Navigating the Challenges of Google Data Studio for Advanced Users
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Chapter 1: Understanding Google Data Studio Limitations
In recent months, my reliance on Google Data Studio has grown significantly. While this tool offers numerous benefits and powerful features, it also presents certain challenges that advanced users should be aware of. Despite the abundance of tutorials focused on data visualization techniques, there is a noticeable lack of discussions surrounding the common obstacles users may face and how to navigate them.
Here, we will explore four critical limitations of Google Data Studio that should be considered before developing your reports.
Section 1.1: Data Blending with Left Outer Join
Blending data within Google Data Studio follows the SQL standard for left outer joins. This method incorporates all records from the primary data source and merges them with matching records from a secondary source.
In total, there are seven types of joins that data professionals commonly utilize. The restriction to just one type can be quite limiting for advanced users.
Implications of Left Outer Joins
In practical terms, it's essential to pay close attention to how data sources are blended and the sequence in which they are added. According to the left outer join definition, the first data source serves as the foundation, meaning that any additional entries from the second source that do not match the join key will be excluded from the resulting dataset. This presents a significant limitation, as it can hinder the ability to analyze patterns effectively due to the rejection of null values.
Alternatives for Data Handling
For those with access, Google BigQuery is a robust option for processing larger datasets. While it excels in data blending and joining, it does require setup and familiarity with SQL, along with costs for each query. If your infrastructure permits, it's advisable to utilize BigQuery for data management. Alternatively, for smaller datasets, Google Sheets can serve as an effective tool. Dave Mendl has authored an excellent guide on employing various functions like VLOOKUP and FILTER to replicate the seven types of SQL joins within Google Sheets.
Section 1.2: Limit on Blended Data Sources
Continuing with the theme of data joining, another frustrating limitation is the restriction of blending a maximum of five data sources, all of which must share a joining key.
Real-World Impact
While this may initially seem adequate, in practice, it can feel quite constraining. For instance, when visualizing custom data extractions from tools like Screaming Frog, the ability to create trend lines is limited to just five data points. Additionally, if you want to correlate various data sources, like tracking improvements on a client’s website, this limitation requires pre-blending before updates can be applied.
Overcoming the Limitations
A practical solution is to pre-blend data offline, utilizing tools like Google Sheets or Excel. Integration with automation tools like Zapier can also facilitate this process, although it may involve some complexities.
Chapter 2: Handling Data Source Challenges
Section 2.1: The Risk of Reports Breaking
Building reports in Google Data Studio often leads to the dreaded "kiss of death"—broken charts, unknown data sources, and various configuration errors. Unfortunately, Data Studio does not provide clear guidance when errors occur, potentially deterring users from revisiting reports.
Best Practices for Stability
To mitigate these issues, I recommend adhering to two key principles: simplification—removing unnecessary elements—and taking ownership of your data connections. By streamlining the content and consolidating dashboards, you can enhance user experience while maintaining report quality. Aim for a clear, stable overview that links to more detailed, dynamic data views.
Section 2.2: Connector Usability and Database Schema
With hundreds of connectors available for report integration, the quality of these connections often depends on their internal database structure.
Alternative Connection Methods
Instead of relying on connectors, consider integrating directly with APIs. This approach not only enhances data management and authentication but also allows for greater control over how your data is organized.
Conclusion: Embracing Google Data Studio
While these limitations may seem daunting, it’s important to note that Google Data Studio remains a powerful tool that I utilize daily for a variety of reporting tasks. From simple personal finance dashboards to complex SEO reports incorporating numerous data sources, I firmly believe in the value of data visualization in all aspects of life.
In summary, here are the four key limitations of Google Data Studio, along with effective strategies to address them:
- Data Blending uses left outer join
- Alternative: Conduct complex data manipulations in Google Sheets or BigQuery based on your dataset size and available infrastructure.
- A maximum of five data sources can be blended, all requiring a common join key.
- Alternative: Pre-blend data using offline tools and automation to streamline the process.
- Incorporating multiple data sources can lead to broken reports.
- Alternative: Simplify your reports and maintain control over your data sources.
- The usability of connectors relies heavily on their internal database schema.
- Alternative: Prefer API integration over connectors for better data management.
This guide aims to inform both novice and experienced users about the strengths and limitations of Google Data Studio, empowering you to maximize its potential.