Google Data Studio | CXL Review
Create more insightful reports and save time
Google Data Studio is the seventh course in the Digital Analytics Minidegree program made by CLX Institute. This course aims to teach you how to create impressive, time-saving reports in Google Data Studio.
Michele is a wonderful instructor, eager to share her experience through many useful tips.
- Getting started
- Building charts
- Using color
- Filters, controls, and segments
- Calculated fields
- Changing data sources
- Sharing and access
- Other useful features
Google Data Studio (GDS) allows you to build interactive reports and dashboards. This Google product is free for everyone, currently without any limitations or features available only for paid users.
It connects to different types of data, either natively (Google Connectors) or through third-party connectors. If you’re a user of, for example, Amazon eCommerce Data or Adobe Analytics Data you can use those as well within Data Studio, through some third-party and partner connections.
Google launched the beta version of Data Studio in mid-2016. The full release (out of beta) was in 2018. It’s constantly changing and updating, so be sure to double-check any information found online (including this post).
Pranav Kaushik wrote a terrific article about getting started with Google Data Studio on Medium, so I’ll skip that part:
Getting Started with Google Data Studio
One of the best data visualization and reporting tools is entirely free to use. Get started with the basics of Data…
Anatomy of Data Source
As you can see in the screenshot below, colored in green are Dimensions, and in blue are Metrics:
You can change a numeric field from a metric to a dimension — if it makes sense to do that.
If you don’t have access to another Google Analytics account, or you want more data to manipulate, you can use the Google Merchandise Store demo account.
Aggregation is the process of reducing and summarizing tabular data.
There are several ways you can apply an aggregation method to your data in Data Studio:
- In the data source: a field’s default aggregation determines how that metric is displayed in charts.
- In a chart: report editors can override the default aggregation and apply a different one to the metric on a chart by chart basis.
- In a calculated field: you can use specific aggregation functions within a calculated field formula to produce aggregated metrics.
GA metrics are already set up aggregated, and dimensions are set up as the appropriate type.
If you use some other type of data source rather than GA, you will have to be more attentive about type and aggregation.
The easiest solution is not to set the aggregation in the data source (leave as none), then choose it when you go to use the metric or use a calculation. By default, when you add a metric to a chart it will SUM.
The best advice will be:
Google Analytics API > Spreadsheet > Data Studio
Connect the Google Analytics API to a spreadsheet and connect that to Data Studio rather than going to the native Google Analytics connector. The reason you might want to do this is to avoid sampling.
There are two main options:
- Connect to a Table
- Custom Query
For whatever the reason might be, sometimes you don’t want to go and build an entire report. This is where the Explore feature in Data Studio kicks in. It allows you to build a quick and dirty view of the data. You can just throw some charts on and start to explore the data and see what it looks like, what some of the values are, etc.
The great news is you can actually save it from an Explore to a report once you’re done.
Think about design before you start
Some questions that can possibly be handy:
- What data points do you want to include?
- How much user engagement do you want?
- Are you building something highly flexible and filterable? Or a curated snapshot?
Sketch it out!
The Data/Pixel Ratio
The Data/Pixel Ratio is a designing principle. It is the proportion of pixels that is used to present actual data compared to the total amount of pixels used in the entire data set.
Edward R. Tufte first coined this as the Data-Ink ratio. Later Steven Few renamed it the Data-Pixel ratio.
“The more effectively the data is presented, the less work the consumer of the data needs to do to understand it.” (Analytics Demystified)
Making your reports and your visualizations as clean and concise as they can allow the user to focus on the data rather than the unnecessary stuff.
Report Basics: Customizing the basics
You can customize things like:
- Color scheme
- Where the nav appears and whether it’s visible
- The size and orientation of the page
Report Basics: Page Size
Control at either:
- The report level (all pages will be 1200x900) or
- Customize individual pages to be bigger or smaller
Customizing individual pages is really helpful when you have different amounts of information on each page and you don’t want a ton of white space.
Available Data Visualizations
- Line charts
- Bar, column, and area charts
- Pie and donut charts
- Scatter plots
- Pivot tables
What chart should I be using?
The answer depends on what you want to show with the data.
- Use Line charts to emphasize a trend
- Use Bar/Column Charts to show differences
- Use Area Charts to show differences over time
Friends don’t let friends use pie charts. Yes, this includes donut charts.
Why pie charts fail:
- The human brain can’t judge the size difference in an area very well
- People use too many pieces
When are pie charts okay?
- When you have minimal items (Less than three)
- They must be parts of a whole!
- You only get one (no comparisons!)
If you need to compare, use one pie chart and something else:
Choosing color in reports and visualizations is an important step. It can help people understand data better, or it can be a huge problem — depends on your color choice.
Don’t rely solely on colour, because 9% of men and 0.5% of women in the world is color-blind. And so are the black-and-white printers!
Be sure to keep colors consistent: match series colors, across multiple charts. In Data Studio, you do this by setting Color By to “dimensions values”.
Coloring by Series Order vs. Dimension
When you have multiple dimensions, Data Studio lets you choose between:
- Series Order: Colours are based on the order of the series and your sort order.
- Dimension Value: Colours are based on the value of the dimension itself (for example, a desktop will always be blue, mobile will always be red.)
Make sure your date field is actually set up as a Date (in your Data Source)
Filters, controls, and segments
Types of “Filtering”
- Filter Control
- Widget/(chart)-level Filters
- Segments (GA)
Example use cases:
- Organic Search report
- Mobile-only reporting
- Specific subdomain/website
- Exclude an OS, e.g. Linux or Blackberry
- Create once, apply to multiple widgets (if you like)
- These only live in this report, they are not an asset shared across multiple reports
- But they will be copied if you copy a report
- And if you edit the filter, it will edit it everywhere it’s used…. (be careful!)
In GA, you can also apply a segment:
- Synced or not
- Must create a segment in GA
- Once you create it in GA, it automagically appears in Data Studio (if in doubt, hard refresh)
Adds a drop-down so that report users can filter the data
Put your filter controls in a commonplace on every report, to train your users on where to find them.
- Drop-down Filter Control
- Interactive Charts
- Pop-up or always visible (fixed size)
- Single-select or multi-select
- Default value
- Enable Search Box
You can also use interactive charts as filter controls.
Page vs. Report Level
Filter Controls can be:
- Report level
- Component level (via grouping)
Data Control lets users actually change the data source that the report pulls from. (For example, one report that you can use across many brands.)
Only available for certain (Google) connectors:
- Google Analytics
- Google Ads
- Campaign Manager
- Search Console
- Ad Manager 360
- YouTube analytics
- TV Attribution
Users only have access to the data that they have access to
You can’t “cull” the list (e.g. Limit it, so that only 1–2 specific GA views, for example, show up.) Users will see everything they have access to.
Calculated fields can calculate any metrics off each other. For example: “Goal Conversion Rate” is typically Goal Completions / Sessions
Basic calculations (addition, subtraction, division, multiplication):
- Sales minus Returns
- Conversion Rate
- Price * Quantity
- More complicated functions (SUM, ROUND, REGEXP,
Aggregate (Sum, Avg, Count…)
Arithmetic (Floor, Round, Sqrt…)
Date (Date_Diff, Year, Day…)
Geo (To Country, To Region…)
Miscellaneous (CASE, CAST)
Text (RegEx, Replace, Upper…)
Two ways to create:
- In the Data Source
- In a specific chart
Pros and Cons:
For creating in Data Source:
Pros: Reusable for many different widgets (without recreating every time)
Cons: Need edit access to the data source
For creating for just that widget:
Pros: Don’t need edit access to the data (you do need edit access to the report though)
- Have to recreate over and over to reuse.
- Can’t “stack” (refer to a calculated field in another)
Super useful to:
- Clean up data
- Rewrite values
- Group values
Data Blending is Data Studio’s version of a Join. You can blend up to five sources. Note that Blends are always a LEFT JOIN — If it doesn’t find data in the far-left data set, it won’t exist.
You can create a blend by either:
- Select multiple charts, right-click, “Blend data”
- Or + Blend Data under Data Sources
Name your blends! Or, once you have a few, you might forget what it is…
Think of your blend like a new data set — you can’t create a visualization of the data set that doesn’t have the field you need!
The best practice is joining raw numbers (Then calculate conversion rates)
Changing data sources
Copying Data Sources
If you want to change:
- The GA View you’re using
- The BigQuery table you’re pointing to
- The spreadsheet you’re pulling from
You could create a new data source from scratch. But you’ll have to recreate any calculated metrics and custom metrics.
- Copy the data source
- Edit the connection
This preserves calculated fields!
Sharing and access
Sharing is similar to Google Drive permissions.
There are Owners vs. Viewers Credentials
- Using Owner’s Credentials: Anyone looking at the report will see the GA data, even if their account doesn’t actually have access
- Using the Viewer’s Credentials: Will only show them the data if their own Google account has access
Sharing Report Links
Make sure you “enable viewer settings in report link”
Sharing Data Sources
Sharing a report allows a user to edit the report. They can use the existing fields in a data set. If you want them to be able to create or edit fields, you need to specifically share edit access to the data source.
Data Source Permissions
- Viewers and Editors can not refresh the data. Only an owner can!
- Relevant for Spreadsheets, BigQuery tables, etc (less so for GA)
Other useful features
- Scheduling — You can schedule reports to auto-send. This function is great for alerts!
- Export to CSV/Sheets
- Export to PDF
- Version History
- Commenting — Unlike other Google Drive document types, Data Studio does not (yet) have a commenting option.
Weird Data Studio quirks
- GA and DS may function slightly differently sometimes
- Case sensitivity: Data Studio might be case sensitive in a place where GA isn’t. It’s weird.
Google Data Studio turns your data into informative reports and dashboards that are easy to read and share. After completing this course you’ll be able to use Google Data Studio on a professional level.