Data reduction is essential for managing the growing scale and heterogeneity, and computational cost of modern analytical pipelines. Selecting an appropriate reduction technique remains challenging due to the diversity of existing methods and the complex interplay between dataset properties, analytical constraints, and user-defined priorities. Existing taxonomies describe families of reduction strategies but do not provide solutions for choosing a method that is compatible with a given context. We address this gap by introducing a context-aware, paradigm-agnostic decision-support system that formalizes reduction-method selection as a multi-objective optimization problem. The system generalizes observed method behaviours via clustering and identifies Pareto-efficient trade-offs before ranking alternative techniques according to a preference-consistent operator. Experiments on seven real-world datasets from diverse domains demonstrate that the system consistently recommends methods that align with dataset characteristics and user priorities.