4 Types of Data: Nominal, Ordinal, Discrete, Continuous

Data refers to the collection of information that is gathered and translated for specific purposes. With over 2.5 quintillion data being produced every day, it is crucial to utilize it in a particular way so that it serves its value to computers and humans for better practices.

Data is organized and stored in charts, graphs, and tables. Data is an invaluable commodity with the potential to reap exceptional benefits for any organization if utilized wisely. In order to be utilized at its maximum efficiency, data has to be stored error-free. The best way to do so is by segmenting it into different sections or types. 

There are two forms of data, Quantitative Data and Qualitative Data. These forms of data are further classified into four categories: Nominal, Ordinal, Discrete, and Continuous.   

Let us read along to know more about types of data in detail.

Different Types of Data in Statistics

There are two types of data listed below:

Qualitative Data

Also known as categorical data, this type of data is neither measured nor counted in the form of numbers. This form of data is assembled by category, not by numbers and usually consists of text and photographs. Qualitative data talks about the emotions of a person. It helps market researchers know and understand their customers’ tastes and preferences, so they can further create new strategies per customers’ demands. Qualitative data helps to provide in-depth analysis. 

Qualitative Data is divided into two parts:

1. Nominal Data

The first type of qualitative data is Nominal Data which labels variables without the numerical value. It is a form of data that cannot be measured. 

Let us understand nominal data with an example. For instance, the color of a car can be black, red, or orange. Here we need to realize that no color is greater than the other. It just represents a particular color of a car.

Nominal data does not follow any hierarchy. This kind of data is used in fields such as education, healthcare, and business to collect and make relevant assessments. Analysts collect nominal data with the help of public surveys to further analyze and obtain results. 

2. Ordinal Data

Ordinal data values are also grouped into categories, just like nominal data. This kind of data is considered a mixture of quantitative and qualitative data while it follows a specific pattern of hierarchy.

Let us take an example to understand ordinal data even better. For instance, the economic status of people within a society. There are usually three socioeconomic status categories: High, Middle, and Lower. Here we can identify that the high class is considered superior to the middle class. Therefore, ordinal data follows a specific pattern of hierarchy. 

Nominal Data versus Ordinal Data

Here are the differences between nominal and ordinal data listed below: 

• Nominal data cannot be compared with one another, whereas ordinal data can be used to compare different items by natural ordering. 
• Nominal data is one type of qualitative data, whereas ordinal data type is referred to be in-between qualitative data and quantitative data. 
• Nominal data does not use numerical values, whereas ordinal data uses numerical values. 

Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

Quantitative Data

In quantitative data, information is countable and measured. This kind of data can be used for statistical manipulations and are represented on pie charts, histogram, and bar graphs. Data like these play an important role in statistics. The researcher uses quantitative data to solve “what” and “how” problems. This form of data is used frequently in math calculations or statistical analysis. 

Quantitative data is further divided into two parts: 

1. Discrete Data

The word discrete means separate. A discrete data value can be counted but cannot be measured as its holds only fixed values. The data is easy to visualize and calculate since a numerical is usually a whole number. The discrete data tends to be easily calculated as it is organized in graphs and scatter plots.  

For instance, the number of chocolates in a box or students in a class are examples of discrete data. Discrete data is represented in bar charts and pie charts, which are further used to compare different and unique data points. 

2. Continuous Data

Continuous data is a type of quantitative data that represents information in smaller parts that are easy to measure. Continuous data is more accurate as compared to other data types. Height, weight, and temperature are examples of continuous data. Although offering accuracy, continuous data type tends to change over time. For instance, the height of an individual will change over the coming years. 

Discrete Data versus Continuous Data 

Some of the differences between discrete and continuous data are given below: 

• Discrete data is countable, whereas continuous data is measurable. 
• The values in discrete data cannot be separated into smaller pieces, whereas in continuous data, the complex values can be segmented into smaller, more consumable parts. 
• Discrete data is represented by bar graphs, whereas continuous data is represented mainly by histograms. 

Check our US - Data Science Programs

Significance of Data

The role of data in fueling growth for several businesses is pretty apparent. Data plays a prominent role in establishing a thriving organization that consistently works on its inefficiencies with data-driven insights to better understand its business and customers. Here’s how data influences varying domains to mark its importance. 

Informed Decision-Making

Data helps an organization gather information and knowledge related to different business aspects, from customer behavior to the latest trends. This further helps the leader to make well-informed decisions. Therefore, clean and structured data is vital for better reasoning and less risky decisions based on data-driven facts.  

In-depth Understanding

An in-depth understanding of data helps gain accurate insights for individuals and organizations. Leaders should understand how every aspect of a business is performing in order to accomplish its target and goals, which is only possible with the help of data-led analysis of organizational processes. 

Effective Problem Solving    

When any product fails to promote sales, data helps organizations to discover the cause and underlying reasons for its failure. Along with a report card of aspects that need improvement, data assists organizations in reaching a valid conclusion as well, extending possible solutions. 

Comprehending User Behavior  

For a successful organization, it is essential to understand customers’ needs and the company’s stance on how well they can perform on the requirements. Data can help a business to build better knowledge and understanding of their potential customers. This will enable them to identify the specific needs of their audience.

Along with user experience, personal growth is another aspect data can influence. For instance, keeping track of the annual objectives and successful goals of specific employees enables managing entities to evaluate their yearly performance. 

Read our Popular Data Science Articles

Exhibiting Process Improvement   

Data helps to reduce the wastage of resources, thereby improving the processes. Further, it helps the leaders to know the organization’s weaknesses, hurdles, or breakdowns in a process. Hence, data plays a vital role in every business. With the help of insights redeemed from varying databases, companies can supervise each step of an ongoing process, along with its vulnerabilities, which can be further improved to make the project a success. 

Conclusion 

The significance of data in different fields is apparent following its use to fuel diverse organs of a business. From understanding employee performance to encouraging marketing teams to absorb in-demand techniques for better customer experience. Knowing data types and their importance in assessment is essential to adopt innovative data analysis tools, which can help you reap maximum value for your business. 

One such field that heavily leverages data is Business Analytics, which uses statistical models built on large databases to reap insightful information for organizations. If you’re interested in opting for a career in Business Analytics, check out upGrad’s Masters of Science in Business Analytics.

Offered under the guidance of San Francisco’s leading institute Golden Gate University, this program covers topics such as Business Intelligence, Machine Learning, Predictive Analytics, Natural Language Processing, and more to help you obtain lucrative opportunities.