Dimensionality reduction compresses high-dimensional data into fewer features while preserving the most important information, making it easier to visualize, process, and model. PCA (Principal Component Analysis) is the classic linear method; t-SNE and UMAP are popular non-linear alternatives for visualization. A data scientist at a Moroccan telecom might reduce hundreds of customer-behavior features to a handful of components before clustering or building a churn model.