It is now Day 3 of the dashboard week!


The data for today is about Star Trek. There have been so many pieces of media for this IP. TV, movies, books, and comics written and developed over the last 50 years. Our aim is to figure out a way to keep track of everything.

We learned from yesterday to not waste time during the morning presentation, and we successfully saved more than half an hour time! We finished the presentation by 10:00am.

Step 1 (Morning 10:00am – 11:30am)

After having the data, we looked at it. There are 10 CSV files in total, and we cannot join them since some are books, some are movies, some are TV series, etc. I started by putting them into Alteryx to look at the data. The data structure is pretty straightforward, but the challenge today is to think about the purpose of the dashboard, what I want to achieve or show, and what functionality I need to provide.

It is around 1 hour of brainstorming to figure out what exactly I want to make.

Step 2 (Morning 11:30am – 12:30pm)

I started cleaning the data and also adding columns to help me achieve my end goal. I also added a column for each of the files to identify the media type. After that, I unioned all the data for Tableau dashboarding!

Have a good lunch break and relax. Eyes will feel sore after too much screen time.

Step 3 (Afternoon 1:30pm – 2:00pm)

I discovered this step during the dashboard week and found it really important and helpful. That is, draw your dashboard first! I spent some time thinking about what I wanted to put on the dashboard and where to put them. It also serves as a great reference when I start putting tiled containers on the dashboard. It is a simple, but very powerful step.

Step 4 (Afternoon 2:00pm – 5:00pm)

This is the time to put things together, make the charts, and format! Always always always allocate enough time for formatting! I finally managed to complete the dashboard before 5:00pm, leaving me only a blog to write after work. But since I am writing the blog while I go during the day. It becomes a quick and easy job.




The Data School
Author: The Data School