Google Analytics UI data is the data that is displayed in the Google Analytics user interface (UI). This data has been processed by Google Analytics to remove duplicates, fill in missing values, and apply other transformations.
(Google) BigQuery export data is the raw data that is collected by Google Analytics. This data is not processed in any way, and it includes all of the details about each user interaction. This allows you to run custom queries and analyses on the data, which can provide you with more granular insights into your website traffic and user behavior.
Differences between Google Analytics UI and BigQuery Export data
| Google Analytics UI data | BigQuery export data |
| Data type | Processed* | Raw |
| Granularity | Lower | Higher |
| Flexibility | Lower | Higher |
| Cost | None | Depends on usage |
*Google Signals, modelling, traffic attribution, and prediction applied to it
You should use Google Analytics UI data in standard reports, Explorations, and the Data API for most reporting and analysis. It is a point-and-click operation.
Use BigQuery Export data if you need more granular insights or want to run custom queries together with CRM data. Knowledge of sql queries is the way to work in BigQuery. With the appropriate algorithm applied, it can predict user churn for your app or measure your website's performance.
BigQuery Export vs Google Analytics UI
The Google Analytics BigQuery Export is a feature that allows users to export their Google Analytics data to BigQuery, a fully-fledged data warehouse. This can be useful for users who want to perform more advanced data analysis on their Google Analytics data.
In "Bridging the gap between Google Analytics UI and BigQuery export", Minhaz Kazi shared why the numbers in the BigQuery export data and the UI data are not intended to be matched. These reasons include:
BigQuery Export data does not include data for users who have opted out of tracking.
BigQuery Export data does not include data for users who have been blocked by the browser.
BigQuery Export data does not include data for users who have been anonymized.
BigQuery event export data may be delayed by a few hours or days.
Things to keep in mind while querying GA data in BigQuery
Information subject to thresholding usually is not available in the BigQuery export.
If you are trying to match your BigQuery export numbers with an Exploration report, ensure that the exploration report is not sampled.
No Google Signals information is available in the BigQuery export. Reports with Google Signals data will most likely have less user count compared to BigQuery export.
GA4 backfills data collection gaps with conversion modeling and behavioral modeling. None of these data is available in the BigQuery event export.
Google Analytics will group less frequent values and label them as (other) because of high cardinality dimensions.
Comparisons should be made on data older than 72 hours.
Advantages of Using BigQuery
There are several advantages to using BigQuery for Google Analytics data. These advantages include:
BigQuery is a fully-fledged data warehouse, so you can store data from multiple sources, including Google Analytics, CRM, commerce platform, Display & Video 360, Google Ads, third-party advertising platforms, Facebook, YouTube, and more.
BigQuery is much more scalable than GA4, so you can easily store and analyze large amounts of data.
BigQuery offers a wide range of features for data analysis, including machine learning, SQL, and visualization tools.
Conclusion
The standard reporting surfaces and the BigQuery Export data aren't expected to be reconcilable.
Google Analytics adds value to the collected data before it reaches the 'Reporting surfaces'. This value addition can include Google Signals, modeling, traffic attribution, and prediction. It provides maximum value to GA users with the least amount of friction. It is good for most reporting and analysis.
BigQuery Export provides more granular data than the Google Analytics UI because it is the raw data that has not been processed. This allows users to run custom queries and analyses on the data to gain deeper insights into their website traffic and user behavior.
