![]() ![]() Senior management can make data-driven decisions faster using the data blending tools. Then they can easily view the products that attract most customers purchasing interest besides the ones that make the most money.ĭata-Driven Decision Making. For example, marketers can blend data from a spreadsheet with the profitability of a product and CRM software. Non-technical users can get rapid results in key areas such as finance, sales, and marketing. In other words, in a LEFT (OUTER) JOIN of Table 1 and Table 2, the result is all the records of Data Source 1 and those records in Data Source 2 that share the same key values. It is also important to note, that Google Data Studio supports only the LEFT (OUTER) JOIN. You can read more about them in our article on Advanced Data Blending in Google Data Studio. UPDATE: As of February 17, 2022, Google Data Studio has added the remaining data blending o ptions. The first thing you need to do is form a hypothesis to help you match the sources. However, choosing the join key depends on the data that you want to compare. Besides, date helps you to spot correlations in datasets easily. The date is the most used join key because it’s very simple to compare two things over time. For example, it can be a user id, product name, or page URL, among many others. Your data sources need to share a common aspect. The keys are super important when it comes to data blending. ![]() Then modify the remaining data, so it’s properly optimized and formatted for an accurate analysis. You can clean and refine your data by removing incorrect and incomplete information. Here is where you will use the data blending functionality in Google Data Studio. Joining DataĪfter getting all the necessary data, now it’s time to join it together. ![]() You need to collect data from various cloud and non-cloud databases, social media tracking apps, cloud, and spreadsheets. The first step in data blending is data collection. Whether you are using Google Data Studio or another tool, the core steps for data blending are mostly the same. Every time they needed to compare newer data, they had to start the process all over again. In the past (a painful experience for the analysts out there), individuals and businesses that needed to compare data from different sources had to export each raw file from the respective source platform and then combine it in Google Sheets or Excel. Data blending tools help unify data from web analytics, spreadsheets, cloud applications, and business systems, among others. Large agencies and other businesses get data from many sources, and sometimes they want to bring this data together temporarily to answer a specific question or compare data relationships. What is Data Blending?ĭata blending is the process of merging data from multiple sources to create one new dataset that can be processed, analyzed, or presented in a visualization tool such as Google Data Studio. When Data Studio first became available, data blending was not its strongest suit, but things have changed for the better. In this post, we will look at data blending in Google Data Studio (the 2021 Edition) and see how the functionality has evolved over the years. I know that there are a lot of articles out there covering data joining and blending, but I wanted to create something that is relevant specifically for Google Data Studio and for 2021. For example, you can blend two different Google Analytics 4 (GA4) properties to measure the performance of your app and website in a single visualization. However, as part of Data Studio’s data blending capabilities, you can create charts based on multiple data sources. By default, all charts in Google Data Studio are connected to a single data source. ![]()
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