Google Data Studio (recently rebranded as Looker Studio) is a powerful tool for transforming raw data into clear, compelling, and shareable reports. However, even with all its power, the journey from raw metrics to actionable insight often hits snags, requiring savvy workarounds and adherence to best practices.
You’ve connected your data and built a dashboard, but critical analysis functions—like easily sorting metrics by the absolute or percentage change between time periods—are not available right out of the box. The risk of missing a key optimization feature is constant.
This guide provides essential Google Data Studio Best Practices and tips, starting with the necessary workaround for creating a sortable change column. By implementing these practices, you can move beyond basic visualization to create fast, reliable, and truly insightful reports that drive smart business decisions.
Read on to unlock the full potential of your reports and start telling your data’s story effectively today.
How to Sort the Difference and Percentage Change in Google Data Studio
Google Data Studio is a very powerful data reporting tool. Like many other tools, the default settings sometimes have their limitations simply because users have different needs. Like the authors of this post in SEER, we are struggling of trying ways to sort out the change or difference (either percentage or absolute value) between metrics. Fortunately, this article has been a great help: HOW TO BUILD SORTABLE CHANGE COLUMNS IN GOOGLE DATA STUDIO.
This sounds like a very basic feature, yet it is not right out of the box. Yes, it is very basic. yet absolutely very important. Having the ability to sort the changes (percentage) and the difference (absolute value) between metrics could easily show the best performing and the least performing metrics.
The solution requires blending your data source with itself and creating custom calculated fields for both the absolute difference and the percentage difference. Once these custom fields are established, they become sortable metrics within your table visualization, instantly highlighting top movers.
Best Practice: Prioritize Audience and Clarity
The most common mistake when building reports is making them an exact replica of the source platform (like a Google Analytics screenshot). Instead, successful reports must be short, simple, and tailored to the audience.
Define Your Audience and Goals
Before diving into design, identify the purpose of your report and who will be viewing it. Are you presenting to a CEO who needs high-level KPIs and business impact, or a marketer who needs tactical data?
Avoid technical jargon and ambiguous words. Present less fluff and more substance. If you must use a technical term, explain it clearly for those who know little to nothing about web analytics.
Simplify and Distribute
Avoid cluttering your dashboards with unnecessary metrics or visual elements. If your report is ten pages long, no one is going to read it, let alone take any action. Distribute related charts across multiple pages to reduce complexity and make the report easier to consume. Use plenty of white space to minimize visual fatigue.
Best Practice: Optimize Data Architecture for Performance
Looker Studio (formerly Google Data Studio) is primarily a visualization tool, not a data manipulation tool. The fastest reports are those that minimize the amount of complex calculation and blending that the platform has to perform live.
Pre-Aggregate and Clean Data
Avoid pulling data directly from a raw platform. Any necessary data manipulations, cleaning, or aggregation should be done before connecting to Looker Studio, typically in a data warehouse (like BigQuery) or an organized spreadsheet (like Google Sheets). Using functions and complex calculated fields within the report itself can significantly increase load time and lead to version control nightmares.
Use Extracted and Blended Data Sources
For large datasets, using an extracted data source creates a static, fast snapshot of the data, which dramatically improves report responsiveness. When blending data from multiple sources, ensure the blending process is done efficiently with a clean join key to maintain data accuracy and report speed.
Tool Recommendations Section
- Google Sheets: Essential for cleaning, manipulating, and pre-aggregating smaller data sets before connecting to Looker Studio, ensuring fast and reliable reports.
- BigQuery: Google’s cost-effective data warehouse, recommended for large-scale data manipulation and pre-aggregation to ensure the fastest possible performance in Looker Studio.
- Looker Studio Templates: Use the robust template library to speed up report creation. Simply connect your data and make minor cosmetic changes (like adding your company logo) to customize an existing, well-designed structure.
Conclusion: Transform Data into Decisions
Mastering the use of Google Data Studio requires more than just connecting a data source. It demands clarity in presentation, strategic pre-processing of data, and knowing the workarounds for its limitations, such as the ability to sort change columns. By following these best practices, you empower your audience to move instantly from viewing metrics to taking informed, decisive action.
Actionable Steps:
- Implement Sortable Change Columns: Research and implement the data blending technique to create custom, sortable difference and percentage change metrics in your main tables.
- Define a Single Key Goal: Review your most frequently used report and ensure it clearly addresses the single most important question for its primary audience. Remove all non-essential metrics.
- Pre-Process Calculations: Move complex calculations out of Looker Studio and into your data source (e.g., Google Sheets or BigQuery) to optimize report load times.
Stop just charting data—start telling your data story.
Frequently Asked Questions (FAQs)
Why was Google Data Studio renamed to Looker Studio?
Google rebranded Data Studio to Looker Studio in late 2022 as part of its unification of all its business intelligence (BI) products under the Looker name. The core functionality and purpose of the free tool remain the same.
Is it better to use calculated fields in Looker Studio or in the source data?
It is generally a best practice to create complex calculations and transformations in the source data (e.g., in BigQuery or Google Sheets). This reduces the computational load on Looker Studio, resulting in faster report load times and a more consistent, reusable metric across multiple reports.
What is the most important element for a non-technical audience?
For a non-technical audience (like managers or CEOs), the most important element is the Scorecard (KPI summary) and the Callouts (text boxes explaining key takeaways). The report should clearly present the bottom-line impacting insights, preferably in a few lines of plain English.
How often should I refresh my data in Looker Studio?
Data refresh frequency should match the audience’s decision-making cadence. Executive dashboards reviewed weekly don’t need second-by-second updates—daily or hourly refreshes often suffice. Balancing data freshness against report performance and potential query costs is key.
What is data blending and when should I use it?
Data blending is the process of combining data from two or more data sources (like Google Analytics and Google Ads) into a single chart or table within Looker Studio. You should use it whenever you need to see a combined view of metrics from different platforms, suchs as calculating total campaign ROI.