Google just introduced a new learning tool for analysts: a Google Analytics sample dataset for BigQuery. The Sample Dataset is meant to enhance analysts’ practical experience, by allowing them to analyze analytics data in BigQuery.
The new Google Analytics dataset is directly available through the BigQuerry interface.
According to Google’s latest release, the dataset will include data from the Google Merchandise Store, an e-commerce website that sells Google-branded merchandise.
Analysts will now be able to query typical Analytics 360 data, such as AdWords, Goals and Enhanced Ecommerce data.
Basically, for each for each Analytics view that is enabled for BigQuery integration, a dataset is added using the view ID as the name.
When it comes to Tables, within each dataset, a table is imported for each day of export. Daily tables have the format “ga_sessions_YYYYMMDD”. According to the Google BigQuery Export Schema, intraday data is imported every 8 hours. Intraday tables have the format “ga_sessions_intraday_YYYYMMDD”. During the same day, each import of intraday data overwrites the previous import in the same table.
When the daily import is complete, the intraday table from the previous day is deleted. For the first 8 hours of the current day, until the first intraday import, there is no intraday table. If an intraday-table write fails, then the previous day’s intraday table is preserved.
However, you must be aware that data for the current day will not be final until the daily import is complete. You might experience differences between intraday and daily data, according to active user sessions that cross the time boundary of last intraday import.
By using the Analytics 360 – BigQuery integration, analysts are able to get a hold on extracting transformational insights for their companies, by accessing session and hit level data and combining it with separate data sets.
How will the Google Analytics Sample Dataset for BigQuery help analysts?
In two words: self-learning. Analysts will now be able to understand more advanced concepts by doing actual hands-on analysis rather than reading tutorials.
Analysts and marketers will be able to use the sample dataset provided by the new integration to understand how granular information can be extracted from Analytics data in BigQuery.
Google also designed a cool guide to help analysts get answers to a number of essential questions, such as:
- What is the average number of transactions per purchaser?
- The percentage of stock sold per product?
- What is the average bounce rate per marketing channel segmented by purchasers?
- Which are the products purchased by customers who previously purchased a particular product?
- The average number of user interactions before a purchase?