OSEMN

OSEMN is a data science workflow that outlines five stages in typical data analysis projects: Obtain, Scrub, Explore, Model, and Interpret. The framework is commonly used as a teaching and practice aid, offering a simple, high-level guide to organizing work in data-driven projects. The order is often iterative rather than strictly linear, with teams revisiting earlier stages as new information emerges.

Obtaining data involves gathering data from internal systems, external datasets, web sources, or APIs. It also includes assessing data provenance and licensing, and preparing data for analysis to support reproducibility and traceability.

Scrubbing, or cleaning, covers handling missing values, correcting errors, addressing inconsistencies, and performing preprocessing such as normalization, encoding of categorical variables, and feature engineering to improve model performance and interpretability.

Exploration involves descriptive statistics and visualization to understand distributions, relationships, and potential anomalies. This stage helps formulate hypotheses, inform data preparation decisions, and guide modeling approaches.

Modeling applies statistical and machine learning methods to build predictive or explanatory models, including training, validation, hyperparameter tuning, and evaluation against appropriate metrics. Practitioners may use baseline models to establish a reference and iterate with more sophisticated approaches as needed.

Interpretation focuses on communicating results to stakeholders, translating findings into actionable business or domain insights, and validating interpretations with domain knowledge. This stage also involves considering limitations, assumptions, ethical implications, and potential impact.

In practice, OSEMN supports an iterative cycle across data collection, cleaning, analysis, modeling, and communication, complementing more formal methodologies such as CRISP-DM.