When you think about the word “format,” many things might come to mind. Think of an advertisement for your favorite store. You might find it as a print ad, a billboard, or a commercial. The information is presented in the format you need. The data format is a lot like that, and choosing the right format will help you manage and use your data in the best way possible.

I don’t know about you, but  I sometimes get stuck between a couple of choices when choosing a movie to watch. If I’m in the mood for excitement or suspense, I might go for a thriller, but if I need a good laugh, I’ll choose a comedy. If I can’t decide between two movies, I might use some data analysis skills to compare and contrast them. Come to think of it, there really needs to be more movies about data analysts. 

I’d watch that, but since we can’t watch a movie about data, at least not yet, we’ll do the next best thing: watch data about movies! 

When we look at the spreadsheet with movie data, we can compare different movies and movie genres. It turns out you can do the same with data and data formats. We’ll start with quantitative and qualitative data. If we check out column A, we’ll find the titles of the movies. This will be qualitative data because it can’t be counted, measured, or easily expressed using numbers. Qualitative data is usually listed as a name, category, or description. The movie titles and cast members will be qualitative data in the spreadsheet. 

Next up is quantitative data, which can be measured or counted and then expressed as a number. This is data with a certain quantity, amount, or range. Suppose the two columns show the movies’ budget and box office revenue in the spreadsheet. The data in these columns is listed in dollars, which can be counted, so we know the data is quantitative. We can go deeper into quantitative data and break it down into discrete or continuous data. Let’s look at some examples of Data formats:

Data formats examples

As with most things, it is easier for definitions to click when pairing them with examples you might encounter daily. Review each data format’s definition first, then use the examples to clarify your understanding.

Primary versus secondary data

The following table highlights the differences between primary and secondary data and presents examples of each. 

Data format classification	Definition	Examples
Primary data	Collected by a researcher from first-hand sources	Data from an interview you conducted – Data from a survey returned from 20 participants  Data from questionnaires you got back from a group of workers
Secondary data	Gathered by other people or from other research	Data you bought from a local data analytics firm’s customer profiles  Demographic data collected by a university  Census data gathered by the federal government

Internal versus external data

The following table highlights the differences between internal and external data and presents examples of each. 

Data format classification	Definition	Examples
Internal data	Data that is stored inside a company’s own systems	Wages of employees across different business units tracked by HR  Sales data by store location Product inventory levels across distribution centers
External data	Data that is stored outside of a company or organization	National average wages for the various positions throughout your organization Credit reports for customers of an auto dealership

Continuous versus discrete data

The following table highlights the differences between continuous and discrete data and presents examples of each.

Data format classification	Definition	Examples
Continuous data	Data that is measured and can have almost any numeric value	Height of kids in third grade classes (52.5 inches, 65.7 inches)  Runtime markers in a lecture   Temperature
Discrete data	Data that is counted and has a limited number of values	Number of people who visit a hospital on a daily basis (10, 20, 200) Maximum capacity allowed in a room  Tickets sold in the current month

Qualitative versus quantitative data

The following table highlights the differences between qualitative and quantitative data and presents examples of each.

Data format classification	Definition	Examples
Qualitative	A subjective and explanatory measure of a quality or characteristic	Favorite exercise activity Brand with best customer service Fashion preferences of young adults
Quantitative	A specific and objective measure, such as a number, quantity, or range	Percentage of board certified doctors who are women Population size of elephants in Africa Distance from Earth to Mars at a particular time

Nominal versus ordinal data

The following table highlights the differences between nominal and ordinal data and presents examples of each.

Data format classification	Definition	Examples
Nominal	A type of qualitative data that is categorized without a set order	First-time customer, returning customer, regular customer  New job applicant, existing applicant, internal applicant  New listing, reduced price listing, foreclosure
Ordinal	A type of qualitative data with a set order or scale	Movie ratings (number of stars: 1 star, 2 stars, 3 stars)  Ranked-choice voting selections (1st, 2nd, 3rd)  Satisfaction level measured in a survey (satisfied, neutral, dissatisfied)

Structured versus unstructured data

The following table highlights the differences between structured and unstructured data and presents examples of each.

Data format classification	Definition	Examples
Structured data	Data organized in a certain format, like rows and columns	Expense reports  Tax returns  Store Inventory
Unstructured data	Data that cannot be stored as columns and rows in a relational database.	Social media posts  Emails  Lectures