Understanding Types of Data: Why is Data Important, its 4 Types, Job Prospects, and More

Can you imagine a world without data? Without insights into the digital economy, weather forecasts, and reliable information to make business decisions, it would be a challenging scenario for individuals and organizations to thrive. 

According to Keboola, data-driven organizations are 19 times more likely to be profitable than those that don’t use data. In fact, businesses using data for decision-making witnessed an 8% increase in profits and a 10% reduction in cost as per Keboola. In this article, you will look at different types of data and discover how to build a career in data science and related fields, and much more. 

Dive in!

What are the Different Types of Data?

Data is essentially a collection of information used to generate insights or make decisions. While data is often thought of in terms of four main types — Nominal, Ordinal, Discrete, and Continuous — these are actually subsets of two broader categories: Qualitative and Quantitative data.

Understanding the distinction between these two main categories is key before diving into their subsets. Here’s a breakdown of the core differences.

Also Read: 6 Phases of Data Analytics Lifecycle Every Data Analyst Should Know About

You might already be familiar with these four major types of data, but it’s also important to remember that data exists in other forms as well. Beyond Nominal, Ordinal, Discrete, and Continuous, we’ll also help you understand Primary, Secondary, Raw, Processed, Structured, Semi-structured, and Unstructured data types.

That said, let’s get started with the four main types of data in detail.

Nominal Data (Qualitative)

Nominal data represents categories or labels that do not have any particular order or rankings. They are just labels or names that help you distinguish between different items. Neither is ‘higher’ or ‘better’ than the other. 

A simple example of nominal data is the eye colors - whether it is blue, green, black, or brown. Each color is just a label and there is no logical way to rank them. You cannot say that one color is superior to another.

This type of data is usually analyzed using the grouping method. Here are the steps to analyze the data.

• Group the variables into different categories.
• Calculate the frequency (or count) for each category.

Furthermore, Nominal data finds its application in demographic studies, where individuals are classified by attributes such as age, gender, ethnicity, and literacy level – none of which involve a ranking system, but rather categorize population into distinct groups.

Ordinal Data (Qualitative)

Just like Nominal data, Ordinal data also consists of categories, but each category has a meaningful order or ranking. The categories can be arranged in a specific order, even though the gap between these categories is not equal.

A simple example of ordinal data is the ranking in a competition - 1st place, 2nd place, 3rd place, where there is a precise order. 1st place is better than 2nd place, and so on. But you cannot determine how much 1st place is better than 2nd place.

These types of data are usually analyzed using visualization tools, with tables being the most commonly used tool. Here are the steps to analyze the data using the visualization method.

• Organize the data in a table, with each row indicating a distinct category.
• You can also represent the data using charts, especially bar charts.

Furthermore, Ordinal data finds its application in the e-commerce industry, where customer feedback and satisfaction is used by the companies to rank their products.

Discrete Data (Quantitative)

Discrete data is information that only takes specific, separate values. Imagine it to be counting items that come in complete units and cannot be broken down into fractions.

A simple example of discrete data is the number of students in a classroom. The number of such students can be 50, 51 or any other whole number. However, the number has to be a whole number and cannot be cannot be, say, 49.5. 

These types of data can be analyzed using the following steps.

• Use graphs to represent the data.
• Calculate the mean, median, range and mode from the graph.

One of the major applications of discrete data is in the manufacturing industry, where manufacturers can identify defect rates, perform quality checks, and implement corrective measures.

Continuous Data (Quantitative)

Continuous data refers to data that can be measured, but not restricted to a whole number. It can take any value between a range - either a decimal or a fraction. In short, there are an infinite number of possibilities for continuous data.

A simple example of continuous data is measuring a person’s height. You can say the height is 5 feet, or you can be more accurate by saying they are 5.5 feet or 5.75 feet tall. You can keep on adding values in decimals to get infinitely accurate.

These data types can be analyzed using various methods as follows.

• Descriptive statistics can help understand data sets using measures such as mean, mode, median, range, variance, and standard deviation.
• Linear regression helps to draw a best-fit line through scatter point data.
• Correlation coefficients can represent how one variable changes with respect to another.
• Data visualization allows the identification of patterns from a plotted graph.

Furthermore, Continuous data finds application in the form of stock prices and exchange rates – used by financial professionals to make decisions.

Primary Data vs Secondary Data: Key Differences

Primary data is collected by the person conducting research to understand and solve the problem at hand. Sources of primary data are chosen to meet the demand of the research.

However, secondary data is collected by someone else and made available for others to use. The data may have been collected for a different purpose.

Primary and Secondary data can be differentiated based on many metrics, tabulated below.

Application of Data in Real-Life: Use Cases

All types of data have countless real-life use case possibilities, ranging from a simple policy implementation to a more complex spacecraft launch. 

Some of the essential applications of different types of data are tabulated below. Have a look.

Raw vs. Processed Data

Raw data is information collected and stored in an unorganized form without any changes. It usually includes data collected from sources such as instruments and sensors. 

Unlike raw data, processed data is obtained through editing, cleaning, and correcting. Presenting the data in processed form makes it much easier to understand.

Raw data and processed data differ in many ways, tabulated below.

Structured, Semi-Structured, and Unstructured Data

Structured data is organized in a rigid manner that makes it easy to manage and search. An example of structured data is a customer data table that contains complete information such as name, address, and contact number.

Semi-structured data is only partially organized. Examples of semi-structured types of data include Log files and CSV files.

On the other hand, unstructured data does not follow a predetermined structure or format. It is pretty standard among big data. Examples of unstructured data include Communication data and Social Media data.

Also Read: Data Science for Beginners: A Comprehensive Guide

What is the Importance of Data?

In today’s world, no major decision is made without data analysis, and the collected data is invaluable. In fact, you can safely say that data is the next big valuable thing after crude oil. The potential use of processed data is immense and cannot be measured in terms of monetary value.

That being said, let’s hop on to the benefits of data that you must know.

5 Reasons that Justify the Importance of Data

Data is an invaluable asset today. It is no longer a collection of numbers and facts that gets limited attention. It has the potential to unlock facts that are usually concealed.

From targeting marketing campaigns to supply chain management, data plays a pivotal role in driving modern businesses. Utilizing the potential of data can help identify potential growth and mitigate upcoming challenges.

Here is a closer look at the 5 important reasons why data is very critical in the current scenario.

• Informed Decision-making:

  Strategic decisions can be made by using insights gained from data analysis.

• Reducing Business Risk:

  Data analysis can provide hints about the potential risk to the business.

• Gaining Advantage over Competitors:

  Data analysis will help identify sectors where there is limited competition.

• Problem Solving:

  Problems like supply shortages can be handled by taking suggestions based on data analysis.

• Improving Efficiency:

  The efficiency of the organization can be enhanced by identifying loopholes in functioning.

Pros and Cons of Using Data

Every technology has its pros and cons. Data as an entity has unlimited potential for benefits, but it can also be used for wrongful purposes. 

For instance, data can be collected and analyzed to solve a problem or to start one. Needless to say, whether data is beneficial or burdensome depends on the user's intention. 

That being said, here’s a closer look at the benefits and drawbacks of different types of data that you must know.

Top 5 Benefits of Using Data

Data from a reliable source has many uses, both in the public and private sectors. The major benefits associated with data are as follows.

• Targeted Service:

  The government can introduce schemes for targeted social groups based on data.

• Cost Cutting:

  Reducing resources allocated to products that have low sales.

• Understand Customer Mindset:

  Data analysis will help understand what a customer wants from a product and then modify the product accordingly.

• Expansion of Business:

  Good quality data will provide insight into market trends, helping business grow.

• Innovation:

  Data can help analyze market trends and identify opportunities that the business can utilize to introduce innovative products.

Potential Drawbacks of Using Data

Data is not entirely risk-free. Some issues can arise due to its use. This is the reason why data is termed as a double-edged sword. Some of the potential drawbacks are listed below.

• Privacy Issues:

  Personal information contained in data can get leaked.

• High Cost:

  Data harvesting, processing, and storage incur high costs.

• Complexity of Data:

  The collected data has to be processed before use.

• Misuse of Data:

  Companies can sell user data, which can later be used for phishing attacks.

• High-skill Requirements:

  Processing data requires skills such as big data analysis and data mining, which are not readily available.

Also Read: Data Science Course Eligibility Criteria: Syllabus, Skills and Subjects

Who Uses Data?

Data is used by both individuals and organizations working in diverse fields. All leading organizations have established units focused on data harvesting and analysis.  

The usage of data in industries and its specific role are tabulated below. Have a look.

Also Read: Basic Fundamentals of Statistics for Data Science

How to Start Learning Data?

Learning data science is more straightforward than expected. You don't need an engineering degree to start. All you need is determination and dedication.

However, securing a job in data science without proper training is difficult. You will have to earn appropriate certification before applying for your first job. UpGrad offers a variety of data science certification courses that cater to different levels of experience and career goals. 

Check out some of the popular courses in data science offered by UpGrad.

• Masters Degree in Artificial Intelligence and Data Science
• MS in Data Science
• Post Graduate programme in Data Science and AI
• Post Graduate Certificate in Data Science and AI
• Business Analytics and Consulting with PWC India
• Professional Certificate Programme in AI and Data Science

You can start learning programming languages that are important for data science. Here are some of the essential programming languages used in data science.

• Python
• SQL
• Java
• Scala

You can also start studying on your own using popular books on data science as they will help you strengthen your knowledge before taking up any certification course. 

Some of the top data science books are tabulated below.

Also Read: Data Structures and Algorithms Free Course

High Paying Job Roles After Learning Data Basics

The data science market is set to witness a compound annual growth rate (CAGR) of 27.7% from 2021 to 2026. The US Bureau of Labor Statistics says that there will be a 36% rise in data science-related jobs from 2023 to 2033 in the country. In fact, data scientist positions are among the fastest-growing jobs in 2024.

Given the role of data science across industries, individuals proficient in data processing and interpretation are highly sought after. In today’s competitive world, data science skill sets offer you a successful career path.

Now that we know data science is in high demand, it’s time to take a look at the jobs that use data science, and their approximate salaries.

Source: Glassdoor

Conclusion

Data from a reliable source is a potent tool to understand your surroundings. Through data science, you can make well-informed decisions that address pressing challenges and enhance the quality of human lives. While data science has undeniable advantages, it is necessary to acknowledge and address potential drawbacks. 

A career in data science will open you up to endless possibilities. Utilize this opportunity to upskill yourself using the best certification courses from UpGrad.

Fast-track your career by taking data science courses from the world’s top universities.

Explore our top-rated Data Science courses in the table below, crafted to guide you from beginner to expert with hands-on projects, real-world applications, and insights from industry experts!

Unlock the must-have Data Science skills in the table below—these foundational and advanced skills will empower you to tackle real-world data challenges and stay competitive in this rapidly growing field!

Browse our popular Data Science articles below—each article brings you cutting-edge insights, practical guidance, and the latest developments to deepen your expertise and keep you informed in the field!