Which data type would be best for a ranking system, like customer satisfaction?

The best data type for a ranking system, such as customer satisfaction, is ordinal data. This is because ordinal data is specifically designed to represent categories with a meaningful order, allowing for the comparison of different levels of satisfaction among customers. For instance, if you rank customer satisfaction on a scale from "very dissatisfied" to "very satisfied," the rankings indicate a clear hierarchy of customer feelings.

In this context, ordinal data provides both the ability to sort the data in order of importance or satisfaction levels and a way to understand the relative difference between those levels. While we know that one level is higher than another, the exact differences between ranks may not be quantifiable in precise terms.

Other data types, such as nominal, ratio, and interval data, do not capture this ordered relationship effectively. Nominal data categorizes items without any order, while ratio and interval data involve numerical values where the differences are meaningful and can be measured or quantified, which is more than what a simple ranking requires. Thus, ordinal data is uniquely suited for a ranking system like customer satisfaction.