2024-09-26 –, Gaston Berger
One of the more mundane tasks in the business analytics world is to measure KPIs: averages, sums, ratios, etc. Typically, these are measured period over period, to see how they trend. If you're a data analyst, you've likely been asked to debug/explain a metric, because a stakeholder wants to understand why a number has changed.
This topic isn't well grounded theory, and the answers we come up with can be lacklustre. In this talk, we discuss solutions to this very common topic. We will look at a methodology we have developed at Carbonfact, and the opensource Python tool we are sharing.
This talk is for all the people who perform analytics in their daywork. The goal of the talk is to add a tool to your belt. You'll leave the talk with a robust framework for answering questions from your stakeholders. For instance, you'll be able to attribute the change in a metric to the drivers of said metric, down to the decimal point.
The presentation will present the (simple) maths of the methodology we propose. It's a simplified version of the Blinder-Oaxaca decomposition framework. We will then present the Python toolbox we are open-sourcing, along with a tutorial. Finally, we will discuss what are the best ways to share such methods in the current data tooling landscape (e.g. with the rise of DuckDB and Polars).
The talk will be given by Max Halford, who is Head of Data at Carbonfact. Max has already given a talk at PyData Amsterdam, and regularly gives talks at different venues.
