There are a few different factors that affects load times in Google Data Studio (GDS), which also affects what you can do to improve performance. Here are our recommended steps.
1. Reducing the complexity of your Dashboard
- Number of widgets - at present Data Studio will perform one query against Funnel for each widget in your report, even if they are displaying the same data (i.e. the same segmentation or breakdown) filtered in different ways. Minimising the number of widgets therefore will have great impact on performance. Splitting your report into multiple pages might be a good place to start.
- Cardinality - viewing your data by Date, Traffic Source, Market and Campaign compared to say Traffic Source and Market only will mean both Funnel and Data Studio will have to process a lot more data. So, basically removing dimensions that aren't absolutely crucial can have good impacts on performance.
- Filters - filters applied to your report are performed in Data Studio rather than in Funnel. That means Funnel will have to load, process and transfer all data relevant for your query, and then let Data Studio apply the filtering. This goes for widget specific as well as report wide filters. A common pitfall is for example a "Top 5 Campaigns by Impressions" widget, which will have to load data for every campaign from Funnel, before Data Studio filters out the top 5.
- Time period - essentially the same thing as a filter, but treated somewhat differently in both Funnel and Data Studio. However the consequence is the same, if you have a report wide or widget specific period setting for a year, that means data for a full year will have to be loaded, processed and transfered. Limiting the number of widgets that displays time series, reducing the length of each time series, or displaying time series data for a summary rather each of your campaigns are things that can have a positive impact here.
- Type of widget - analogous to the item above, using the right widget for your use case can be important. I.e. using a time series widget when you are interested in totals will cause a lot of extra data to be queries.
2. Limit data volumes by using Views
Setting up a View in Funnel gives you the ability to limit the number of fields available in Data Studio which can make it easier to work with. It also gives you the ability to apply filters to further reduce the amount of data that gets sent back to Data Studio.
3. Using Extracts on Views (experimental)
If you still experience bad performance or rate limits, you can use an Extract. Extracts will pre-calculate, aggregate and store the defined dataset separately to make it readily available when Data Studio requests new data. Please read the linked article to learn more about potential limitations and how you can get access to this experimental feature.
4. Going via a Data Warehouse
If you are unable to get satisfying performance out of our Community Connector for Data Studio, or require highly segmented data in your reports, a workaround could be setting up a Data Warehouse export in Funnel, exporting data to e.g. BigQuery and using the Data Studio BigQuery connector to get that data into your report. These methods can be used in parallel and can be applied to only a subset of your data, or used in only a specific widget.