Export to BigQuery asap if you set up Google Analytics 4

Export to BigQuery asap if you set up Google Analytics 4

As a Marketing Analytics Database System, Google’s BigQuery is ideal (DWH). Even though you’re not a tech-savvy marketer and your team lacks the authority to work with a DWH, just do your potential techie and your company a favor and allow the Export to BigQuery feature right now. From the date of setup onwards, the data will be exported, and you will begin gathering data. It’s too late now.

There are many sources regarding Big Query ML, which enable great information for digital marketing and software enthusiasts. As a digital marketing expert and software developer, I would like to share my insights on how to export BigQuery asap if you set up your Google Analytics 4.

Analytics raw data are exported from Google Analytics 360 into BigQuery. This premium feature is now open to anyone with the latest Google Analytics 4 property. Also, external data can be imported to supplement the data, and this opens up the possibility of creating your own machine learning models. Forecasting, regression, sorting, clustering, recommendation structures, and other models are all examples of models.

Using raw data allows you to break away from the constraints of the user interface:

  • This export saves raw data on a hit-by-hit basis. More questions can be answered with hit stage data than with GA UI.
  • You have more analysis options than the UI allows with free querying over hit results.
  • The raw data is not sampled in the same way as it is on the UI. Sampling isn’t always a negative thing. However, if you had high cardinality for certain dimensions, this might be a challenge.
  • All external data can be enriched and joined with the data.
  • The analytics web data can be processed using the entire Google Cloud Infrastructure Stack.
  • Connections to BigQuery, Tableau, PowerBI, and other well-known data reporting vendors are available.

Having raw data in BigQuery allows for more professionals to use custom Artificial Intelligence tools in Marketing:

  • SQL, which is common to most analysts, can be used to query BigQuery results.
  • BigQuery ML allows for simple AI using SQL syntax.
  • You will use AI to rate guests, predict conversions, and explore personalization opportunities for your service, among other things.

Short: A web analyst or a data scientist can have huge fun in BigQuery.

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An example Google Cloud Infrastructure for ML with BigQuery and BigQuery

How to set up automatic export to BigQuery

How do I set up an automated BigQuery export?
The BigQuery export is set up very quickly. Since this was previously included in Google Analytics 360, which currently costs about $ 150.000, I’m curious if Google will charge for it in the future. It is currently open. Don’t be concerned with BigQuery prices. Costs are tiny, even the smallest income for the company would be worthwhile. The Google BigQuery Price List is a good place to start.

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Navigate to Admin and BigQuery Linking

With this article, I wanted to show the benefits of Big Query with Google Analytics 4. By using standard SQL syntax, Google ML lets you to apply machine learning to Big Query data. Isn’t it cool?

If you want to learn further about Big Query, feel free to read other articles such as How to build a User-based Content Recommendation System for your Website with BigQuery (ML).

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