The task

Create a dashboard using CPI data from ABS. Our coach had kindly cleaned the data for us to use, along with wages data to supplement.

There was an extra bonus if we were to supplement data.

Granularity of the data was not the same with each table.

The approach

With a lack of documentation I decided to focus on looking at the cleaned data with Alteryx.

I spent a wasteful 3 hours trying to create another Alteryx workflow and a new dataset. Although it helped with revision since I had not used Alteryx during my involvement in all my client projects.

Moving forward

I decided to look through the cleaned dataset and use that instead.

Upon observing the data through Tableau I found an outlier and zoned in on it. I attempted to figure out why the outlier occurred. Unfortunately it took me less than a couple of clicks after a Google search to find my answer. So my exciting analysis ended there.

I believed my new problem was that I had not done enough so I continued to look for any other interesting outliers and went towards that.

I did manage to go a little bit further but not much.

As a backup, I made another dashboard (using similar metrics and style). It didn’t end up being necessary but it did the job of lightening up the mood.

I made the decision not to supplement my data due to shortness of time.

First dashboard

Second dashboard


My dog and I doing a retrospective on these dashboards, this morning

What I did well
  • Revision of Alteryx
  • Zoned in on outlier for a story
  • Researching and attempting to understand the meanings behind technical terms
  • Researched said outlier to explain the “why”
What I can improve on
  • How I start – Once again my emotions got the better of me
  • I could have done a cross analysis of pets and childcare
  • Maybe I will research the field first before beginning to look at the data?
  • Further understanding as to how joins and relationships in Tableau work
  • Better timeboxing required
  • Work on an MVP first
  • Think of a way that is easier for users to connect charts. For example, in my first dashboard I used a heat map and a line charts. This can cause difficulty for people to interpret if there is any relationships or connections between the two charts.

I hope you enjoyed this post.

You can check out the dashboard here.

If you would like to contact me, please feel free to connect with and message me over at LinkedIn.

Thank you for reading and have a great day!

The Data School
Author: The Data School