Google Analytics (GA4) plugin

The Google Analytics plugin allows you to visualize your website data.

Click the following link for additional content such as blogs, videos, use cases, and more:

Google Analytics

Adding a data source

To add a data source, open the relevant workspace and then click on the + next to Data Sources on the left-hand menu in SquaredUp. Search for the data source and click on it to open the Add data source page. Adding from the data sources page

Before you start

1. Enable the Google Analytics APIs
2. Configure the GCP Service Account
3. Configure the data source

Enable the Google Analytics APIs

In the GCP console select your project and ensure that the following are enabled:

• Google Analytics Data API
• Google Analytics Admin API

Configure the GCP Service Account

1. Create a new Service Account, or edit an existing account.
   See GCP - Creating and managing service accounts.
2. Ensure the account has the role Viewer by adding the role Basic > Viewer.
   See GCP - Grant a single role.
3. Create a new key for the Service Account using the key type JSON. Download the JSON file, as you will need to copy information from this JSON file when adding the data source.
   See GCP - Creating service account keys
   Important

   Make sure to store the key file securely, because it can be used to authenticate as your service account.

Configuring the data source

1. Open the JSON file that was downloaded when creating the key.
2. Display name:
   Give the data source a name.
3. Client email:
   Copy and paste the client_email from the JSON file into the data source form.
4. Private key:
   Copy and paste the private_key from the JSON file into the data source form (everything between the quotes).

5. Restrict access to this data source:
   Optionally, enable this toggle if you only want certain users/groups to have access to the data source, or those with the permission to link it to new workspaces. See data source access control for more information.

   Restrict access to this data source

   Note

   The term data source here really means data source instance. For example, a user may configure two instances of the AWS data source, one for their development environment and one for production. In that case, each data source instance has its own access control settings.

   By default, Restrict access to this data source is set to off. The data source can be viewed, edited and administered by anyone. If you would like to control who has access to this data source, switch Restrict access to this data source to on.

   Use the Restrict access to this data source dropdown to control who has access to the workspace:

   • By default, the user setting the permissions for the data source will be given Full Control and the Everyone group will be given Link to workspace permissions.
   • Tailor access to the data source, as required, by selecting individual users or user groups from the dropdown and giving them Link to workspace or Full Control permissions.
   • If the user is not available from the dropdown, you are able to invite them to the data source by typing in their email address and then clicking Add. The new user will then receive an email inviting them to create an account on SquaredUp. Once the account has been created, they will gain access to the organization.
   • At least one user or group must be given Full Control.
   • Admin users can edit the configuration, modify the Access Control List (ACL) and delete the data source, regardless of the ACL chosen.

   Access level	Permissions
   Link to workspace	User can link the data source to any workspace they have at least Editor permissions for. Data from the data source can then be viewed by anyone with any access to the workspace. User can share the data source data with anyone they want. User cannot configure the data source in any way, or delete it.
   Full Control	User can change the data source configuration, ACL, and delete the data source.

   See Access control for more information.

6. Click Test and add to validate the data source configuration. SquaredUp will now attempt to connect using the provided authentication method.

   Testing outcomes

   • Testing passed – a success message will be displayed and then the configuration will be saved.
   • Testing passed with warnings – warnings will be listed and potential fixes suggested. You can still use the data source with warnings. Select Save with warnings if you believe that you can still use the data source as required with the warnings listed. Alternatively, address the issues listed and then select Rerun tests to validate the data source configuration again. If the validation now passes, click Save.
   • Testing Failed – errors will be listed and potential fixes suggested. You cannot use the data source with errors. You are able to select Save with errors if you believe that a system outside of SquaredUp is causing the error that you need to fix. Alternatively, address the issues listed and then select Rerun tests to validate the data source configuration again. If the validation now passes, click Save.

   You can edit data source configurations at any time from Settings > Data Sources.

Testing and troubleshooting

If you encounter an error refer to the guidance or contact our support team in-app or via SquaredUp Support

For errors on dashboard tiles see Troubleshooting tiles.

Next steps

Once you've added the data source, you can start creating dashboards to visualize your data using any of the following methods:

1. Browse graphs: Click on the Google Analytics data source on the left hand menu, then click on an object to view graphs for the data streams. Click on the three dots
   at the top right of the graph and then Copy to, to add the graph to a dashboard, then edit if required.
2. Data Explorer: Click on the Google Analytics data source on the left hand menu, then click on one of the data streams, such as Page Views. The preconfigured data streams will quickly show you data. Use the Data Explorer to show the data you want, then click Save to dashboard.
3. Add a data tile to a dashboard and select either:
   • A preconfigured data stream, such as Sessions by Page or Page Views, to quickly see data for the selected object, see Showing page views per day using the Page Views data stream
   • A configurable data stream, such as Report Query where you can enter your own query from GA4 Query Explorer, or Metric Query with a metric and dimension.

Data streams

You can use these data streams to create new tiles to show data, or if there are preconfigured dashboards installed you can copy or edit those.

The following data streams are installed with this plugin.

Showing page views per day using the Page Views data stream

1. On a dashboard click + and then Data to add a new data tile.
2. Data Stream tab:
   Select Google Analytics (GA4) then Page Views.
   Click on the Objects tab (or click Next).
3. Objects tab:
   Click on the site you want data for.
   Click Done.
4. A suitable visualization is chosen, where possible, but at this point you might like to change the visualization used, see Visualization Settings.
   In the right hand pane you can also hide and sort columns,
   The Timeframe, Shaping and Columns sections can help you configure the visualization as you need.
   For example, to configure the Bar chart for views we will configure the Group by and Aggregation type.
5. Timeframe tab:
   Choose a longer timeframe, for example Current Month
6. Shaping tab:
   Shaping allows you to perform filtering, grouping and sorting operations on the data.
   Expand the Group section, and on the Group by drop down select Timestamp, and on the Bucket by drop down select Day.
   Change the Aggregation type to Total.
   
   Filtering

   Data can be filtered according to whether data in a column meets or does not meet specified text or numerical value conditions.

   Multiple filters

   You are able to add multiple filter conditions using the following operators:

   • AND: All conditions must be satisfied (e.g. Status-Equals-Closed ANDType-Equals-Question).
   • OR: Any condition can be satisfied (e.g. Status-Equals-Pending ORStatus-Equals-Closed).

   Available filters

   The following options are available when filtering data, which ones display depends on the column type.

   Option	Description
   Equals	Checks if the value of a field is the same as the specified value. For example,a Status of Active will return results where the status is Active.
   Not equals	Checks if the value of a field is not equal to the specified value. It returns true if the values are different. For example, a status of Active would return results where the category is notActive.
   Contains	Returns data if the specified value exists within the field value. For example, example: URL Contains projects will return results where the URL includes the word "projects" anywhere in the string.
   Doesn't contain	Returns data if the specified value doesn't exist within the field value. For example, example: URL Doesn't contain projects will return results where the URL doesn't include the word "projects" anywhere in the string.
   Less than	Checks if the value of a field is below the specified value. It is used for numerical or date values. For example, IncidentsLess than50 would return results where the number of incidents is below 50.
   Greater than	Checks if the value of a field is over a specified value. It is used for numerical or date values. For example, Incidents Greater than 50 would return results where the number of incidents is over 50.
   Is more than	Available when working with a date / time column. Checks if the value of a field is older than a given time period. You must additionally specify a time quantity and period, and whether to measure ago or from now. For example, Due Is more than 100 days from now will return results where the due date is later than the current day + 100 days. Similarly, Due Is more than 100 days ago will return results where the submitted date is earlier than the current day.
   Within last	Filter records that fall within a specific time range from before the current date and time. You must additionally specify a time quantity and period. For example, Submitted Within last 100 days will return results where the submitted date is between the current day and 100 days ago.
   Within next	Filter records that are within a specified time range after the current date and time. You must additionally specify a time quantity and period. For example, Event date Within next 7 days would return results where the event date is within the next week.
   Is empty	Returns all data without a date value. Useful for identifying records where data is missing.
   Is not empty	Returns all data with a date value.

   Grouping

   Use the grouping section to group and aggregate data columns.

   For example, for AWS cost data you might configure the following settings to display a table or bar chart of cost per label:

   • Group by:label
   • Aggregation type: Total
   • Aggregation column: Amount

   Which columns are available depends on the data stream you chose.

   Configuring grouping enables different visualizations to be displayed, such as bar chart and donut. For example, grouping tickets by channel allows you to show a donut of how many tickets were logged by email vs web form.

   Bucket by

   If you group by a time column, and further grouping is possible, the Bucket by dropdown appears. Use this field to control how the time data is grouped, for example by hour, day, month etc.

   Aggregation

   To aggregate your data, you must select an Aggregation type and a target Aggregation column. For example, if creating bar chart for an Azure Resource Group cost, you could configure the following settings:

   • Group by: Timestamp
   • Bucket by: Day
   • Aggregation type: Total
   • Aggregate column: Cost

   The following lists the available aggregation types.

   Aggregate	Description
   Total	Sums up all numerical values in a dataset, providing the overall total
   Average	Calculates the mean by dividing the total sum by the number of values
   Count	Determines the number of entries in a dataset, including duplicates
   Distinct Count	Counts only unique values, ignoring duplicates
   Max	Identifies the highest value in a dataset
   Median	Finds the middle value when data is sorted in ascending order
   Min	Identifies the lowest value in a dataset
   Mode	Determines the most frequently occurring value in a dataset

   Sorting

   The Sort section allows you to select one or more columns to sort your date by, in either ascending or descending order.

   While this sets the default sort order of data, but you can always click on a column heading to sort the data table on the fly.

   To sort by multiple columns, click Add sort by to add a new row of sort fields to the list. This allows you perform more complex sorts, such as sorting data by the data it was created, then sorting those results alphabetically.

   Enabling the Top toggle allows you to specify the top n rows of data to display.

7. Optionally, configure Columns.
   
   Column configuration

   SquaredUp automatically defines the metadata retrieved from data streams so the data is assigned the correct data type, however in some circumstances you may want to override this.

   For example, when retrieving data using the Web API plugin, scripting, or custom query data streams (such as Splunk Enterprise plugin), the assigned data type may not be quite correct or as you expect.

   Column settings

   Use the following options to format your columns.

   Option	Description
   Name	To rename a column, click the current Name value and enter a new one. Columns that can be renamed display the Rename column icon when hovered over.
   Type	Click on the row and then select an option from the Type dropdown to change the data type of the column. If any additional options are available to configure, the dropdown is expanded below.
   Value	Displays the original value of the column.
   Formatted	Displays the formatted value of the column.
   Add a copy of this column	Click to duplicate a column. The copied field displays Copy of [field name] above its Name.
   Remove this column	Click to delete a cloned column.

   Custom formatting

   Clicking on a row opens the Configure column window, where you can modify the column name and type. Additionally, you can also supply a format expression to manipulate how the column value is displayed.

   For example, you might want to attach a label to a value, combine multiple columns together or display the result of some other calculation.

   See Expressions for more.

   Custom columns

   Custom columns allow you to take any data stream and add completely new columns defined by you. You can provide a value expression when configuring the column to calculate the new column value by leveraging the available data.

   Note

   It is currently not possible to reference a custom column via an expression from inside another custom column.

   For example, you might want to create a new URL column by entering an expression that combines an address with a ticket ID, or to map data values in order to create state columns.

   To add a new custom column, click the Add button under the columns table and select the Custom option. A new column is then added to the list and the Configure column window opens.
   

   See Expressions for more.

   Comparison columns

   Comparison columns are used to compare two values, for example you may want to compare the number of tickets raised this month to the number of tickets raised last month. You can choose to show the value as an absolute change (for example, 12 more tickets) or as a percentage change (for example, a 28% increase).

   When a column has a Type of Number, the Add comparison

   button displays at the end of the row, which you can click to open the Add comparison window.

   
   From this window, if you have multiple columns with a Type of Number, you can create a comparison column by doing the following:

   1. Column A:
      Select the first column to compare against. Automatically populated with the column of which you clicked Add comparison
      .
   2. Column B:
      Select the second column to compare against.
   3. Output:
      Select how to display the comparison value. This value is displayed in the Preview field. Choose from:
      • Absolute: Show the numerical value of Column A - Column B.
      • Percentage: Show the ratio of Column B to Column A as a percent.
   4. Click Add to create the comparison.

   Additional options

   Some data types have advanced settings that can be configured in the options section, which is displayed whenever you change the data Type or by clicking expand

   next to the column Name.

   Option	Description
   Output Format	Enter a custom format to display date values as a string. Any specified output format is supported. For example, dd/mm/yy, dd/mm/yyyy or d/M/Y. Note By default, dates and times are displayed in your local timezone to ensure the data makes sense to you.
   Input Format	Enter the format that corresponds to the inputted date. For example, if your data has values such as 05/27/24 01:44 PM, then the Input Format should be set to dd/mm/yy hh:mm aa. Any input format is supported, however if you have a custom input format that is missing any time zone information, the input is always assumed to be UTC. Note This field is required if the data string for the column are not ISO-8601 formatted. For example, 2024-09-09T13:52:25.281Z.
   Currency	Select the currency to display the value in. This does not convert the currency value.
   Decimal Places	Formats the number of decimal places for a supported data type. Enter a value between 0 and 20.
   Link Text	Specify the text of the URL links in the column.
   Format as duration	Toggle between displaying the time value in minutes and seconds or seconds.
   Map Values to States	You can map the states you're getting back from your data to the states SquaredUp expects. SquaredUp expects the following values for states to be able to show Status Blocks in the correct matching color: success green warning yellow error red unknown gray If your data uses different values for states, you can map them to the values SquaredUp expects. Tip: Any state value that SquaredUp doesn't recognize gets automatically set to unknown. You can usually just leave out the unknown state from your mapping and just specify the other three states.

8. Optionally, configure Monitoring and KPIs.
9. When you have finished click Save.

Configurable data streams

The following data streams installed with this plugin have configurable Parameters.

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