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Day 2 – Super trouble with 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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If you read my blog yesterday, I ended it saying there is no way but up. Oh how wrong was I. Presented with a rich dataset of Australian superannuation data I was confronted with several challenges that I struggled with through the day.

The very first one was my lack of knowledge in the subject matter, which led to a rabbit hole of readings trying to find a story to tell through the data.

The second was transforming this data into a usable format. Considering that I had narrowed my scope yet, it was difficult to clean the data efficiently as I wasn’t sure which facet of the data I required.

Finally, I came up with the idea of building an Amazon like interface where a user could feed in parameters like their investment income, type of growth and period. Once the parameters are filled and a fund is selected there are visuals the user can acquire more details about that particular super fund.

The Win
I believe the concept and interface works well and my ability to quickly design a good looking dashboard has begun to improve.

The Learning

Going down a rabbit hole to quickly learn about a vast topic was definitely not the right approach. Having to do it all again I probably would build an exploratory dashboard to visualise the data on a macro level, allowing the user to slice and dice the data as they please.

Onto the next one and it is bound to be better.

https://shorturl.at/arFJ7

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