Data can be vast and overwhelming, so understanding the different types helps to simplify what kind of numbers we are looking for. Even with the treasure trove of data most organizations have in-house, there are tons of additional data sets that can be included in a project to add valuable context and create even deeper insights. It’s important to keep in mind what type of data it is, when and where it was created, what else was going on in the world when this data was created, and so forth. Using the example of a restaurant, let’s look at some different types of data and how they could impact an analytics project.
Numerical data is something that is measurable and always expressed in numerical form. For example, the number of diners attending a particular restaurant over the course of a month or the number of appetizers sold during a dinner service. This can be segmented into two sub-categories.
Discrete data represent items that can be counted and is listed as an exact number and take on possible values that can be listed out. The list of possible values may be fixed (also called finite); or it may go from 0, 1, 2, on to infinity (making it countably infinite). For example:
Number of diners that ate at the restaurant on a particular day (you can’t have half a diner.)
Amount of beverages sold each week.
How many employees were staffed at the restaurant on a day.
Continuous data represent measurements; their possible values cannot be counted and can only be described using intervals on the real number line. For example, the exact amount of vodka left in the bottle would be continuous data from 0 mL to 750 mL, represented by the interval [0, 750], inclusive. Other examples:
Pounds of steak sold during dinner service
The high temperature in the city on a particular day
How many ounces of wine was poured in a given week
You should be able to do most mathematical operations on numerical data as well as list in ascending/descending order and display in fractions.