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Thoughts on Learning Tableau

Knowing how one contemporary tool, programming language, or framework works makes it easier to pick up new ones.
Jul | 15 | 2019

Jul | 15 | 2019


Back in the late-1990s through the late-2000s, I spent a great deal of time writing reports for my clients. To be sure, the methods varied. One of my go-to applications was Crystal Reports, now part of SAP. In a way, the specific tool didn’t matter. As long as I knew the structure of the database tables,1 I could find what I wanted and return the results in the desired format.

I am keeping this in mind as I prepare to teach a dataviz course next semester for the first time. No, we don’t use Crystal Reports. It isn’t 1998. Instead, we use Tableau. Fortunately, the same old rule about reporting applications holds. At a high level, both Tableau and Crystal largely accomplish the same things and offer comparable functionality. Brass tacks: As long as you understand the data, you should be able to do what you want.

One Report, Many Options

For instance, I used to enjoy creating interactive Crystal reports for my clients. That is, rather than just spewing out the data in a predefined, static format, I would often allow users to select the way that they wanted to see the data. Examples included different cuts of the data and different grouping, sorting, and downloading options. Put differently, Crystal allowed report creators to add dynamic parameters.

I haven’t touched Crystal Reports in a decade, but Tableau lets you do the same thing. I inserted a string parameter winner name on my growing tennis data visualization. Here’s an animated gif showing it in action:

In case you’re wondering, you can do so much more with parameters.

Simon Says: Experience matters.

I’m no Tableau Zen Master, but I am certainly not terrible with the reporting application. As I’ve told my students many times before, knowing how one contemporary tool, programming language, or framework works makes it easier to learn new ones. And lifelong learning is here to stay.


What say you?


  1. Data dictionaries are beautiful things.


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