When you run a business, it’s important to keep on top of your data management. But what types of business data are there? Let’s take a look at the main categories.
Ordinal and discrete data
1. Ordinal data
This kind of data has a specific order attached, combining quantitative and nominal information types. The distinct order is there but it acts as a label. You can’t calculate any of the values in the list. The main objective of this type of data is to show how a certain subject is either performing or working in relation to something else.
For example, the outcome of a running race, showing who finished first, second, etc, would be ordinal data. In a business, a data collection company such as https://shepper.com/ might use ordinal data to rank customer shopping experiences.
2. Discrete data
This kind of quantitative data can’t be divided into parts as it only works with whole numbers. For example, it could show how many people are in a group. Every individual is one person, and a person cannot be split into a fraction! This type of data is often used for inventory checking and assessing.
Continuous and interval data
3. Continuous data
This type of data can be divided into parts such as fractions, but it keeps its meaning. For example, you could measure time in years, or split it into other units of measurement, such as seconds, minutes, days or weeks. This type of data is great when you’re focusing on specifics, such as production weight for shipping costs.
4. Interval data
This type of data combines variables both by order and by the differences between them. They’re measured on a scale, which means that negative values can be included. An example of this could be a demographic survey that includes income data by increments or brackets.
Summary
This article explains four types of business data. It covers ordinal data, which ranks items in order without allowing calculation, such as ranking customer experience, and discrete data, whole numbers used for things like inventory counts. It also covers continuous data, which can be divided into fractions and units like time or weight, and interval data, measured on a scale that can include negative values, such as income brackets in a demographic survey.
Questions this article raises
- How do you decide which of these four data types applies to a given business metric?
- What tools are best for analysing ordinal data like customer experience rankings?
- Why does interval data allow negative values when ratio data typically doesn’t?
