The Power of Framework Dimensions: What Data Scientists Should Know

Conceptual frameworks in data science rely on dimensions which are classified as categorical, ordinal, or continuous. Categorical dimensions consist of unordered groups, ordinal dimensions have ordered categories, and continuous dimensions represent quantitative measures on a scale. The choice of dimension type involves a trade-off between information richness and the resources required for analysis and presentation. A case study of a software company's sales performance demonstrates how modifying framework dimensions, such as changing from continuous sales dollars to an ordinal performance ranking, can reveal new insights and guide different business actions.