A sluggish report in Google Data Studio is one of the worst nuisances that every marketer dislikes. But why is Google Data Studio so slow at times?
Creating dashboards that load faster and respond well to viewer changes is a dream that Google Data Studio users seek to have. But how can you get it?
Today, we will explain the three main factors that lead to a slower Google Data Studio and the four solutions that will help you speed it up in this article.
So, let’s get started.
What affects the speed of your Google Data Studio reports?
There are three reasons why Google Data Studio may be so slow. These may include:
Querying huge amounts of data for your reports
When you run a query that needs to load a large amount of data on your dashboard, you may experience some delays. Reports with long timeframes or with a large number of charts are often the reasons impacting the speed of your Google Data Studio.
Using complex queries to load your reports
Another reason why Google Data Studio is so slow is when you use many complex queries to build your dashboards. Performing a lot of data crunching, like using blends or applying too many unimportant or unnecessary dimensions, is a common pitfall whose consequence is a slower report.
Underperforming data sets on your dashboards
When your data sources are unresponsive or slower, you will surely have an underperforming dashboard for all the reports that depend on those data sets. Fetching large data sets directly from such sources is often a reason why your dashboards in Google Data Studio load slowly.
How to make Data Studio faster
After we’ve known the possible reasons that make our dashboards load sluggishly, let’s now dive into the solutions that can accelerate its performance. Whether you are dealing with bigger or smaller data sets, applying these tips will speed up your Data Studio’s loading time.
So, let’s dive right in!
Reduce the complexity of your dashboards
A dashboard that is too complex or filled with clutter may affect the load times in google Data Studio compared to a clean and concise dashboard. Adding too many charts, using longer timeframes, or applying a higher cardinality and data crunching can negatively impact the speed of your reports in Google Data Studio.
That is why you should avoid using longer timeframes, as it will take less time to load, process, and visualize data for three months than for a year.
Also, aim to use fewer dimensions in your reports to speed up your dashboards because, as we’ve seen, a higher cardinality is another reason why Google Data Studio is so slow. Moreover, avoid too much data blending or precalculated fields, as it can also affect the load times of your dashboards.
So, to fix Data Studio’s slowness, you should reduce unnecessary fields, calculations, transformations, timeframes, and widgets in your reports to enjoy a faster Google Data Studio.
Also, splitting your reports into multiple pages can reduce the complexity of your dashboards, which will significantly improve the speed and performance of Google Data Studio.
Tweak data freshness for a prolonged refresh frequency
Data that doesn’t need to be updated more often can lead to a faster dashboard than those that need shorter refresh intervals. Your data sets probably have different requirements of how fresh the data should be, and reducing the update frequency can fasten the load time for your reports.
This is possible because Data Studio provides features that improve your report performance. For example, the cache system in Google Data Studio allows you to fetch your data internally from temporary storage, which is much faster than getting the data directly from your live data sources.
To improve the performance of your reports in Google Data Studio, reduce the frequency with which your data in the cache system is updated. Thus, your reports will use the cache system to answer repetitive queries, which can improve your report load times.
Data Studio has a default refresh rate for each data source, where it automatically refreshes the cached data. If you need to tweak this default value, here’s the process:
- Go to the data source you need to change, and find Data freshness at the top.
- On the data freshness pop-up, select a longer refresh option that is available under the “Check for fresh data:”
- Finally, click Set Data Freshness, and you’ll be all set.
There are other benefits to the cache system beyond improving your load time speed. If you use paid services to load your reports into Google Data Studio, this feature will save you on costs for repeated queries on your live data sets.
Use Google Data Studio’s Extract Data Connector
One of the main solutions to a slower Google Data Studio is using extracted data sources to build your reports. The Extract Data connector allows you to explore a subset of your data by pulling them from your connectors and storing them as cache for later use.
Thus, it eliminates the time Data Studio uses each time it loads your reports, making it load faster. Also, exploring a subset of your data makes it more responsive to work with than with the live connectors.
Pro Tip: Use Dataslayer para Google Data Studio with Extract Data connector and enjoy the best experiences, efficiency, and performance with these powerful tools.
The Extract Data connector, however, is static, and you should keep refreshing your data sources to maintain freshness. Nonetheless, it is a minor inconvenience as you can use schedules to update your data at specific time frames.
How to set up an Extracted Data Source
To set up an Extracted Data Source, follow this process on your Google Data Studio:
- After you’ve signed in to Google Data Studio, click Create on the top left, and select Data Source.
- Use the search bar to find the Extract Data connector in the list of connectors.
- Select Extract Data on the search results, then pick an existing data source where you want to extract data from.
- Next, add the Dimensiones y Métricas you want to extract from the Available Fields list. Selected fields will appear on the far right, where you can also set Auto-Update for scheduled updates.
- You can apply aggregations, filters, and date ranges to customize the extracted data source to fit your needs. Finally, hit Save and Extract.
Once you successfully create an extracted data source, you can use it like any other connector. However, unlike the live connectors, this newly extracted data source will be much faster. Thus, it will improve the performance and speed of your reports and explorations.
Accelerate response time with the BigQuery BI engine
For large datasets with over 100 MB or where data freshness is critical, caching and Data Extract connector may be inefficient for reducing the response time for your Data Studio reports. And — that’s exactly when BigQuery BI Engine comes to the rescue.
BigQuery BI Engine is a fast, in-memory analysis service that lets you analyze your data in BigQuery. It allows you to use BigQuery data in Google Data Studio, eliminating the data processing time.
So, by taking your data into BigQuery and integrating the BI engine with Google Data Studio, you can speed up your data exploration and analysis. You enjoy the best, rich, and most interactive dashboards in Google Data studio. And more importantly, you will not compromise performance, data freshness, and scale.
Pro Tip: Use Dataslayer for BigQuery to analyze millions of rows from different data sources and combine them in BigQuery. That way, you can visualize or create faster reports in Data Studio and other reporting platforms that you love.
Accelerate Google Data Studio’s performance with Dataslayer
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