Latest Updates on Google Data Analytics (May 2022)

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, Data 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.

If you want to take a closer look, here you can find the Release Notes from BigQuery, Data Studio, Google Analytics & Google Tag Manager.

BigQuery

Collate Function

BigQuery got a new function which is in some special cases quite helpful. The new function is called COLLATE and orders your strings using different conditions. Let us take a look at a simple example, at first without the COLLATE function:

 
SELECT *
FROM UNNEST([ 
'x',    
'Y',   
'z'
]) AS character
ORDER BY character

The result here is the order Y, x, z. So at first it handles the Upper case letters and then the Lower case letters. The corresponding example with the collate function is:

​

 

SELECT *
FROM UNNEST([    
COLLATE('x', 'und:ci'),
'Y',   
'z' 
]) AS character
ORDER BY character

 

Here is the result x, Y, z. So it ignores the case of the characters and just sorts them alphabetically.

Other important facts are:

• The function can be used in Strings, Strings in a Struct and Strings in an Array
• The current only available specification for this is “und:ci” (ordering case insensitive)
• Like in the example it is enough to set the COLLATE specification on one element to order the strings in the whole column
• You can also set a default specification on a table

You can find further information here.

Column-level data masking

BigQuery still improves the whole data privacy and security. The column-level access control is already available, which means that some users have no access to the chosen columns. One problem is, that if the user gets a SQL-Query with a column the user has no access to, the Query fails during the execution.

The new column-level data masking offers another approach. The chosen masked columns for the user are still available but the real data is not shown. This has the big advantage that the user has still access to the full structure of the table.

The following values are shown for the most important data types in the masked columns:

• INTEGER: 0
• FLOAT: 0.0
• STRING: “”
• TIMESTAMP: 0001-01-01 00:00:00 UTC
• ARRAY: []
• JSON: {}

STRUCT columns are a bit special because it is not possible to mask a full struct column, just the leaf fields are possible.

You can find implementation advice here.

New Date Transformations

BigQuery already had a lot of possibilities to transform your dates into the desired format. They added one new format to get the day of the year based on the ISO-8601 format. The needed character is %J.

Additionally, the functions PARSE_DATE, PARSE_TIME, PARSE_DATETIME and PARSE_TIMESTAMP are supporting some new format elements. You can get more insights here.

Data Studio

Optional Metrics in Pivot tables

I like these optional metric functionality more and more. It is a good way to give the analysts the possibility to get more detailed insights. But persons who are not interested in these metrics, do not have to open them.

A lot of chart types have these optional metrics already. Google also added this functionality to pivot tables now. It has the same handling as the optional metrics for the table for example.

Improved Data Freshness for some connectors

On 10th February 2022 Data Studio published a new version, where it is possible to set the data freshness of your BigQuery data down to 1 minute. That was a big game-changer for us because it is possible to show your BigQuery data in Near-Realtime. We already put it in a former blog post here.

This improvement is also available for other connectors now. The connectors are:

• MySQL
• PostgreSQL
• MS SQL Server
• Amazon Redshift
• CloudSQL for MySQL
• Google Cloud Storage
• Cloud Spanner

Google Analytics

Lookback in audiences

It is possible to define audiences based on an event count of an event within a selected timeframe. You can create audiences under Configuration > Audiences. An easy example would be:

• User who purchased something on the website in the last 30 days

The Event Name is “purchase” and when the event count is greater than 0 in the last 30 days, the user is part of the audience. For sure it is a rolling window, so every day there are users who are out of the window and the customers from yesterday are part of the window.

New Funnel Report API

Google also still improves the new Google Analytics Data API. With the function property.funnelreport you can create a report which visualizes the quite new Funnel Report.

The documentation you can find here.

Google Tag Manager

No further release for the Google Tag Manager.

Upcoming datadice blog posts for this month

• Data Modeling in Y42 (Part 2)  - Coming Soon
• Google Analytics 4 Intro  - Coming Soon

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