Google Analytics 360 to Power BI

This page provides you with instructions on how to extract data from Google Analytics 360 and analyze it in Power BI. (If the mechanics of extracting data from Google Analytics 360 seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Google Analytics 360?

Google Analytics 360 is an enterprise-level marketing analytics tool for large companies. It's one of a suite of six Google products designed to help marketers get a holistic view of their online marketing efforts. The software was formerly called Google Analytics Premium.

What is Power BI?

Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.

With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.

Getting data out of Google Analytics 360

Google Analytics 360 stores data in a Google BigQuery data warehouse. If your analytics stack is also based on BigQuery, integrating Google Analytics 360 data with the rest of your data is a matter of writing SQL queries. If you use a different data warehouse, however, you need to export the data. That means setting an account up with the required permissions, then figuring out what datasets and columns you want to export. You can't export to a local destination — the export file must be stored in Google Cloud Storage. In addition, Google imposes certain export limitations you have to be aware of.

Preparing Google Analytics 360 data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Power BI

You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.

Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.

Analyzing data in Power BI

In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.

The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.

Once you've created a report, Power BI lets you share it with report "consumers" in your organization.

Keeping Google Analytics 360 data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, most data sources include fields like id or created_at that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

From Google Analytics 360 to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Google Analytics 360 data in Power BI is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Google Analytics 360 to Redshift, Google Analytics 360 to BigQuery, Google Analytics 360 to Azure Synapse Analytics, Google Analytics 360 to PostgreSQL, Google Analytics 360 to Panoply, and Google Analytics 360 to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Google Analytics 360 with Power BI. With just a few clicks, Stitch starts extracting your Google Analytics 360 data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Power BI.