The WEEK function in Google Data Studio is an excellent tool for analyzing and presenting data according to weekly cycles. It allows users to efficiently break down a date parameter on the basis of weeks to better measure and compare growth, performance, sales and other metrics.
Isolating the week can provide invaluable insights in trends that may not be apparent in daily or monthly data. For instance, it could help uncover patterns in a retail setting whereby certain products do better in specific weeks of the year.
With two primary ways to use the WEEK function, users have flexibility based on data type: semantic date field or compatibility mode date.
Semantic date field:
WEEK(date_expression)Compatibility mode date:
WEEK(X [, Input Format])Where: -
date_expressionXXThe WEEK function's primary role is returning the week number for a given date.
In the case of a semantic date field, you only need to input
date_expressionWEEK(date_expression)On the other hand, for a compatibility mode Date value, the syntax
WEEK(X [, Input Format])XConsider an electronics store that wants to analyze its weekly sales data. The store records the sales date for each transaction. They could use the WEEK function like so:
With semantic date field:
WEEK(Sales_Date)If Sales_Date == '2019-01-01', the output will be '1'
With compatibility mode date:
WEEK(Sales_Date, 'DEFAULT_DASH')If Sales_Date == '2019/01/01-09:40:45', the output will be '1'
These examples demonstrate how the WEEK function can be used to identify the week of the year a sale was made, helping to group sales data for weekly performance analysis.
While useful, the WEEK function has its constraints. It operates per the ISO 8601 standard, which may differ from local calendar's week number, especially for dates close to the start and end of the year.
Also, the function won't directly associate data with day of the week (Monday, Tuesday, etc.), which is sometimes a more useful metric when analyzing trends and patterns.
With the power to dissect data week by week, you are better equipped to track patterns and make data-informed decisions for improved business performance.
