geo Chart
Line Chart
Area Chart
Scatter Chart
Pivot Table
Bullet Chart
Treemap
You can access these chart components by clicking on ‘Add a Chart’ drop-down menu in the report editor:
Let’s build a chart to show website performance.
Click on the ‘Add a Chart’ drop-down menu and then click on ‘Score Card’:
On the right-hand side of your report editor, you should see the panel where you can select the metric for the scorecard.
Let’s select ‘Sessions’ metric:
Note: You can also add filters or segments to the selected metric.
Let’s repeat this step to add a few more metrics to our report.
Once you are done, our report may look like the one below:
Now let’s add a trend chart for ‘Visits in Last Week’.
Click on ‘Add to Chart’ drop-down menu and then select ‘Time Series’:
Under the ‘Data’ tab on the right-hand side panel, select ‘Date’ as a dimension and ‘Sessions’ as a metric:
Now our chart on the canvas may look like the one below:
Since Google Data Studio supports dynamic reporting, you can also add controls to your report, to give your report users more freedom to slice and dice data by selecting a date range, device type, segment etc.
Frequently Asked Questions about Google Data Studio Tutorial
What is Google Data Studio?
Google Data Studio is a completely free data visualization tool which allows you to build great dashboards and reports. 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.
Why use Google Data Studio?
Google Data Studio can greatly speed up your data analysis. It allows you to quickly make sense of data and determine patterns and anomalies which otherwise can be extremely hard to detect in a timely manner. Data visualization helps in data interpretation and data retention. It helps to tell meaningful, emotional and engaging stories to key decision-makers.
What are the benefits of using Google Data Studio?
Google Data Studio is a great visualization tool since it is very versatile, easy to create, share and collaborate with zero cost.
– You can connect Google Data Studio to multiple data sources and collect and combine data in one single report.
– You can fully customize the Data Studio reports. You can use a blank canvas to design your own report from scratch or you can use a Google Data Studio template. This make
– Google Data Studio is a very convenient and hassle-free way to design reports and dashboards for various data platforms like Google Analytics, Google Ads, YouTube ads, etc.
-Google Data Studio reports are very dynamic in nature. That means you can apply any sort filter on a data source and narrow down the data based on date range, time, users, device category, country, etc. Also, these reports can be made real-time by pulling the data in real-time as it is available in the corresponding data source.
Why should I pull data into Data Studio via a spreadsheet?
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. But 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.
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