Nominal Data

Nominal data, often called “categorical data,” is a type of data that is used to label or name categories or groups. In business intelligence and data analytics, nominal data helps companies sort information without any numeric value or order. Think of it like organizing your favorite T-shirts by color, not by which one you like the most.

For example, if a store keeps track of what clothing items are returned, they might use nominal data to mark each item as a “shirt,” “pants,” “dress,” or “shoes.” There’s no ranking here; a shirt isn’t better or worse than shoes, it’s just different. When businesses analyze this kind of data, they can figure out patterns, like if more shirts are returned than shoes, but they can’t use it to calculate an average or do other mathematical operations.

Analyzing nominal data helps businesses make decisions on product lines, marketing strategies, and more. By using special statistical techniques designed for categorical data, such as frequency counts or chi-square tests, companies gain insights into customer behavior and preferences, which can lead to more effective business practices and strategies.