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Dashboard Week day 2 – From the best to the not so good superannuation funds in Australia

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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On day 2 of Dashboard Week, we worked with annual fund-level superannuation statistics. The data ranged from 2004 to 2022 and came from the Australian Prudential Regulation Authority (APRA). Applicants for the next Melbourne cohort, DSAU22, who made it to the final round of interviews also received a similar dataset. However, Coach David said the applicants were only given one table to work with and it was cleaned data. On the contrary, we had free reign on 15 tables and some data cleaning was expected. David also ruled out exploratory dashboards this time – we had to find and tell a story that’s hidden within the data with our final viz.


See other posts in my Dashboard Week series:


Data exploration

I started poring through the data and found some interesting things I could focus on:

  • There are two categories of superannuation plans. Defined benefit superannuation plans tie one’s final superannuation takings with one’s salary, service tenure and age. On the other hand, defined contribution plans are more common in Australia and depend on one’s own regular contributions (as well as any contributions from one’s employers) before pension age. What are the characteristics for people who choose defined benefit plans vs defined contributions plans?
  • I have been with Unisuper for quite some time from when I worked at university years ago (despite various job changes since). In FY22, how did my superannuation fund Unisuper do, across various metrics, when compared with the top three funds in Australia?
  • Last of all, I stumbled upon eligible rollover funds. ERFs are a kind of ‘miscellaneous’ superannuation fund. They are used to store small amounts of money from lost or inactive superannuation accounts. What kind of ERFs are in existence and what is the history behind these funds?

At this point I came upon a news article from January 2020. Back then, AMP had opened new ERFs for customers without their consent. These ERFs were created to house excess fees charged that ought to have been refunded to customers directly. So, I decided to compare and contrast AustralianSuper, which has almost always been the #1 superannuation fund over the years, with AMP’s ERF and their other superannuation funds.

Deciding on the focus of my viz

After building a number of charts, I settled on four that served the time-series analysis angle quite well:

  • Total assets
  • Total number of accounts
  • Marketing expenses
  • Rate of return (over 1 year, 5 years and 10 years)

By a stroke of luck I discovered AustralianSuper (orange) and AMP (blue) had naturally contrasting brand colours. I split up my viz into two halves, with each side dedicated to the funds belonging to one of these superannuation companies. This way, the viz could either be read left to right within each section from top to bottom, or flow from top to bottom for one set of funds on the left followed by the other funds on the right.

Alteryx workflow

Here is my Alteryx workflow for processing the data:

Alteryx workflow for combining superannuation fund profiles
Alteryx workflow for combining data on superannuation fund performance
Alteryx workflow overview

Tableau viz

See my viz on Tableau Public:

Tableau dashboard on superannuation funds in Australia

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