It has been a while since I have been hearing a lot of things about Alteryx especially as a great ETL tool for Tableau since Tableau lacks the ability to change the shape of the data (except for the few limited features like unpivoting and union that were added, if my memory is right, in the last 1 year). Having the data in the right shape (or as we call it, having the right data model) makes working in Tableau frictionless and productive. When encountering a tricky reporting requirement, especially one that does not seem easy to build on Tableau, most newbies to Tableau try to write complex calculations or resort to some hacks to solve the problems. Whereas people who are masters in the craft, like Joe Mako, often talk about changing the data model to make working in Tableau easier. Every real-life project that we work on always has a few data model issues that makes data cleanup/data preparation necessary before bringing the data into Tableau. So, we have been on the lookout for good ETL tools that would fit the budget for our clients. But I have ignored Alteryx for a number of reasons which I will explain below and now I have decided to take Alteryx for a test drive now, primarily inspired by 2 people – Joe Mako and Ken Black. In this series, my intent is to capture my entire journey in learning and evaluating Alteryx as a tool for data preparation and data cleansing.