Let’s Talk About Waste
Hi, my name is Bruna Guglielmi, and I’m part of Cohort 26 from Sydney Data School. Today’s challenge was all about creating a story from a mining dataset with 12 tables, covering 1,171 mines and reports on 80 different materials from 2000 to 2021. Thankfully, we were given a pre-built data model, which was a lifesaver because the complexity of this dataset was on another level!
The Approach
At first, my plan was to dive into the Waste table and the main Facilities table, hoping to pull out insights that would compare Brazil’s mining waste data to the rest of the world. I went straight to creating DAX formulas without any initial data transformations, I wanted to see what I could build on the fly.
However, I quickly ran into an issue: the Brazil data was incomplete. There were so many nulls that it was clear I wouldn’t get much of a story from Brazil alone. So, I shifted focus to Peru. Why Peru? Because, in this dataset, Peru stood out as the top country in terms of waste production, with plenty of data to work with.
Insights on Peru’s Mining Waste
Switching to Peru turned out to be a goldmine of information. Not only does Peru have more mining waste than 17 other countries combined (yes, including big players like Brazil, South Africa, Australia, Argentina, Indonesia, Canada, and Guyana), but it also dominates the waste metrics, offering an extensive dataset that can fuel some insightful analysis.
Using this information, I began crafting some visuals in Power BI like:
- Bar Charts to compare waste output across facilities.
- Line Chart to show trends in waste over time.
- KPI cards, etc.
This analysis helps spotlight Peru’s role in global mining waste and offers a deeper look into the environmental impact of its mining activities.
Check out the documentation for more details on the dataset:
Thanks for reading and I hope you enjoy the final dashboard.
