Worst of all-analysts had to take extra steps to view combined metrics across accounts, properties, and platforms to make optimization decisions. Often, these third-party applications also cost extra to connect and blend the data for you. Ultimately, data blending allows for holistic reporting on performance.īefore data blending in Google Data Studio, we were forced to blend these metrics through other applications first before connecting it to Google Data Studio. You’re able to provide performance for an overall budget that’s spread across more than one source or medium (ex. app conversions in Firebase and website conversions in Google Analytics). Clients and analysts should be looking at combined performance metrics for efforts that have the same goals or budgets, regardless of how the data collection is disjointed.īlending data in Google Data Studio allows you to combine metrics that are reported on in different platforms that should be viewed as a total (ex.
Most often than not, I’ve run into clients who have multiple Google Analytics properties or Google Ads accounts, are running Paid Social ads in multiple social platforms, or offer their service or product within a website and across applications. Why Use Data Blending in Google Data Studio?įor me, this is undeniably the most useful feature of Google Data Studio. GDS also recently came out with the ability to very easily create a Data Blend in a report by simply clicking multiple scorecards or tables at once, right-clicking, and selecting ‘Blend Data.’ This method creates the Data Blend for you with the selected metrics and sets the first data source you selected as your left-most source. You’re able to blend data sources to combine metrics, like revenue and transactions, across multiple properties by dimensions like campaigns, keywords, and device types. It can be used to create any type of visualization offered in Google Data Studio for a complete view of performance across different sources. Introduction to Data Blending in Google Data Studioĭata blending is a feature that was released in Google Data Studio in 2018 that allows you to combine metrics from multiple sources. In this blog post, we’ll introduce you to exactly how data blending works in Google Data Studio, note the considerations that limit our blending abilities, and share some tactics we’ve successfully implemented to resolve data blending issues. You’ve surely relished in its powerful capabilities to combine and calculate metrics across data sources with a simple Join Key-and therefore, you’ve undoubtedly encountered its caveats and downfalls, as a product that’s still ever-changing.
A guide to blending data sources in Google Data Studio and tips I’ve learned along the way to resolve common issues.Īs a Google Data Studio (GDS) user, you’ve most likely already delved into the data blending feature.