The OSEMN Framework is a structured process for data analysis and machine learning that helps data analysts to structure their work and ensure that the results are reliable and meaningful. The five stages of the framework are: Obtain, Scrub, Explore, Model, and Interpret.
In a 2010 post called “A Taxonomy of Data Science” on the Dataists blog, Hilary Mason and Chris Wiggins introduced the OSEMN framework.
OSEMN Data Analysis
Goals
To ensure that all necessary data is obtained and made available for analysis.
To clean and preprocess the data to be ready for exploration and modeling.
To understand the characteristics and relationships of the data through exploration.
To build and evaluate models to make predictions and generate insights.
To interpret the results and communicate the findings to stakeholders.
Best pratices
Start by defining the goals of the analysis and determining what data is needed to meet those goals.
Pay attention to detail when scrubbing the data, which can significantly affect the analysis results.
Use visualization techniques to explore the data and uncover relationships and patterns.
Try multiple models and techniques to see which ones perform best.
Make sure to communicate the results clearly and concisely, using visualizations and other tools to make the findings accessible to stakeholders.