Data Analytics

What is Qualitative Data, and Why is It Still Critical in the Digital Age?

Written by Karin Kelley
|
Updated on September 24, 2024

Data analytics has emerged as a go-to technology for all organizations looking to better understand their user base. This technique has made strides by allowing the organization of collected data and identifying trends. Qualitative and quantitative data are the two main forms of data used for data analytics. While quantitative data is the most common type, qualitative data presents unique opportunities.

This article serves as a guide to the concept and types of quantitative data. You will learn how to identify, collect, and process qualitative data. For a more in-depth study, we recommend an industry-recognized data analytics bootcamp for those who want to build a career in this field.

What is Qualitative Data?

Qualitative data refers to non-numerical data. It includes characteristics, approximations, opinions, and properties. It can be collected by observation, sentiment analysis, and specific descriptive words. Qualitative data is also called categorical data, as it enables the categorization of a phenomenon according to its features.

Here are a few examples of qualitative data:

The children are happier when playing outside
The man is exceptionally tall
How happy are you with the food? not happy, average, will return…
She progressed in her career due to her excellent communication skills
Hybrid work provides a better work-life balance

Also Read: Exploring the Impact of AI in Data Analytics

Why is Qualitative Data Important?

Qualitative data helps gauge elements that cannot be quantified but are critical to gaining an overall picture of the situation. For example, a banking website may receive an average customer satisfaction rating of 4.5. Still, a comment like “I wish there were easier options to access fixed deposit accounts” would provide more context on why the rating is not 5. Understanding the customer’s ideas, experiences, and expectations is critical to enhancing the customer experience.

Moreover, every person has a unique problem that AI chatbots cannot always solve. In such a case, qualitative data comprising their requirements in words, rather than simple clicks on the given options, would facilitate a quick resolution.

Finally, similar traits, features, or sentiments may exist regarding a specific situation or group. For example, veterans may be more likely to experience post-traumatic stress disorder syndrome. Here, the words ‘more likely’ indicate an inference drawn from qualitative data collection.

Types of Qualitative Data

Qualitative data is categorized into three types to facilitate the analysis, such as:

Nominal Data

Data given a label or name but not a numerical value is called nominal data. It is also called nominal scaled data or name-labeled data. Such data helps identify statistically significant variations in datasets. It forms groups of observations based on categories. However, these categories cannot be ranked as they are separate and cannot be compared.

Ordinal Data

Ordinal data categorizes observations into groups that can be compared and ordered based on their characteristics. The ranking of the groups holds prime importance here, although the distances between the groups may not be equal. Bar charts and line charts are used to represent ordinal data.

Binary Data

This additional type of qualitative data uses a combination of zeros and ones to represent it. Binary data is typically used to indicate the likelihood of a two-result event, such as yes or no, positive or negative, etc.

Also Read: What’s the Difference Between Classification and Clustering, and What About Regression?

Qualitative vs. Quantitative Data

While qualitative data may have some numerical components, some features distinguish them from quantitative data. Let us look at some of them.

Qualitative data	Quantitative data
Helps understand the reasons for or manner of any occurrence	Helps calculate the frequency and severity of the occurrence
It is descriptive and gives priority to the emotional aspect or uncountable characteristics.	It is countable and considers aspects that can be measured in numbers.
It is subjective and requires personalization and context.	It is absolute, fixed, and universally understood as the context is implicit.
Classification, topics, and categories are used for analysis.	Statistical analysis is the main method of analysis.
Uses observations, interviews, and emotion-based questionnaires to gather information.	Employs measurements and quantitative surveys to collect data.

What is Qualitative Data, and How Can You Identify It?

Due to its subjective nature, qualitative data may feel confusing in certain scenarios and seem the same as quantitative data. Here are some of its characteristics to help you identify qualitative data.

First and foremost, it is not numerical. It requires words, images, and descriptions to clarify its meaning
It is more difficult to organize due to its open-ended and subjective nature
You may need to use new classifications to categorize the data
Identifying patterns in the data is more complicated due to its non-numerical nature
It includes viewpoints or experiences, such as ease of use of an e-commerce website
Qualitative data dives deeper into the emotional aspect of the answer, such as the trending sentiment words on X (formerly known as Twitter)
It requires detailed interviews to understand individuals’ feelings, motives, ideas, and thought processes
Anecdotal stories, individual requirements, and specific needs of the individuals form a part of qualitative data

Also Read: A Beginner’s Guide to Data Analytics in Finance

Methods of Collecting Qualitative Data

The data collection methods for qualitative data require more personal interaction than those for quantitative data. Here are some collection methods you can choose from.

Focus groups: Ask a small group of people (5-10) a question and observe their discussion. Record the viewpoints, answer any questions they may have, and encourage deeper insights into their requirements. A focus group on a new perfume to be released to the market is an excellent example of how you can discern specific concerns and feedback.
Observations: You may not interact with the individual and only observe the person’s behavior. For example, some gaming software asks the testers to play the games while on a video camera so that they can observe the specific emotions while the testers navigate the various features.
Interviews: Where you ask the individual questions and note their responses, tone of voice, and how quick or slow the response was. Interviews are helpful when dealing with issues such as investigating a factory incident when the workers are more comfortable answering questions without anyone else around.
Case studies: These involve examining the context of the issue and analyzing the problem from all aspects. These are useful for unique or difficult situations.
Surveys: You can ask individuals to fill in open-ended questions with their feedback. For example, you may ask, “Are you satisfied with the product? If not, please tell us why.” The individual may write the details of their problem in the space below the question. You can analyze the answer for the feedback and the implied emotion.
Audio and video recordings: This method enables you to delve deeper into a person’s feelings that may not be expressed clearly in written form. They also help detect verbal clues and tone.
Text analysis: This method is crucial in assessing social media for trending issues. It looks for specific phrases, sentiments, and user demographics.

Qualitative Data Analysis Steps

Once you have collected qualitative data, you can analyze it using the following steps:

Systematize your data by first converting it into text format. This will make the unstructured data usable.
Arrange the data into categories based on your research objectives. Even if the data set may seem small, it is always easier to work on multiple ones to avoid losing focus. After analyzing the data in each subset, you can derive larger insights.
Use data coding to set codes, assign characteristics, and discern patterns to the data.
Validate your data by ensuring it does not contain errors. This step ensures your method and results are reliable and accurate.
Create a report and present your findings based on the analysis, recommendations, and future scope.

Also Read: Exploring Data Analytics for Marketing and Why It’s Critical

When to Use Qualitative Research?

Qualitative research is most often used when starting a study. You do not know what you may find, so it is best to gain an overall perspective by assessing different aspects. Qualitative data allows you to look at the context to understand newer things better.

Further, you can use qualitative data when you find the inferences from quantitative data do not match the results obtained. For example, the sales figures of a website are falling despite the higher number of visitors. So, you can conduct qualitative research to assess what happens when visitors navigate the website and why the sales are not completed.

Finally, qualitative research is immensely useful when dealing with topics affecting the human mind. For example, the impact of natural disasters on livelihood may bring issues like trauma to the forefront only when interviews and observations are conducted. Otherwise, you will only use quantitative data to determine how many lost or regained their livelihoods and homes.

Advantages and Disadvantages of Qualitative Data Analysis

Like any other data type, qualitative data has unique pros and cons. Let’s examine how to employ this data form.

Advantages

It helps gauge the thought process and thinking patterns
It facilitates a detailed analysis of the issues
Comprehension of customer language and terminology helps with new product requirements
The data can be assessed from multiple angles to gain varied insights due to its subjective nature

Disadvantages

The results of data analysis are restricted to the group studied and cannot be generalized
The data collection is tedious and may remain incomplete if the individuals do not provide the required information
Accuracy and recording of the data depends on the abilities of the researcher. If the researcher cannot understand the patterns or sentiments in the text, the data will remain unused.

Also Read: What Is Data Ethics? Principles, Examples, Benefits, and Best Practices

Explore Exciting Career Paths in Data Analytics

Qualitative data research is a critical part of data analysis. It brings the human component to data analytics by delving deeper into the emotional aspect of the feedback and information received from the individuals under study.

By providing appropriate context, it supports and improves the inferences drawn from the associated quantitative research. Hence, aspiring data analytics professionals must understand how to incorporate qualitative data analysis into their projects.

This data analytics program provides comprehensive training in all the essential aspects, such as data acquisition, SQL, ETL tools, visualization, and data mining, while teaching you how to use Python, R, Power BI, and statistical techniques for efficient data analysis. You will also be able to strengthen your practical knowledge through capstone projects.

Ready to advance your career in data analytics? Enroll today!