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 |