Latest Updates on Google Data Analytics (May 2023)
The highlights of the updates on BigQuery, Looker Studio, Google Analytics (GA) & Google Tag Manager (GTM). By Alexander Junke
In this blog post, I want to summarize the new releases from the Google tools, that we use daily in datadice. Therefore I want to give an overview of the new features of BigQuery, Looker Studio, Google Analytics and Google Tag Manager. Furthermore, I will focus on the releases that I consider to be the most important ones and I will also name some other changes that were made.
When you execute a query and look a the output table, you will see that you can change the sorting of this table. You can order by any column descending and ascending. Usually, you do not have to pay for this operation, but if the table gets bigger, BigQuery shows the info, on how much the sort operation will cost.
Partitioning and Clustering recommender
This one is a nice idea from Google. It is a new recommender that gives you hints for Partitioning and Clustering of your already created tables. At the top right corner of your BigQuery project, you should see a bulb and a number, which shows the number of recommendations.
I looked into the recommendations for some of our more complex projects and the recommendations could be greatly improved. E.g. For projects I know we can do a lot of improvements for partitioning of the tables, but I get just one clustering recommendation for one table.
Then in the details, you see the table name, the type of recommended option, and the target column.
In the end a nice idea, still no good suggestions, but I think it will get better over time.
Column description in views
A quick one. You can add a description to view columns now. It is possible in the CREATE VIEW or ALTER COLUMN statement or in the UI itself.
Release and Workflow Configuration
I will take these two new features because they are quite closely connected and it is the most important update this month.
With these configurations, you can set up a full schedule of automatic query executions in your project.
First, you have to create a Release Configuration.
You just have to choose the name of the release and which Git branch (“commitish”) you want to use for the release. The idea behind the release configuration is to differentiate between the environments e.g. staging and production. Further information you can find here.
In the next step, you can use a Workflow Configuration. There you can do the following configurations.
- Name of the Workflow Configuration
- Choose the Release Configuration you created before
- Set up the execution frequency with a cron schedule and the time zone (Default: UTC)
- Then you can determine which parts of your models you want to schedule. You can choose between a full update, just selected actions, or just selected tags.
Further information you can find here.
With this new feature, Dataform is able to build a self-sufficient data pipeline ecosystem with a lot of features. In the past, we used Airflow to build up these data pipelines. For all the new projects, we already used Dataform, but still in combination with Airflow, due to the lack of scheduling possibilities.
Now it is possible to get rid of Airflow and just use Dataform to transform and update your data regularly with Version Control, Dependency checks, DAGs, automatic checks and many more.
In the next days we will publish a more detailed blog post with guides how to setup workflow configurations and release configurations.
There is a new running calculation available. It calculates the running percentage delta of a metric, based on the sorting you set.
In the example, the revenue change from the product category Hoodie to T-Shirt is 41,78% and you can see it in the T-Shirt row.
The underlying calculation is (current value — previous value) / ABS(previous value)
This a feature that is good to have during the development and creation process of the dashboard. You can add once all the desired data to your dashboard and then pause the report updates. There will be no data requests until you resume the updating process and you can create the dashboard and save costs easily.
There was always a lack of funnel analysis in GA4 to see your user’s journey. Google catches up, by giving you the possibility, to build custom funnel reports and there is a default report available.
In the exploration menu is the “Funnel Exploration” available. You can configure the steps and the dimensions and metrics you want to show. The custom report looks similar like the new default “User purchase journey” report, just with different dimensions and metrics.
The “User purchase journey” can be found under Monetisation > User purchase journey. There you get some insights into your purchase funnel and at which checkout step the users drop off. The look and feel of the report is like the same report from Universal Analytics.
For sure you have to track this information on your website and send the data in the correct structure to your GA4 property.
Improvements in the audience builder
The audience builder in GA4 got some new features and you can build up your audiences more granularly.
A lot of new dimensions you can use to filter your users, e.g.:
- Item-scoped (Item Brand, Item name, Item variant, …)
- E-commerce metrics (Revenue, add to cart, …)
- item-list click events and low-engagement sessions
Next to the new dimensions, there are also new functions and operators available to build audiences
- You can use the event value itself to create conditions (The event value of event X needs to be over 60 …)
- For event count the operators >=, <= and != (Not Equal) are available
- For dates, the operator “Match between types” is available
Google Tag Manager
No further release for the Google Tag Manager.
Upcoming datadice blog posts for this month
This post is part of the Google Data Analytics series from datadice and explains to you every month the newest features in BigQuery, Data Studio, Google Analytics and Google Tag Manager.
We also started with our own YouTube channel. We talk about important DWH, BigQuery, Data Studio and many more topics. Check out the channel here.
If you want to learn more about how to use Google Data Studio and take it to the next level in combination with BigQuery, check our Udemy course here.
If you are looking for help to set up a modern and cost-efficient data warehouse or analytical dashboards, send us an email to firstname.lastname@example.org and we will schedule a call.