... and it was! Today Jenny gave us data from the Organisation of Economic Co-operation and Development (OECD) which had 8 datasets about wages and pay!
We were tasked to pick at least 2 datasets and complete the data preparation in Power Query and visualisations in Power BI.
I used data on annual wages across countries and time, as well as the age and gender gaps in wages across time. My plan was to do a demographic deepdive into the factors that may affect one's annual wage, specifically telling a story based on my own qualities that may or may not put me at a disadvantage against the world population i.e. my age, gender, and country I am living in. I was really intrigued to see the actual quantified effect of each one and whether there were any significant insights which means these factors would really impact wage.
Big take-aways from today
- I wasted time on data prep as I rushed and missed basic errors. For instance, I misclicked when joining and duplicated many rows so that I did not understand what one row of data was once my charts were built. Going back and really assessing the granularity of each table, and ensuring one row of data looked how I wanted in Power Query was so important in my progression in actually building the charts
- My dashboard plan essentially was not possible with the granularity of data and I had to re-adapt! Felt frustrating, but eventually, I could reiterate that creative process and thought of something new.
- Prioritising having correct data and conceptualising something analytical was more important than formatting, eventually I will improve my time management so I can then add on viz best practices.
- I pushed myself to try using R Studio today, and am happy I learned something new!
Here is the Viz!