Bronson Shonk

Living in Seattle for the past two years, I have been a part of countless conversations about how rainy it is here. I often hear people make comparisons to New York, claiming that “while it feels like it rains a lot here, it actually rains more in New York.” Then why the heck does it seem so rainy here all year long? Plus New Yorkers surely do not complain about the rain as much as Seattleites do! To get to the bottom of the issue, I turned to the data.

Back in October, I shared the Tableau challenge workbook with fellow data explorers at the Tableau Conference, and many people asked me, “How can I make something similar?” So I thought I’d walk you through the steps. There are three major components to building a Tableau application: data, framework, and formatting.

I joined Tableau in September 2013 as a product consultant. We PCs are Tableau experts. We help with all things product-related, from technical sales to internal support and training, to content development. Basically, if you have a problem in Tableau, you get help from a PC. Every new PC starts working through the challenge workbook on the first day. The goal is to become an expert before ever getting on a customer call. This works so well for us internally that I thought it would only be fair to share with our community!

I am a huge fan of doing things the right way instead of using workarounds or shortcuts. Tableau is an incredible tool for solving problems, and using the functionality within Tableau, I have rarely found a problem unsolvable. That being said, sometimes it just makes more sense to make the data easier to work with before bringing it into Tableau.

Databases are populated with many large tables of tabular data, making them problematic to conceptualize. The ability to see and understand the data within a database easily is crucial to beginning any analysis. It turns out there are many database administrative tools that map out data models, but mapping hundreds of tables in a database tends to be a mess. Plus they are generally not available to the end users.

For many years, data users, whether they knew it or not, have struggled with database cardinality issues. I am excited to share a simple solution to this challenge.

Continue discussing Level of Detail (LOD) Expressions. Since it is often easiest to learn through examples, we will showcase some common analytical questions that LOD Expressions solve elegantly. We will start with simple examples and get progressively more complex throughout the series.