As a seasoned user of Google Looker Studio (psst, I even did a course on Google Looker Studio for advanced users, check it out), I’ve extensively explored its data visualization features and capabilities over the past few months. As any sophisticated tool, Google Looker Studio has its own set of limitations and challenges.
In this comprehensive guide, I will delve the lesser-discussed pitfalls of the tool. More importantly, I provide practical insights and strategies on how to navigate these challenges effectively. Whether you’re a beginner or an advanced user, this guide aims to enhance your proficiency in using Google Looker Studio for creating impactful data visualizations and conducting in-depth analyses.
So, here are some of the limitations of Google Looker Studio (formerly Google Data Studio) that advanced users should take into account before building their reports.
Data blending limitations
While data blending is a powerful feature of Looker Studio, it also comes with some limitations that can impact the performance and reliability of your reports. Here are some of the key limitations to be aware of.
Limited Number of Blended Sources
You can only blend a maximum of five data sources within a single blended data source, which is one of the most frustrating limitations I have encountered. This means that if you need to combine data from more than five sources, you’ll need to use another data manipulation tool or consider aggregating the data into a single source beforehand.
While this may sound like a lot, trust me — it isn’t.
Looking at a couple of examples in the SEO realm. I use Looker Studio for visualizations 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.
Reduced Data Integrity Control
When you blend data sources, Looker Studio handles the underlying data manipulation and joins automatically. This can make it more difficult to verify the accuracy of the blended data, as you can’t directly inspect the raw data from each source.
This issue is not isolated to Looker Studio, either. If you look at community forums of other data visualization tools, like Tableau, for instance, you will find numerous posts about data blending causing data inaccuracies.
This can be detrimental to the use of these data visualization tools for business reporting, especially when business investment or planning decisions are made on the foundation of the data in your dashboard.
Potential Performance Issues
Blending data can increase the processing and loading times of your reports, especially as you add more blended sources. This can make your dashboards less responsive and could affect the user experience.
Limited Cross-Data Source Calculated Fields
You can only create chart-specific calculated fields within a blended data source. This means you can’t define calculated fields that apply to the entire data set, which can limit the flexibility of your analysis.
Limited Data Manipulation Abilities
Blending is primarily focused on combining data from different sources, but it lacks the ability to perform more complex data manipulation tasks, such as filtering, aggregating, or transforming data.
How to resolve Looker Studio blending challenges
To mitigate data blending limitations in Looker Studio, consider these strategies:
- Use Data Blending Strategically: Only blend data when it’s absolutely necessary and carefully evaluate whether it’s the best approach for your analysis.
- Avoid Overly Complex Blends: Keep blended data sets as concise and focused as possible to minimize the risk of performance issues and data integrity concerns.
- Consider Alternative Data Manipulation Tools: For more complex data transformation or manipulation tasks, consider using dedicated data preparation tools, like via AppScript in Google Sheets, or via LookML transformations. 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.
- Optimize Data Blending Workflows: Optimize your data blending processes by choosing the most efficient joining methods and minimizing the number of joins.
- Continuously Monitor and Audit Data Blends: Regularly monitor the performance and accuracy of your blended data sources to identify any potential issues.
- For highly important data points that require blends, double-check the calculations’ accuracy via an alternative method: This can enable you to have insight into potential issues with the data integrity before important decisions are made on the basis of wrong data.
Broken dashboards and chart errors
If you have ever built a Looker 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, Looker 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.
There is a great 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.
How to minimize errors in Looker Studio data visualization dashboards
Here are some tips on how to minimize errors in Looker Studio data visualization dashboards:
- Use the correct data source: Make sure you are using the correct data source for your dashboard. This will help to ensure that the data is accurate and up-to-date.
- Filter your data: Filter your data to only include the data that you need for your dashboard. This will help to reduce the amount of data that needs to be loaded and rendered, which can improve performance (and help you avoid quota issues). Proper planning of the data story in this step is crucial.
- Use appropriate visualizations: Choose visualizations that are appropriate for the type of data you are visualizing. Don’t just choose them cause they look pretty.
- Label your visualizations: Label your visualizations clearly so that users can easily understand what they are seeing. Make sure to use descriptive labels that are relevant to the data being visualized. Include commentary and annotations, whenever possible. These elements will make sure that even when the charts encounter loading errors, the page will still be useful for people accessing your report.
- Get ownership of the connection: Do everything in your power to manage the added data sources and the ways they connect. Whenever possible avoid over-reliance on third-party connectors
- Use Big Query to avoid quota errors in Google Search Console and for GA4: Avoid your most important data not being shown as a result of a quota issue, diminishing your integrity as a consultant in the process.
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.
Difficult to use or unreliable third-party connectors
There are hundreds of connectors you can use in your reports, as well as embedding your own data sources. Third-party connectors in Looker Studio play a crucial role in accessing and integrating data from various sources, enriching the data visualization capabilities of the platform. However, these connectors can sometimes present challenges due to their complexity, limited capabilities, or occasional unreliability. 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 our third-party tools in Looker Studio, and the problems they cause.
What I have noticed is that bulky, ugly-looking spreadsheets or a Big Query-hosted dataset make the best type of data sources. For instance, the Google Analytics (and other Google-owned) data source has one connection per property, which includes all data fields.
For comparison, Semrush’s connectors (and other digital marketing platform tool connectors like it) involve choices between three different menus, each connecting to a different segment of the Semrush data. What this results in is an improved likelihood of you later hitting that challenge with data blending mentioned earlier, having a report with a hundred data sources if you are reporting on an enterprise property or managing an agency portfolio of clients, and getting a literal headache when you connect the data sources, oh and later – when you try to copy the report.
On another note, third-party connectors may not be as reliable or consistent as native Looker Studio connectors, potentially leading to data errors, connectivity issues, or performance bottlenecks. This can disrupt data analysis and reporting efforts.
Another hindrance is that such connectors may have limited or inconsistent documentation or support, or even worse – suddenly change their pricing policies, making it difficult or downright impossible to resolve technical problems or address compatibility issues. This can lead to delays in resolving issues, or worse – requiring you to rework or recreate your entire Looker studio dashboard portfolio around new data sources.
How to resolve limitations, caused by third-party connectors
To address these challenges and optimize the use of third-party connectors in Looker Studio, consider the following strategies:
- Choose Reputable and Well-Maintained Connectors: Prioritize connectors from established providers with a proven track record of reliability and support. Check user reviews, community forums, and documentation to assess the quality and support level of potential connectors.
- Implement Data Quality Checks: Implement data quality checks within Looker Studio to identify and address any data inconsistencies or anomalies that may arise from the third-party connector. Regularly monitor data quality metrics to ensure data integrity.
- Provide alternative benchmarking sources, whenever possible: Make sure you provide alternatives to create benchmarks for potential data integrity issues caused by third-party data.
- Directly integrate via available APIs as opposed to using connectors: 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: Looker 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 income streams, to robust multi-page reports for SEO consultancy, as complex as using dozens 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 Looker Studio, its limitations, and how to overcome them.
