After putting so much time and effort in creating your dashboard in Data Studio, it can be quite frustrating to wait 10 or more seconds for it to load.
This, combined with the various reports showing that user experience decreases with page load time, only makes your frustration to get bigger and bigger with each extra second it takes to load the dashboard.
And if you use non-Google connectors like the ones provided by third-party tools like Supermetrics or CervinoData, the load time only increases.
Although not your fault, slow loading Data Studio reports can put you in a bad light in front of your company’s stakeholders or clients who use the report.
The Data Studio slowly loading reports fix
Even though there are some ways of decreasing the report load time, like adjusting data freshness or using fewer custom fields, usually they have a limited effect on report load time.
So after implementing them, there is a high chance that your Google Data Studio reports will still load slow.
Fortunately, there is a better fix to the slow load time which can make reports load significantly faster.
And the fix is Data Extracts.
Data extracts allow you to extract specific fields from an existing data source, on a regular basis.
By doing so, when visualizing the dashboard, instead of loading the whole data set, you’re loading only the selected fields. And this makes your report load significantly faster.
That’s why when your Google Data Studio reports load slowly, it is worth exploring this fix.
Implementing the fix
To implement the fix, we need to create an “Extract Data” data source and replace the original data source with it.
To create the new data source, from Data Studio homepage, click on Create and select “Data Source”.
On the next screen, select the “Extract Data” connector and configure it as follows:
- Select the original data source from which the connector will extract the data. In our case, this is the Google Analytics connector used in our original report.
- After selecting the data source, we need to choose the metrics and dimensions that the new connector will extract.
For better performance, select only the metrics and dimensions already used in your original report.
- Next, you need to define the date range of your extract. As a general rule, the date range should be as short as possible. Meaning if your report focuses only on the last 30 days, select the past 30 days as the date range.
For higher accuracy, you could add 1-2 buffer days to the first and last day of your date range.
- If you use segments or filters in your original report, you should also define them in the extract.
In our example, the original dashboard used the “Organic Traffic” segment so, we also added it to the data extract.
- Enable auto-update. For extracted data to be up to date with the original data source, you need to enable auto-update.
You can choose to auto-update your data on a daily, weekly, or monthly basis.
In our example, we are trying to speed up a Google Analytics organic traffic report. So for the data source, we used the original Google Analytics data source and set “Organic Traffic” as the segment.
After you’ve configured the data source, click “Save and Extract” to finish the setup.
With the new “Extract Data” data source ready, go back to the original report and change the old data source to the new extract data source we just created.
And with the data source change, the fix should be applied and reports ready to be used.
Hopefully, the company’s stakeholders or clients viewing the dashboard will be pleased by the improved load time.
To test how the new data source compares with the old one, we created a simple dashboard that uses data from the Google Analytics demo account. For a closer match with everyday dashboards, we applied the “Organic Search” segment to all report charts.
With default Google Analytics data source and a “last 30 days” date range, the dashboard loaded in 5.5 seconds. After we switched to the new “Extract Data” data source, the dashboard loaded in 1.7 seconds.
Meaning the “Extract Data” connector loaded data 3.8 seconds faster than the original Google Analytics connector.
In a second test, we changed the date range to “previous month” and tested the “Extract Data” data source first. This time, the “Extract Data” connector loaded the data in 2.1 seconds. When we switched the data source back to the original Google Analytics, the dashboard took 8 seconds to load.
We did many similar tests and the “Extract Data” data source always loaded data faster or much faster, compared to the original data source.
Thus, we can safely conclude that the “Extract Data” connector loads reports significantly faster than the regular connectors.Are your Google Data Studio reports loading slow? Use the “Extract Data” connector to load reports significantly faster the regular connectors. Click To Tweet
A word of caution
During our tests, but also from the observations made by other users, we noticed that using the “Extract Data” connector with Google Analytics or Google Ads data sources sometimes results in data discrepancies.
It’s not clear what is causing these discrepancies but let’s hope the Google team is working to fix them.
That is why whenever you plan to use a Google Analytics or Google Ads data source with an “Extract Data” connector, check to see if the reported numbers match.
As the last thing you would want is to have higher speeds over less reliable data.
It’s also worth noting that you should not use the “Extract Data” connector with data sources larger than 100MB, as that is the data size limit for this connector.