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Dashboard week challenge 2: Superannuation data

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 the dashboard week challenge, we were tasked with creating a dashboard that visualizes the Australian Superannuation Data. It is a comprehensive dataset that covers various aspects of superannuation funds, such as fund members, fund benefits, fund performance, and fund assets. The dataset contains 13 tables with different levels of granularity and dimensions. Working with this dataset was a great opportunity to learn more about the superannuation system and the factors that affect its performance. It also helped me develop the awareness of the following:

 

Outliers and extra background research

 

One of the key lessons I have learnt from doing this dashboard is to focus on outliers. Outliers are data points that deviate significantly from the rest of the data. They can reveal interesting insights or potential errors in the data.

 

For example, in the dataset, there are some funds that have exceptionally high or low investment returns compared to the average. By investigating these outliers, I was able to find out the reasons behind their performance, such as their investment strategy, asset allocation, or market conditions.

 

Another example is the fund with the highest number of members, which is the AustralianSuper fund. This fund has more than 2.3 million members, which is more than double the size of the next largest fund. By doing some background research, I was able to find out that this fund is an industry fund that caters to a wide range of sectors and occupations, and that it has a strong reputation for delivering competitive returns and low fees.

 

Background research can help me understand the context and meaning of the data, as well as validate or challenge my assumptions. It can also help me identify the limitations and gaps in the data, and how to address them.

 

For example, in the dataset, there are three noticeable dips in the investment return rate, which are 2016, 2020, and 2022. By doing some background research, I was able to find out the major events that occurred during those years, such as the real estate crash in America, the COVID-19 pandemic, and the Russia-Ukraine war. These events had a significant impact on the global financial markets and the superannuation industry.

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