How to Get Data from Google Analytics to Power BI

Bringing your Google Analytics data into Power BI opens up a universe of custom reporting possibilities far beyond the standard GA dashboards. This setup allows you to blend your website data with other business metrics for a complete performance picture. This guide walks you through connecting the two platforms, highlighting the best methods and a few common pitfalls to avoid.

Why Connect Google Analytics to Power BI?

While Google Analytics has a powerful interface, it tends to keep web data in a silo. When you bring that data into a flexible Business Intelligence tool like Power BI, you gain some huge advantages:

• Combine Data Sources: This is the biggest win. You can finally visualize your web performance alongside other critical data. Imagine a dashboard that shows Google Ads cost, Google Analytics sessions, Salesforce leads, and Shopify revenue all in one place. This allows you to build a full-funnel view of your marketing and sales performance.
• Advanced Calculations with DAX: Power BI’s formula language, Data Analysis Expressions (DAX), lets you create sophisticated custom metrics. You could calculate things like Cost Per Acquisition for specific campaigns, lead conversion rates from different traffic sources, or customer lifetime value based on their initial acquisition channel — calculations that are difficult or impossible within Google Analytics alone.
• Unlimited Custom Visualizations: You are no longer limited to the charts and graphs available in GA. Power BI offers a huge library of standard visuals, plus a marketplace for custom ones. You can build perfectly tailored reports that communicate insights exactly the way you want.
• Overcome Data Sampling: For accounts with high traffic, Google Analytics often uses "sampled" data to generate reports quickly. This means the numbers aren't 100% accurate, as they're based on a subset of your data. The way you connect to Power BI can help you access complete, unsampled data for maximum accuracy.
• Automated Data Refreshes: Once your report is built in Power BI, you can publish it to the Power BI Service and schedule automatic data refreshes. Your dashboards will always be up-to-date without you ever having to manually download a CSV file again.

Getting Started: What You'll Need

Before you jump in, make sure you have a few things ready to go:

• Power BI Desktop: This is the free application from Microsoft where you will build your reports. You can download it directly from the Microsoft Store on your Windows PC.
• Google Analytics Access: You need at least "Reader" permissions to the Google Analytics property you want to connect to. "Editor" access is ideal, as it ensures you won't run into any permission issues.
• A Google Account: You'll use this to sign into Google Analytics through Power BI.

Connecting Google Analytics to Power BI: The Native Connector Method

Power BI has a built-in connector for Google Analytics, making the initial connection straightforward. This is the fastest way to get your GA data into the platform.

Step 1: Open Power BI and Select 'Get Data'

Launch Power BI Desktop. On the "Home" tab of the ribbon at the top, click on Get Data. From the dropdown menu, select More... to open the full list of available data connectors.

Step 2: Find and Select the Google Analytics Connector

In the Get Data window, you can either scroll down to the "Online Services" category or use the search bar to type in "Google Analytics." You'll see connectors for both Universal Analytics (UA) and Google Analytics 4. Choose the one that corresponds to the version you are using and click Connect.

Step 3: Sign In to Your Google Account

Power BI will prompt you to sign in to your Google Account. A pop-up window will appear, asking you to choose the Google account associated with the Google Analytics property you want to access. After selecting your account, you'll be asked to grant Power BI permission to view your Google Analytics data. Click Allow.

Once you’ve successfully connected, click the Connect button back in the Power BI pop-up window.

Step 4: Navigate Your GA Data in the Navigator Pane

This is where many users feel a bit overwhelmed, but it's simpler than it looks. Power BI will show a 'Navigator' pane with all the Google Analytics accounts and properties you have access to. Expand the account, property, and view you wish to use.

You’ll see a list of folders containing dozens of reporting options. These are your dimensions (the "what," like Country, Page Title, or Source) and metrics (the numbers, like Sessions, Users, or Bounce Rate). To select data, find the dimensions and metrics you want in your report and check the box next to them. For example, to see website sessions by country, you would look for and select Country from a dimension folder (like 'Geo Network') and Sessions from a metric folder (like 'Session').

Step 5: Select Your Dimensions & Metrics

A good starting point is to rebuild a basic GA report. Let's try to get data to analyze 'Sessions by Source / Medium'.

1. Navigate through the folders and check the box next to Source / Medium (a dimension).
2. Check the box for Date (a dimension, so you can see trends over time).
3. Check the box for Sessions (a metric).
4. Check the box for Users (a metric).
5. Check the box for New Users (a metric).

As you check the boxes, a preview of your data table will appear on the right. You can select multiple items to pull them all into a single table.

Step 6: Load or Transform Data

Once you are happy with your selections, you have two options at the bottom of the window:

• Load: This will directly load the selected data into your Power BI data model, ready for you to start building visuals. For simple data pulls, this is fine.
• Transform Data: This opens the Power Query Editor. This is a powerful tool for cleaning and preparing your data before it gets loaded. You can change data types, split columns, filter rows, and perform hundreds of other transformations. It’s a best practice to at least review your data here before loading it.

For now, click Load to import your data into the report canvas.

Limitations of the Direct Connector

The native connector is fast and easy, but it comes with some significant limitations you need to be aware of.

• API Mismatches and Errors: A huge frustration for many users is the "The combination of dimensions and metrics is not supported" error. Google Analytics doesn't allow just any dimension to be queried with any metric due to how it processes data (user-level vs. session-level scope). The Power BI connector isn't great at helping you avoid these invalid combinations. You often find out a combo doesn't work only after it fails to load.
• Data Sampling: If you are requesting data over a long date range or from a GA property with very high traffic, the Google Analytics API may return sampled data to speed up the response. This is a big problem if you need precise numbers. Power BI doesn't explicitly tell you if the returned data is sampled, which can lead to unknowingly reporting on inaccurate information.
• Slow Refreshes on Large Datasets: If you pull many dimensions and metrics at once or are querying a large amount of data, the data refresh process in Power BI can be extremely slow. Each refresh is a new request to the GA API, which has its own rate limits.

Alternative Method: Connect Via Google BigQuery

For Google Analytics 4 users, there is a much more robust and scalable method: using Google BigQuery as a data warehouse.

GA4 offers a free, native integration that continuously exports your raw, unsampled event data into Google BigQuery. This approach solves all the major problems of the direct connector.

Why Use BigQuery?

• No Data Sampling: You get access to 100% of your raw event data. Your reporting will be completely accurate.
• Data Ownership and Flexibility: The data sits in your own BigQuery project, giving you full control. You can query it with SQL, blend it with any other data, and build a truly resilient data pipeline.
• Speed: Connecting Power BI to BigQuery is incredibly fast. Power BI is optimized to work with database sources, so refreshes are much quicker than hitting the GA API directly.

How It Works (High-Level)

1. Link GA4 to BigQuery: Inside your Google Analytics 4 property settings, go to Admin → Product Links → BigQuery Links. Follow the steps to connect to your Google Cloud project. It's a free and straightforward setup.
2. Connect Power BI to BigQuery: In Power BI's "Get Data" menu, search for the "Google BigQuery" connector. Sign into your Google Cloud account, select your project and dataset, and then you can either load an entire table or write a custom SQL query to pull in exactly the data you need pre-aggregated.

While this method involves an extra step, it is the industry-standard best practice for serious analysis and reporting with GA4 data.

Building a Simple Report

Once your Google Analytics data is loaded into Power BI, creating your first visual is easy. From our earlier example, we loaded 'Source/Medium', 'Date', and 'Sessions'.

Let's create a line chart showing daily sessions.

1. In the 'Visualizations' pane on the right, click the 'Line chart' icon.
2. An empty chart outline will appear on your report canvas. Click to select it.
3. From the 'Data' pane (far right), find your query. Drag the Date field into the 'X-axis' field of the visualization settings.
4. Drag the Sessions field into the 'Y-axis' field.

Power BI will instantly generate a line chart showing your session trends over time. You just built a report that automatically updates and can be customized with dozens of formatting options! From here, you can add more visuals, like a table showing sessions by source/medium, or a pie chart breaking down users by country.

Final Thoughts

You now know how to connect Google Analytics to Power BI, which is a critical skill for creating comprehensive and automated business dashboards. The direct connector is great for getting started quickly with smaller datasets, but for more scalable and accurate reporting, exporting your GA4 data to BigQuery first is definitely the better long-term solution.

The time you spend connecting tools, navigating API limits, and manually building reports is exactly the type of friction we created Graphed to eliminate. We provide one-click integrations with your data sources, including Google Analytics, so you can skip the complex setup found in traditional BI tools. Instead of wrestling with connectors and Report Builders, you can simply use natural language to ask for what you need - like, "create a dashboard showing GA sessions by channel for the last 90 days" - and our AI builds it for you in seconds. You can start creating real-time, interactive dashboards today with your own data by signing up for Graphed for free.