AI Super Powers for FP&A: Hexbin plot with marginal distributions
With the “new” power of AI and help of tools like ChatGPT, Gemini, or Copilot, everyone can start coding in Python in hours.
But what to do with these new superpowers in FP&A?
One quick win is to design and use better and more powerful graphs to enhance business partnering, influence strategic decisions, and communicate insights more effectively.
But which graphs?
Welcome to our new series: AI Super Powers for FP&A.
In this series, I aim to show you 50 things that you didn’t believe were possible (at least just using Excel) but now are thanks to AI.
Today, I bring you “Hexbin Plot with Marginal Distributions”
I know, fancy name.
But I like to think of a Hexbin plot like a city map with a traffic heatmap.
Each hexagon represents a neighborhood, and the color shows how busy the traffic is in that area.
The busier the traffic (more data points), the darker the hexagon.
The marginal distributions are like side streets showing how traffic flows into and out of each neighborhood.
