By 2025, there will be 175 zettabytes of data in the global datasphere. Not sure what a zettabyte is? Well, one zettabyte has 21 zeros…
In short, there’s A LOT of data out there.
But not all data is created equal. No matter what you’re measuring, the best data for one situation isn’t always the best for another. Likewise, certain forms of data collection are better suited to specific concepts.
But what are the different types of data that exist today? Let’s take a look!
Quantitative data is the answer to questions such as “how much?” and “how many?”, representing numerical values that are easy to quantify and understand. Quantitative data is also simple to organize, although OvalEdge can help you get on top of all your data governance, no matter the amount or type of data you have. Some examples of quantitative data are:
- Percentage scores on a test
- Room temperature
You can divide quantitative data into two categories: discrete and continuous.
Discrete data refers to integers, or whole numbers. You can’t subdivide discrete values into parts. For example, the number of people working in a company is discrete data as you can only count whole individuals. You can’t count 57.3 workers. As such, discrete data only has a limited number of possible values.
In contrast, continuous data is information that you can subdivide into smaller levels. You can measure this continuous data on a scale and it can have almost any numerical value. For example, you can measure your height at more and more precise scales, down to the millimeter or even smaller.
Qualitative data is information that you cannot measure or express as a number. Instead, qualitative data consist of pictures, symbols, and words. Because you can sort qualitative data into categories, it is also called categorical data. Some examples of qualitative data include:
- Favorite ice cream flavor
- Economic status
There are two types of qualitative data: nominal and ordinal.
Nominal data labels different variables, without giving them any kind of quantitative value. There is no intrinsic order or sequence to these variables, they are only the names or labels of the different possibilities. Some examples of nominal data include:
- Eye color
- Marital status
- Gender identity
Even for nominal data with few categories, such as eye color, there is no way to order the possible answers from highest to lowest.
Ordinal data shows is qualitative data with ordered values, making it somewhere in between quantitative and qualitative variables. Some examples of ordinal data are:
- Final race positions
- Letter grades
- Customer satisfaction on a scale of 1 to 10
As you can see, ordinal data represented by numbers only show sequence, meaning that you cannot use them in mathematical analyses.
Using a Combination of Different Types of Data
All of these different types of data play a crucial role in statistics, research, and data science.
Gathering all of these different data types together and knowing how and when to apply them is the key to making successful data-driven decisions for organizations and businesses alike.
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