Today was the third day of the dashboard week. If you’re unfamiliar with dashboard week, it is a week where the data schoolers in training have to build 5 dashboards along with 5 related blogs in 5 days. So that’s 1 dashboard + 1 blog a day. Every day the topic and requirements are provided in the morning and we have till the end of the day to finish our work.
Dashboard Week Day 3 Topic
Today we got a surprising dataset to work with. Based on the knowledge of what the previous cohorts were given, we expected today to be a PowerBI challenge day. But to our surprise, it wasn’t. We had to use Tableau but there was a surprise twist. Here’s what the brief said.
- Create a dashboard based on the Kickstarter dataset sourced from Kaggle
- You’re not allowed to use Alteryx but you can use Tableau Prep
Not being able to use Alteryx always makes data cleaning a bit challenging. However, the dataset was fairly clean and I didn’t have to do much cleaning on Tableau prep. I was able to move into visualising in Tableau pretty quickly.
So I started by putting in the data in Tableau prep. We were given two files containing data on Kickstarted campaigns and various other details such as launch date, status, category etc. It turned out that using one of the files was enough as one was simply a clean update of the other. There were some bad data in there but cleaning them out was a fairly simple task.
I then moved on to visualising in Tableau. There was a lot that can be done using the data in terms of finding insight and seeing trends. But I wanted to focus on seeing how well campaigns do across different categories; what their expected amount is versus how much they actually end up getting. I wanted to do more of an overview than a granular analysis to be able to see patterns and draw insights from them. I was also keen to see which days and time of the day people are more motivated to launch their campaigns.
I decided to create a line chart showing the number of campaigns across the last few years as well as the average number of backers. This was to find out if the number of campaigns was increasing or if there were any seasonalities. I also wanted to see if the demand meets the supply. That is, if the number of campaigns increase, is the platform attracting more buyers as well. I also did a map dividing the countries into different regions to be able to compare the performance between the different parts of the world. I wanted to show the performance of campaigns across different categories in the most intuitive way. So I started by experimenting with bar-on-bar, combo chart and then finally decided on using a dumbell chart (Thanks for the help Alex).
To see the insights for yourself, you can see the viz on my Tableau profile or simply click on the screenshot of my viz below.
I would like to look into this data further but due to time constraints, I decided to call it a day. Hopefully, in the future I can blend in the insights from this dataset to use in some other projects.