
~ Feb, 17th 2022 Update – Google Data Studio Just introduced advanced data blending options, essentially eliminating one of the limitations, mentioned in this article (1). Go ahead and read about it in this article: Data Studio Introduces New Data Blending Options – a Game Changer for Data Visualisation. Other limitations mentioned in this post are yet to be addressed.
Google Data Studio is a tool I have been using more and more in the past few months. With the high usage, I have come to notice its advantages over other tools, its capabilities, but also its limitations.
There are many tutorials and data visualization guides with techniques, but not that many discussing the common pitfalls of the tool, and how to overcome them.
So, here are 4 limitations of Google Data Studio that advanced users should absolutely take into account before building their reports.
1. Data Blending uses left outer join
Blending Data in Google Data Studio follows SQL parameters for Left Outer join.
Left outer joins work by blending data by taking all the records from the left data source and combining them with the matching records from the right data source.

In total, there are 7 types of table joins, which Data Science professionals are used to working with. Having only one can feel quite disempowering for advanced users.
What does this mean in practice?
You have to pay extra special attention to how your two data sources are blended, and more specifically, the order in which you add them to the blended data entity.
Based on the definition of a left outer join, the first table (data source) added will be the one that will be used as a basis, so any additional data entries from the second one (that go beyond the join key field from the first table) will not be admitted into the new dataset.
This is a very limiting approach to joining tables as although it is advanced enough to seek patterns, it is rejecting null values.
What are the alternatives?
If you have access to it, Big Query is a great alternative for data handling for bigger data sets. By great, I mean in the context of blending data and joining tables, not so great in terms of having to set up data pipelines, learning SQL, and paying for each query. Should the infrastructure exist, do use it instead for data handling.
Alternatively, for smaller datasets and more controlled operations, Gsheets should do just fine as well. Dave Mendl has written an awesome guide on the different functions you can use to replicate the seven types of SQL joins in Gsheets, using functions such as VLOOKUP, FILTER, and advanced operations.
2. There is a limitation of five data sources for blending, all of which should share a joining key.
While still on the topic of joining tables and blending data sources, one of the most frustrating limitations I have encountered is that you can only blend up to five data sources for the creation of a new one.
While this may sound like a lot, trust me – it isn’t.
Looking at a couple of examples in the SEO realm. I use GDS for visualization of custom-extractions from Screaming Frog, which are done monthly. Having a timestamp allows for these to be imported and visualized, showing a trend-line. Well, I mean, a trend-line of five data points…
Imagine also having a separate sheet showing the dates on which any improvements on the client site were made. Combining these data sources would in theory enable measuring the impact of specific recommendations I issue. Sadly, considering this limitation, any such blending would have to happen before having an update.
So, how would this impact you?
If you are anything like me, you’d like to push the limits of the tool in terms of visualization capabilities, as well as extract the most value for the report’s users out of the data incorporated. Being able to use up to five data sources that share a single dimension, on which they could be joined, often results in limitations on what you can display and how.
Another quirky thing about joins is that Data Studio as a visualization tool does not handle missing values. So, if there are such values in the join keys, the blending will not be able to take place.

What are the alternatives?
The easiest solution is to blend data offline, using Google Sheets or Excel. You can integrate both with Zapier or other automation tools, even though this solution is quite wonky in its implementation.
3. Incorporating many data sources will likely cause reports to break.
If you have ever built a Data Studio report, you’d know about the ‘kiss of death’ – breaking charts, unknown data sources, user configuration error, data set configuration error…. error, error, error.
Sadly, Data Studio does not have a good reputation in terms of being kind or helpful when your tables break, it’s like they don’t even care that this could absolutely repel users from ever visiting your report again.
I absolutely love this article by Juan Bello about the common Data Studio errors and how to fix them. I won’t go into a lot of depth about each of the errors, but I will say that there are several different scenarios that you should investigate.
What I have learned to implement is the rules of these two gems:
- simplification – simplify, remove and reduce.
- ownership of connection – this means doing everything in your power to manage the added data sources and the ways they connect
By reducing everything you don’t need and incorporating different dashboards into one, you get to keep a good user experience, without necessarily compromising on the quality of the report. A good rule of thumb is providing a good, stable holistic overview, which links to fancy, less-stable, granular data views.
4. The useability of connectors highly depends on their internal database schema.
There are hundreds of connectors you can use in your reports, as well as embedding your own data sources. Over a century ago, Emmert Wolf wrote that "a man is only as good as his tools", and it remains true to this very day.
So, let’s talk about the tools.
What I have noticed is that the bulky, ugly-looking spreadsheets make the best type of data sources. For instance, the Google Analytics Data source has one connection per property, which includes all data fields. For comparison, SEMrush’s connectors involve choices between three different menus, each connecting to a different segment of the data, contained on SEMrush.
What are the alternatives?
Integrate directly with the APIs, as opposed to using the connectors. This is not only the safest connection in terms of managing the data source and surpassing authentication and potential delays but also as it enables structuring the data source the way you want it to be structured.
The Takeaway
While this may be a lot to take in, before providing the takeaway, I just want to emphasize: Data Studio is absolutely amazing and I use it every day for all sorts of reports.
I have experience in building small reports, such as a personal finance dashboard to track my income streams, to robust reports for SEO consultancy, as complex as using hundreds of different data sources into a single report.
I advocate with both hands for incorporating Data Visualization and reporting in all aspects of life, which is perhaps why I love Data Studio as much. So, it is perhaps, as the old saying goes:
We criticize the things we love the most
On a serious note, though, hopefully, this list has helped both beginner and advanced users to be better aware of the capabilities of Data Studio, its limitations, and how to overcome them.
To sum up, here is a list of the 4 Google Data Studio limitations, and some handy alternatives in overcoming them.
- Data Blending uses left outer join
Alternative: Perform complex data augmentations in GSheets or Big Query, depending on the size and complexity of the data and your infrastructure capacity.
2. There is a limitation of 5 data sources for blending, all of which should share a joining key.
Alternative: Blend data offline and use automation tools to ease the process.
3. Incorporating many data sources will likely cause reports to break
Alternative: Simplify the report and maintain ownership of data sources.
4. The useability of connectors highly depends on their internal database schema
Alternative: Use APIs, instead of connectors.
Feeling curious? Keep reading…
4 New Data Studio Features, Released in 2021 That You Need to Know About