Understand the limitations of data blending in Google Data Studio and learn the tips to get past those limitations. You will make a remarkable impact and get the most valuable insights from your data sources. 

Google Data Studio, is undoubtedly the best free tool to visualize your data from different sources. It helps you unlock invaluable insights and possibilities with its powerful tools and functionality, like the data blending feature. Furthermore, Data Studio gives you an unmatched competitive advantage for your company, thus taking your business to the next level.

However, like many other Google tools, it’s easy to get by — but rock hard to master. Features like data blending are easy to get their advantages or capabilities, but only the pros understand their common pitfalls.

Thankfully, we will talk about the seven limitations of data blending in Google Data Studio and how you can overcome them. Are you ready? Let’s get started.

But first, the basics:

What is Data Blending?

Data blending in Google Data Studio is the process of merging multiple data sources into a single dataset for you to create charts, tables, and controls for your data visualization. For you to join several data sources into a blend, you need to understand the concept of data joining. So, what is it?

Understanding data joining in Google Data Studio

Because this is a big concept of data blending, it is often a challenge that many marketers find confusing. Blending two tables in Data Studio requires a join configuration with a join operator and join condition. This is how the join configuration window looks like in the Data Studio:

Data joining is the process of merging two data sets, side by side, with a shared data column from each dataset. Before 17th Feb 2022, the join configuration was limited to the Left Outer join configuration.

The new join configurations in Data Studio now include four other join conditions:

• Inner join – returns only matching rows from both the left and right table.
• Left outer join – returns the matching rows on the right table with the non-matching rows on the left table.
• Right outer join – returns the matching rows on the left tables with the non-matching rows on the right table.
• Full outer join – returns all the matching rows on either the left or right table.
• Cross join – returns every possible combination of rows from the left and the right table.

The Six Main Limitations of Data Blending in Google Data Studio 

Blending in Google Data Studio is limited to five data sources

Another limitation of blending data in Google Data Studio is that you are limited to five sources. If you have over five sources you want to merge, you’ll not get a chance with Google Data Studio.

Therefore, you may miss out on advanced or in-depth reports that require more sources to create a detailed table with too many columns. If you need more than this, consider using Google Sheets.

• Consejo Profesional: Utiliza Dataslayer para Google Data Studio to unlock over 40 data sources for blending

Google Data Studio Does not handle any missing or miscalculated values automatically

While Google Data Studio will show you when your blend has a configuration error, it will not fix these errors automatically. Such errors may result from a broken calculation or a missing field in the join keys. 

For example, the missing fields will become ‘null.’  GDS will not replace them with zeros ‘0’ or any other custom values for you if you don’t do so before blending the data.

In case of such configuration errors, you will see a pink ‘missing’ field in the join condition or a red box around the join configuration for mistakes in your tables.

Consider using pre-blend filters and calculate your fields accurately, or apply the fix manually to ensure all the join keys are valid and matching.

Blending can create unnecessarily large data sets

In Google Data Studio, without strict control on what data to include in your blend, your blend can quickly generate a large dataset with too many fields. These unnecessary datasets often slow performance and reduce your productivity. Also, you could incur considerate costs if you query more and more data through your paid connections or API services.

Therefore, your blends should include only the fields you want to visualize, which may be just a subset of your data. Also, be wary of the cross-join operations, as it may cause too much data in a blend.

Blend charts calculate all rows even if they’re unused

When you work with blends, you will often encounter the error ‘This chart requested too much data.’ As we’ve mentioned, if you generated too much data in your blend, you may have an issue visualizing them into charts. But why?

This limitation happens because a chart in a blend will calculate all the rows available even if it will never use it. That means, Google Data Studio will calculate all the fields in your data and use only those you selected. 

Therefore, think more before using join operators like cross join and include only the fields you need for your blends to avoid these errors.

Blend data may be wrong

It is common to find that the results of your blend data are not those you expected. This inaccuracy can be very hard to notice if you work with complex data. This issue often results from Google Data Studio’s way of creating the tables for your blend.

Data Studio will query the data for each table’s data before joining them into a final blend with your preferred join condition. If you applied the date ranges, filters, or calculated fields on your tables before joining them can explain the different output for your blend.

That could typically be the reason. Otherwise, Data Studio does this in the background, so you are limited to seeing the logic of what could’ve probably gone wrong.

You can try small pieces of data before blending your complex datasets. Also, ensure that all your pre-calculated tables and filters are accurate.

• Leer más: Cinco maneras de evitar el muestreo de datos en Google Analytics

Connector usability depends on internal database schema

While Google Data Studio offers several connectors for you to use for data blending, each connection often provides a single link for each of your properties. Therefore, your preferred data sources may not offer more connection property choices for different data segments.

You can integrate your data sources directly with the APIs instead of the connectors to get more flexibility and avoid these limitations. That way, you can customize the structure of your data sources the way you like.

How to Overcome Limitations of Data Blending in Google Data Studio

Google Data Studio is powerful for data visualization. While blending in Data Studio has some drawbacks, they are easy to workaround. And basically, there are tips to work some of them in Google Data Studio. Other complex workarounds require you to use Google Sheets, an excellent tool for data manipulation.

So, Google Sheets will be our go-to tool to handle all the limitations of Google Data Studio. Let’s start with the tips.

General tips to follow while blending data on Google Data Studio:

• Pick well-understood sampled data to test and strengthen your understanding of blending.
• Ensure that all your tables are in the exact order you want them to be.
• Be sure that the number of dimensions in each of your tables is minimal, when possible.
• Ensure that you’re using the correct filters and calculated fields.
• Start with fewer tables, like two, then keep adding others while watching the results.
• Don’t use a complex join condition at first, especially one that results in as few fields as possible.

How to use Google Sheets to overcome the limitations of blending in Data Studio

Most, if not all, of the limitations of the Google Data studio for blending data, can be easily fixed with Google sheets. It can be a complex process, but it is worth it.

That way, it would be helpful for you to blend your data in Google sheets, then take it back to Data Studio for reporting. In Google Sheets, you’ll get more flexibility and control over your data than it would have been in Data Studio. 

Furthermore, loading data is easier, faster, and more manageable in Google Sheets. Google Sheets offer great formulas to manipulate your data automatically without too many hassles.

So let’s roll our sleeves and get to know some useful functions for data joining in Google Sheets. Now, you can analyze your data in a single table and get meaningful insights.

Leer más: Why Google Data Studio is So Slow and How to Fix it

Understanding Data Blending functions in Google Sheets 

After pulling your data from your favorite sources with Dataslayer, what you have to do next is to work on your data. Here are some of the powerful functions to help you in Google Sheets:

VLOOKUP

Scouring for information from a large data set can be daunting. Thankfully, the Google Sheets VLOOKUP function is available to help you look for your data. This function is particularly useful when dealing with multiple data sources — it is more effective no matter the number of lines in your data.

VLOOKUP is a vertical lookup function, meaning it looks up for specific values in vertical columns. To utilize this function efficiently, use columns as your fields. Additionally, VLOOKUP is for exact matches only, so don’t think about using it for approximates. 

The syntax for VLOOKUP is:

VLOOKUP(search_key, range, index, [is_sort])

Here, the searck_key is the value that you want to search for. The range is where you want to search it from, and the index is the column index of the value that will be returned. Then, the last parameter is if you want the exact match (set to false or ‘0’) or a loose match (set to true or omit).

• Consejo Profesional: Use absolute reference to search for the value and drag the formula across other cells to reuse the function.

Here’s the best article to help you learn How to use VLOOKUP.

INDEX AND MATCH

Utiliza el MATCH function to find cells with approximate values, and the INDEX function to get the value of a cell with a specified index. Therefore, you can both to give you better results where the VLOOKUP could not effectively look up for approximate values.

The syntax for INDEX is:

INDEX(reference, [row], [column])

The syntax for MATCH is:

MATCH(search_key, range, [search_type])

Syntax for using both INDEX and MATCH is:

INDEX(reference, MATCH(search_key, range, [search_type]), [column])

Here’s the best article to help you learn How to use INDEX and MATCH 

Macros

Automation is king nowadays. Macros can help you define custom functions to help you automate your repetitive reporting actions. This functionality will save you hours of doing repetitive tasks, especially on larger, everyday tasks. You’ll save time and energy while minimizing mistakes when working on your data.

Setting up macros is easy; you can write them or record your actions to get more control and flexibility in your data analysis.

If you need a comprehensive guide to setting up macros in Google Sheets, check out this article: Google Sheets Macros. 

• Tip: After blending your data on Google Sheets, you can now connect it to Google Data Studio for reporting.

Next steps

Data blending is an advanced topic. While it is a challenging concept, we hope this article has been resourceful for you. There are countless ways you can use the blended data for your reporting. Hopefully, Google Data Studio’s limitations for data blending did not stop you from getting the most out of it.

We have also shared some tips and a few Google Sheets functions to help you overcome those limitations of data blending in Google Data Studio. 

The list of functions that we shared is, however, not exhaustive — but they’re the core functions that can meet most of your data blending needs. So be sure to keep growing your skills for handling your marketing data creatively.

Also, check out how you can use Dataslayer to help you pull all your marketing data from different sources to your favorite go-to reporting platforms, be it Google Sheets, BigQuery, or Google Data Studio.