Google’s Data Studio dashboarding tool is progressing quickly – almost too quickly to keep up with.
But thankfully, here at CIFL we have a dedicated community of Data Studio users, standing by to bring you the latest vision of what’s possible.
Without further ado, here are all the latest Google Data Studio features + hacks, in chronological order. Many thanks to Ahmad Kanani of Siavak for his recent contributions:
The video below summarizes 5 key updates from the fall of 2019, with each summarized in text below.
Starts at 39s in video above.
If you create a table or chart, and select ‘Apply Filter’ under Interactions, when you click a row within that table, it’ll filter the rest of the charts on your page for just that data slice.
For example, here we have a chart showing Search Console Impressions + click-through rate trends across a site, and below we have a table with URL-level data.
If we set the table to ‘Filter on Click,’ when we select a row from the table, it’ll filter the chart to show trends for just that row.
Starts at 59s in video above.
Another beautiful new feature in Data Studio, is the ability to set ‘drilldowns’ on charts + tables.
Drilldowns allow you to set a default, more zoomed out view, and allow your team to drill deeper into more granular slices of data as they choose.
This cuts down a lot on the raw number of charts / tables you need to use in a report, and works really well when combined with the ‘Apply Filter’ on click method above.
For example, if we have a list of Search Console data by URL and keyword – we may want to first look at the URL-level totals, and then ‘drill down’ into the list of active search keywords for that URL.
Starts at 1:40 in video above.
Before the last couple months, Data Studio had a pretty narrow set of date ranges.
You could compare this month, last month, this year, last year, but not much more granular than that.
This made it tough to do stuff like display traffic from the last rolling 12 months, or rolling quarter.
Thankfuly, they just introduced ‘Advanced date comparisons,’ which allow you to set custom date ranges like this.
In our Monthly SEO report template, we use these to set ‘rolling 12 months’ and ‘rolling quarter’ date ranges for charts.
You basically specify how many days / weeks / months / quarters / years you want to go back or forward from today, for both the start and end dates on your chart.
With this advanced functionality (full docs here), you can create pretty much any rolling date window you like.
Starts at 2:26 in video above.
Not every person on your team is going to crack open Data Studio and use your reports.
For some people (like clients), they prefer getting an old school PDF report sent to their inbox.
Data Studio now supports that – you can, from the ‘Share’ menu, schedule email delivery via PDF to your team, and specify which pages are included in that PDF.
Another new sharing feature we’re using quite frequently is the ‘Get report link’ view, which allows you to send a report with your filters + date range pre-set for the person you’re sending to.
This saves us a lot of time messaging in Slack about what we’re seeing in a report.
Starts at 2:55 in video above.
Last but not least, one for all the BigQuery users out there.
We use BigQuery to run our Website Quality Audit service, and couldn’t live without it – it allows us to process many thousands of times the data volume that we could handle in Google Sheets.
By the way, if you’re a marketer and not yet using BigQuery for your data analysis, it’s time to step your game up – get started with BigQuery here.
The Data Studio team has been making big updates to how fast data can be pulled from reports into BigQuery. They’re releasing what’s called ‘BI Engine’ to all users by default, who are using Data Studio to visualize BigQuery data.
We’ve seen this speed up our report load times by over half. Keep it coming!
And if you’re not using BigQuery, you can similarly speed up your report using what’s called ‘Data Extracts’ in Data Studio, which pre-caches a subset of your data on a schedule.
Thanks again to Ahmad Kanani of Siavak for contributing his favorite Data Studio features this month.
Pulling data from APIs like Google Analytics into Data Studio can be slow, especially if you’re retrieving analytics data for a large site.
Extract Data helps speed up data source loads, by basically caching a saved snapshot of another data source (ie a snapshot of your Google Analytics data).
It can be setup to pull specific metrics and dimensions for a given time period, on intervals that we choose. It will save a snapshot of the data set inside Google Data Studio to be accessible immediately for reporting purposes.
First, make sure you have a data source set up in Google Data Studio:
Resources > Manage Added Data Sources
And then, from the Add a Data Source button there, select ‘Extract Data’ by Google from the list of Data Sources.
Select your existing data source and specify Dimensions and Metrics that you need to be extracted, i.e. the ones you need to use in your report widgets.
Enable Auto Update, choose extraction frequency (daily, weekly, monthly) and click Save and Extract:
Replace the data source of your widget with this new “Extract Data” data source that you have created.
The downside of this approach is that the data isn’t always completely fresh, as it’s updated daily, weekly, or monthly, but its benefit is that because the report doesn’t need to wait for an API connection to pull data, it will load extremely quickly and will be delightful for the end-user (which is usually a busy business owner) to use on daily basis.
By default, a filter control or a chart used to filter the report, will filter ALL the other charts and widgets on the report canvas. What if we only want a filter control to affect one or a handful of other widgets, and not the whole report?
In such situations, we can group widgets together by selecting them together and selecting Arrange > Group in the menu, or by pressing Ctrl/Cmnd+G on our keyboard.
When one or more widget or chart are grouped together with a filter control, any filtering will happen only inside that group, and won’t affect the rest of the report.