The Anatomy of a Great Google Data Studio
“An editorial approach to visualization design requires us to take responsibility to filter out the noise from the signals, identifying the most valuable, most striking or most relevant dimensions of the subject matter in question.” — Andy Kirk
I wanted to start with a quote that tells you the importance of data visualisation. As a marketer or advertiser, you would encounter a real circumstance by providing a report of KPIs, metrics or analytics to prove the effectiveness of any campaign. Sometimes, you will drown and lost in the sea because you have tons of data you might acquire. Yet, showing these statics data won’t work for your manager/boss since it can barely help with storytelling. Instead, you need a visualisation tool that could turn data into a landscape that you can explore with your eyes. And hence, this is where Google Data Studio ( GDS) comes in, a tool that puzzles all unconnected dots into a meaningful act.
What is Google Data Studio?
Google Data Studio is a free data visualization product that is always referred to as Google’s version of Tableau. It is a web-based tool that allows connecting with different native or third party data sources. Since its launch as a beta in mid-2016, the feature is constantly changing and updating and up until now, users can enjoy lots of improvement for FREE.
Google Data Studio Quick Overview
Log into GDS. Go to this link. On the LHS, you have your quick menu to create a new report or view the shared report. There are several pre-built templates available up the top, and you also have the option to create a blank template from scratch. The Data Sources and Explorer tab are found next to the report that is immediately accessible.
Data Source — Google Data Studio Connector.
Data Source is split into two concepts. One is the native Google Connectors such as Google Analytics, BigQuery, Excel, or any Google product. And another one is the Partner Connectors, which partners build connectors for DS that allows users to bring in other data sources.
If you connect the data source into GDS, you will see two kinds of DS anatomy: the dimensions in green and Metrics in blue. You can have different types of dimensions and metrics. For metrics, you will have the option to choose how the aggregation shows.
Now, Google Analytics metrics are already set up aggregated, so the dimension is set up as the appropriate types. But you still need to be more thoughtful about type and aggregation if you later use another type of data source other than GA. The easier way might be not to set the aggregation in the data source ( leave as none) and choose when you use the metric or use a calculation. By default, when you add a metric to a chart, it will SUM.
There are some other popular connectors worth talking about, especially Google Sheets that can connect any of the flat files you get from anywhere. There is also a life hack for GA users where you can connect GA API to a spreadsheet and then connect with DS ( rather than choosing the native GA connectors).
Why would users want to connect Google Analytics API to Data Studio? Few reasons are as follows ( I’ll talk about a little hack for this later) :
- Avoid Sampling
- Structure data in a certain way
- Pull non-goal data for funnel
How to connect Google Analytics to data studio?
I’ve briefly mentioned how to connect GA 4 to data studio, and since many of you could still be Universal Analytics users, I’ll show you the best practices of using UA as your data source.
First, you would need to click on the “Blank Template” to create a dashboard, select “Add data” to connect a source, click on GA connector, which always pops up on the first row.
Pro tip: Right before choosing your data source to data stdio, always remember to set the data source at the report level.
To do that, go to File > click Report Settings > select Data Source.
Taking the step further would help you save a huge amount of time if you ever need to switch out the underlying data source, and you won't have to switch it for each widget manually. But, there is always an occasion when you would not want to set the data source at the report level, especially when you’re mixing and matching a ton of different sources. It won’t save much/any time.
When you select the GA connector as your data, always remember to give a logical name, adjust your data freshness duration, and enable “ fire editing report”, which will allow you to rename fields, change aggregation, etc. Once you have your aggregation set up correctly, you can click create report and mess around with the charts and data. ( I’ll explain features, how to create the chart in the dashboard, and more in the next article).
What if you are not ready to commit? “Explore” a data source before you build out a report (quick and dirty, before you bother thinking about the layout of a report, etc.). You can save from Explore to a report once you’re done exploring.
There is always a good reason to think about the design before you start building your report in Data Studio. Grab your whiteboard and think about:
- 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!
It will help you end up with much cleaner, easier to use reports than continually churn charts on the page.
One critical principle mentioned by Michele Kiss, the GDS veteran from CXL, is getting your Data/Pixel Ratio right. This is essentially about the pixels you are using for data display because you want the pixels to display data but not the ancillary information.
So, having the graphic in the background is not really important thing to showcase in your report. Rather, make sure your report and visualisations are as clean and concise as they can be. Just like how this ratio is showing
And that’s it. You’ve enough for today’s information. Let’s dive into more details about creating the Google Data Studio report in my next article!
