Google Data Studio For Beginners

Welcome back! This is my 7. post reviewing CXL Institute’s Digital Analytics Minidegree. This week will be about Google Data Studio. First question is what is Google Data Studio?

Data Studio is Google’s reporting solution for power users who want to go beyond the data and dashboards of Google Analytics. The data widgets in Data Studio are notable for their variety, customization options, live data, and interactive controls (such as column sorting and table pagination). There are key points to get the maximum efficiency from the data studio. First one is the dashboards.

Dashboards

Basically they are the visualization of the data in the best way to quickly understand what’s going on.

To build a dashboard in Google Data Studio, you start in Edit mode, which has a graph paper background where you drop visualizations such as tables, pivot tables, pie charts, or scorecards. When you’re done, you can see the dashboard visualizations in View mode and share them with your clients or colleagues. These visualizations show data from your data sources.

With the visualizations, you can see and interact with your data in the way that’s most useful to you. For instance, you could pull in all of your traffic sources and a table that shows how many goals are being achieved such as new leads.

Date ranges are another interactive element. You can load data for certain date ranges so that anyone who views the dashboard sees the date ranges you specified. You can make the default date range for the last 30 days, 14 days, or 7 days, depending on what you need. Then you can compare the range you specify to last year’s period so you can see trends in the data.

You also have control over the dashboard’s appearance including the layout, colors, fonts, sizes, and so on.

Using colors

Consistency is key and the easy part of data visualization. We just need to define some presentation rules and to apply them each time we work on the involved contexts.

One of these constant rules is the systematic use of the same color for a specific dimension value across the same report or a collection of reports.

For example:

• Red for “mobile” device category (see the screenshot below)
• Blue for “Google Ads” channel value
• Green for “ecological” political part value

Inside Google Data Studio, these color rules are defined through a dedicated panel named “Dimension value colors”. It contains all reported values across all dimensions.

But, for the moment, there is a limitation regarding bar chart widgets. Color by dimension value is available only if the bar chart displays several dimensions. There is a solution but its too complicated for now.

Filters

Adding date range filters

With Date Range filters, you can group data by specific date ranges.

To add a Date Range filter, you can select the icon, and then draw a shape on the report where you want the filter added. Within the Date Range Properties panel, there is a “Data” tab. Use the default date range selection. In the “Style” tab, you can change the look of how the data appears on the report. Similar to Google Analytics, you can select predefined options like the Last 7 days or Last quarter, or you can customize the data.

One issue with the Date Range filters is that it will by default add the filter to every element on your page. Yet, you may want other charts or tables to have different date ranges. So, how do you correct this?

The best way to ensure that your Date Range filter is applied only to certain elements is by limiting the filter to either a single element or a group of them. First, you must group the elements together. To do this, follow the below steps.

• Select all the charts that you want to group.
• Right-click on the set, and select “Group”. Alternatively, you can click on the Arrange menu and then click “Group”.

Once your group charts, tables, etc., the data filter will only be applied to the selected elements. To change the date of certain filters, you would apply the same principle. Select the icon, and then draw the shape over the widgets that you wish to change. Be sure to group the widgets that you want to add the filter to first.

To take this one step further, you can also add the Date Range filter to every page of a multi-page report. By default, the date will only appear as a page-level object, meaning that it will only appear on the page where you add it. To make it appear on every page in your report, do the following:

• Start editing the report.
• Select the Date Range control icon.
• Click “Align”, and then “Make Report-level” menu.

You’ll now see the date range on every page.

One note about Date Range filters: These can only be applied to data sources that have date dimensions. If your data doesn’t have any dates associated with it, this won’t work.

Besides the date range filers, you can also add filter controls for other fields as well, including:

• Metrics
• Dimensions
• Styling

You can find the filter controls in the top right corner of the canvas.

You can also add Object filters, which can be applied to a single visualization. To do this, select the table or chart that you want to filter. And, then, do the following:

• Go to the visualization properties.
• Then, data and filter.
• Add the filter that you want to apply to your chart or table.

Creating advanced dimensions using formulas

In many respects, Data Studio works similarly to certain Excel reports. Like Excel, you can add advanced reporting elements by using different formulas. Known as calculated fields, they allow you to manipulate the data within your data sources. These calculated fields can both dimensions and metrics, and they appear as new fields within the data source. For example, you can use a formula like REGEXP_MATCH() to return a specific value if X matches Y, or CONCAT(), which combines text from various sources.

To create a calculated field, follow the below instructions:

• Begin by editing your data source.
• At the top of the “Field” column, you’ll see a blue “plus” button. Click on it.
• Give the field a name.
• Enter the formula that you want to use for this field.

Once the field is created, you can implement the formula by applying the calculated field to a row of data within a chart.

The formulas within the calculated field use one of the following syntaxes: Functions or Arguments.

• With Functions, you can generate formulas that use mathematical equations, logical comparison, text handling, and more. A formula can also use multiple functions.
• Arguments instruct he function to act upon a certain command. It requires one or additional field-expressions to be used as arguments: Some form of text that corresponds to a field name within the data source.

There are many types of functions you can use for calculated fields. The whole list can be found within Data Studio Help. One example of how you can use these fields is by cleaning up campaign tagging. We’ve all seen databases where there are different cases for say address, i.e. ADDRESS, address, and Address. You can easily fix this by changing all the cases to lowercase.

Data Blending

Why blend data in Google Data Studio?

The world of accounting is an interesting place. It can be complicated and confusing, but it is precisely the numbers we receive from our ad cabinets and CRM systems that give us an accurate picture of how our business is performing.

So why blend data in data visualization software? Well, here are two main reasons.

1. Performance assessment

Say you have launched a new advertisement campaign. It was running in Google AdWords and Facebook Ads platforms simultaneously. The said advertisement campaign just ran to an end and you would like to assess the total advertising expenses for a certain time period.

To receive these numbers, you would have to blend data from all of your data sources — Google AdWords and Facebook Ads — and display them in one place, or Google Data Studio in this case.

2. Data fulness

Google Analytics data can be quite incomplete. So to get a full picture of a certain event or occurrence you would need to blend data with another data source.

For example, our website has a number of support tickets and a number of “contact me” requests, filled by our website visitors. In order to see the correlation between leads and support tickets, we would have to blend data from our Google Analytics account and our website.

This is it for this week! See you next week.