Day two of dashboard week, prove very challenging, ABS data is not the best data to work with. Although some cleaning was already done when we got given the datasets (3), the Consumer pricing data had a Hierarchy of commodities nested within the data. Group > Subgroup and Expenditure class was all in one column, making drill down challenging. Lesson learned here, double check the data even though it’s cleaned to avoid surprises.

I decided to go after it and structure it to create a drill down, however more issues presented themselves the more I tried. Given the time constraints, I decided to pivot and create a simple dashboard ab Commodities and wages on a high level.

Looking at Consumer Pricing Index Goods Vs Services performance, the impacts of Covid were clear, with the average percent change for Services dripping to its lowest since the 1980s (3.85 pp).

Alcohol and tobacco are the only commodities that performed well over the course of the pandemic, however, Education and Furnishing, household equipment, and services took a steep fall, but have since regained pre-pandemic rates.

Many Industries reached a 5-year high such as Administrative and support services, Information Media and telecommunications, Manufacturing, Wholesale trade,  Rental, Hiring and Real estate.

With the exception of Melbourne, most of the other major states are recording it’s highest Wages figures in 5 years.


  • Time boxing – better time boxing and reverting to MVP, could have allowed to bring in additional data to enrich the visual
  • Thinking through the problem first in all of its complexity instead of trying to force the outcome.
Jude Shu
Author: Jude Shu