Posted by Eric
April 29, 2016
This is the first in a series of challenges for those that believe they have mastered the more complicated aspects of Tableau. The series will call on your understanding of the intricacies of how Tableau functionality works.
For example, did you know that you can perform merchandising affinity analysis using dynamic sets? Did you know that forecasting in Tableau uses the exponential smoothing technique, and do you know how that affects the results of the forecast? Did you know that when using non-additive computations or LOD expressions that reference a secondary data source you may need to have the linking field from the primary data source in the view?
If these questions are news to you, then you may be interested in trying out our Tableau Master Challenge series. We will explore some new facet of advanced Tableau functionality with each post. Even if you can’t solve the challenge, we will walk you through the solution and the logic of how and why the solution works.
Our first challenge involves filtering. Did you know that there are nine different kinds of filtering in Tableau? These filters all happen in a specific sequence and affect various functions and computations in your view. Now that we’ve defined the challenge’s topic, here is the challenge.
The data source for this challenge will be the Tableau Superstore Sales Excel data source. The solution we will provide is based on the 9.3 version of the Excel workbook, but if you have older versions they should also work.
Construct a single view that compares the average sub-category sales to the multi-year sub-category average sales and computes the percentage variance from average for each year. Only the top three sub-categories based on the annual average sales within each category should be displayed.
If you succeed in this challenge, your results should match the following given the same filter configuration for year and sub-category.
Keep track of our Tableau Master Challenge series and pit your Tableau skills against the industry best!
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