These are practiced for labeling variables without any quantitative value. The scales are generally referred to as labels. And these scales are mutually exclusive and do not carry any numerical importance. Let's see some examples:

• What is your gender?
• Male
• Female
• Third gender/Non-binary
• I prefer not to answer
• Other
• Other examples include the following:
• The languages that are spoken in a particular country
• Biological species
• Parts of speech in grammar (noun, pronoun, adjective, and so on)
• Taxonomic ranks in biology (Archea, Bacteria, and Eukarya)

Nominal scales are considered qualitative scales and the measurements that are taken using qualitative scales are considered qualitative data. However, the advancement in qualitative research has created confusion to be definitely considered as qualitative. If, for example, someone uses numbers as labels in the nominal measurement sense, they have no concrete numerical value or meaning. No form of arithmetic calculation can be made on nominal measures. 

You might be thinking why should you care about whether data is nominal or ordinal? Should we not just start loading the data and begin our analysis? Well, we could. But think about this: you have a dataset, and you want to analyze it. How will you decide whether you can make a pie chart, bar chart, or histogram? Are you getting my point?

Well, for example, in the case of a nominal dataset, you can certainly know the following:

• Frequency is the rate at which a label occurs over a period of time within the dataset. 
• Proportion can be calculated by dividing the frequency by the total number of events.  
• Then, you could compute the percentage of each proportion.
• And to visualize the nominal dataset, you can use either a pie chart or a bar chart.  

If you know your data follows nominal scales, you can use a pie chart or bar chart. That's one less thing to worry about, right? My point is, understanding the type of data is relevant in understanding what type of computation you can perform, what type of model you should fit on the dataset, and what type of visualization you can generate.