What’s new in Google Data Studio

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Data mixing may as well be a craze for the dance, as the capability is very popular among analytical solutions. Solution providers are refining their data import capabilities to provide analysts with more creative options for their data models.

Over the past few months, Google has refined Data Studio to provide better blending capability and more flexibility in combining data. In February, the Data Merge feature was expanded, enhancing the Data Source Selector updates made last fall. All of this makes the usability of data attached to the platform more intuitive.



Start with connectors to know where the data is coming from

Understanding the value of data fusion begins with appreciating connectors, a plug-in extension that enables solutions to share data.

Google has a library of connectors that users can select. Twenty-two connectors integrate with other Google cloud services, such as Google Ads, Firebase, Google Analytics, and Big Query. There are also connectors for other platforms such as Amazon Redshift and Microsoft SQL Server, both of which were released in beta this year. Third-party vendors like Supermetrics have developed connectors for other data platforms where an API is available. As a result, there are a wide variety of data sources available for import, ranging from Twitter ads to Reddit.

When signed in to a Data Studio account, you can select a connector and then choose tables through the connectors using the data merge option in the data picker.

You can view the added connectors in your Data Studio report. The view also shows who owns the connector and when it was last accessed.

google data studio connector

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Where joins can participate

The updated data source selector introduced more configuration options for joins. Joins are the connection of fields between tables. A join condition is one or more fields that are found in each table and are used to link the records of these tables together.

Join conditions are the type of join you would see for SQL, Python, or any other language query data. Originally only a left join was available. Now, the data merge function has five choices: a cross join, an inner join, and three outer join operators. The three outer joins include the previously available left outer join, a right outer join, and a full outer join. Each will match tables based on the left table, right table, and both tables, respectively.

Google refers to the resulting table that contains the combined fields of a data smoothing as smoothing in its support docs. Smoothing represents the output of a data join. By default, data merging is a left outer join arrangement in which the primary source, which is on the left, is added to a secondary source, which is on the right. Together they form a shuffled array of data.

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Reliable centralized platform

Another advantage of Google Data Studio is the simplicity of key tables.

Usually, in queries like SQL, a joint key is needed to be able to fetch data from separate tables. Google Data Studio does not require a key once data is imported through a connector.

A downside of Google Data Studio’s merge feature is that the non-query syntax is easily visible to verify a request. In SQL you can see how rows and columns join tables in query syntax. By ignoring the joins, the analysis can become difficult to inspect if the right data is gathered. It’s also hard to see how this data is treated as NULL or N/A once the tables are combined; many programming languages ​​and platforms treat missing data differently in a result. Many repositories such as data world will allow you to preview potential SQL syntax that represents a potential combination of data. Merging data can be complex when multiple tables and different combinations of fields are needed.

Despite some complexity, the latest data fusion features in Google Data Studio will help analysts quickly identify the best way to use data for reporting and decision making. It provides a reliable centralized platform for developing robust visualizations and analyzes from a variety of data.

Pierre DeBois is the founder of Zimana, a digital analytics consultancy for small businesses. It reviews data from web analytics and social media dashboard solutions and then provides recommendations and web development actions that improve marketing strategy and business profitability.

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