Data helps people, organizations, and governments establish baselines, measure performances, and eliminate guesses. The data we create exists in various formats.

- Structured and unstructured data (1)
- Discrete and continuous data (2)
- Nominal and ordinal data, and more.

This article explores two other data types: quantitative and qualitative data. You’ll learn what they mean and their applications, similarities, differences, and features. We’ve created more data in the last two years than in the entire human history (3).

Researchers estimate that every human is creating about 1.7 megabytes of data—quantitative and qualitative data every second. Let’s explore this data type, starting from their similarities and differences.

**Quantitative vs Qualitative Data: Similarities and Differences**

Before diving into their similarities and differences, let’s clear a few things first.

**What Is Quantitative Data?**

Quantitative data is a quantifiable variable—it deals with numbers and statistics.

This data type measures variables using numbers or based on their quantity instead of their qualities or attributes. Quantitative data is typically number-heavy and forms the bedrock of quantitative research.

Researchers rely on this research method to test or confirm theories, hypotheses, and assumptions, and its findings are usually objective, grounded, and a generalized fact.

The United States national census data is quantitative; it’s number-heavy, quantifiable, and conclusive. People can quickly analyze the data using systematic approaches.

They could use percentage to find the proportion of males and females in the population, mean or median to find the central tendency of the age distribution, or standard deviation to examine the spread or variability.

They could also present the population figures in a time series graph to learn how the population is growing or forecast future growth.

Other examples of quantitative data are:

- The number of students in college
- The percentage of males in New York City
- The total volume of water in the world
- The temperature of a room
- The financial statement of a company

**What Is Qualitative Data?**

Qualitative data is non-numerical data.

This data type measures and deals with variables based on their description, attributes and distinct qualities rather than on their quantities. Quantitative research purview lies in collecting information that helps describe things than measure them.

It’s often text-heavy and usually involves first-hand observation.

Qualitative data is generally unstructured, and it helps researchers gain more profound insight into difficult to understand topics. Its findings are often subjective and not usually suitable for conclusive generalization.

The social media posts of a company’s customers are an example of qualitative data. The data is word-heavy, subjective, and could be challenging to analyze using mathematical and statistical methods.

Other examples are:

- Online product reviews
- Multimedia files
- Open-ended surveys
- Opinions polls
- Interview transcripts
- Observations and notes

**Quantitative vs Qualitative Data Similarities**

Here are the few areas quantitative and qualitative data are similar:

- The data types are both helpful for gaining valuable insights.
- Researchers can collect quantitative and qualitative data using survey forms.
- They help forecast the future.
- Users can classify the two data types for easy analysis.
- Quantitative and qualitative data have wide use cases.

**Quantitative vs Qualitative Data Differences**

Quantitative and qualitative data differ in several ways. Let’s quickly explore them:

- Quantitative data is typically numerical, while qualitative data are often non-numerical.
- The numerical nature of quantitative data makes it easily quantifiable, but qualitative data are not. It places much emphasis on describing data than quantifying them.
- Qualitative data works best for formulating hypotheses, and quantitative data helps validate them.
- Quantitative data makes sense on its own, while qualitative data exists in raw and native format and could require processing to become valuable and insightful.
- Quantitative data is objective, while qualitative data is subjective, making the latter valid for generalization.
- Researchers can use statistical and mathematical methods and tools to analyze quantitative data, but these methods and tools don’t often find applications in qualitative research. This property makes quantitative data intuitive and easy to analyze.
- Quantitative data is structured data, while qualitative data is unstructured. The latter also exists as discrete and continuous data.
- Data collectors use close-ended survey questions to collect quantitative data, while close-ended survey questions work best for qualitative data.
- Quantitative data is easy to present on graphs, tables and spreadsheets, but qualitative data is not.
- Databases and data warehouses are great for storing and managing quantitative data, while data lakes work for qualitative data.
- Quantitative data is more readily available than qualitative data.
- While qualitative data relies heavily on expert opinions, which are susceptible to biases to make forecasts, quantitative data forecasts are often science-backed.
- Unlike qualitative data, researchers can easily interpolate quantitative data—estimate unknown values in a time series. For instance, a business that lost part of its sales records might use interpolation to estimate them.

**Advantages and Disadvantages**

What are the advantages and disadvantages of quantitative and qualitative data? Here’s what we found.

**Quantitative Data Advantages**

Here are some of the advantages of quantitative data:

- It exists in predefined formats, making it intuitive to work with, store, and easily search for in a database.
- Analysts can analyze them using statistical and mathematical techniques and tools.
- It helps in validating hypotheses.
- Quantitative data is objective and is often non-tinted with human emotions, making it useful for generalization.
- Its forecasts are more accurate, and researchers can find unknown data values by interpolating the data.
- Researchers can present the data using graphs, making it more straightforward for people to understand without overwhelming them with figures or information.
- It helps people to make data-driven decisions.
- Researchers can use quantitative variables to find the relationships between variables.
- It’s compatible with most business intelligence tools.

**Qualitative Data Advantages**

Here are some of the advantages of qualitative data:

- It’s very abundant.
- Qualitative data are more insightful by a long shot.
- Qualitative surveys help researchers better understand social problems.
- Businesses can mine qualitative data to improve their processes.
- It helps formulate hypotheses.
- Qualitative data exists in its native format, making it easily adaptable for broad uses.

**Quantitative Data Disadvantages**

Here are some of the disadvantages of qualitative data:

- Quantitative is less abundant than qualitative data.
- Researchers can’t rely on it to formulate hypotheses.
- Quantitative data exists in a defined format, making it less adaptable.
- It provides limited insight.

**Qualitative Data Disadvantages**

Here are some of the disadvantages of qualitative data:

- It costs much to mine qualitative data.
- The data requires much effort to analyze.
- Qualitative data requires larger storage space.
- It’s not compatible with most business intelligence tools.
- Qualitative data are often subjective, making them less fit for conclusive generalization.
- It’s often challenging for researchers to interpolate qualitative data.
- It’s not easily feasible to present qualitative data on graphs or tables.
- Qualitative research cannot validate hypotheses.

**Quantitative vs. Qualitative Data: Side-by-Side Comparison**

How do quantitative and qualitative data compare side-by-side? Let’s examine that.

**Key Characteristics**

Quantitative and qualitative data are easily identifiable by their key features. Here, we’ll explore them.

**Quantitative Data Key Characteristics**

Here are some of the defining key characteristics of quantitative data:

- Quantitative data is often in hard numbers.
- The data deals with variables based on their quantities rather than attributes.
- Quantitative data is easy to measure.
- It’s straightforward to analyze using both mathematical and statistical techniques.
- Quantitative data is objective.
- Researchers use the data to make a conclusive generalization.
- Analysts can extrapolate the data to forecast the future.
- Quantitative data can show relationships between two variables on a scatter plot.
- It is easy to store in relational and SQL databases and data warehouses.

**Qualitative Data Key Characteristics**

Here’s a quick rundown of some of the key features of qualitative data:

- Qualitative data deals with variables based on their attributes rather than quantities.
- It’s hard to analyze qualitative data.
- Qualitative data are subjective and biased. Hence, it’s not suitable for conclusive generalization.
- The data is usually in words, and it’s difficult to express them in graphs.
- Qualitative data often requires processing to provide meaningful insights.
- Qualitative data is the most abundant data type today.
- Data lakes or non-relational databases are the best places to store qualitative data.

**The Key Takeaway**

The critical defining feature of quantitative data is that it’s heavily composed of numbers, while qualitative data are often in text.

**Data Structure**

Structured or unstructured data? Here’s what we found.

**Quantitative Data Structure**

Quantitative data usually exists as structured data.

Structured data is a data type with a precisely defined format. It’s easy to organize the data in tables, into rows and columns, and with a clear relationship between them. According to most estimates, about ten to 20 percent of all globally generated data is available as structured data.

From the above stats, we could safely say quantitative data makes up about ten to twenty of the global data sphere.

**Qualitative Data Structure**

Qualitative data rarely has predefined formats and exists in raw form, making the data unstructured.

Researchers believe unstructured data accounts for 80 to 90 percent of the global data sphere, growing at 62 percent annually. They predict that by 2020, unstructured data will account for 93 percent of all data.

Its overwhelming data volume makes it more insightful than structured data when mined for insights, data-driven decisions, or competitive advantage.

The global big data analytics industry generated about $42 billion in 2018. Analysts project the revenue will grow to $103 billion in 2027 with a CAGR of 10.5 percent—these numbers show how heavily businesses are willing to pay for qualitative data analysis.

Netflix saved about $1 billion by mining and utilizing qualitative data insights. The Entrepreneur found that businesses that use BDA grow profit by eight to ten percent (4).

**The Key Takeaway**

Quantitative data is often structured, discrete and continuous data, while qualitative data is generally unstructured.

You could learn more by checking out our article on structured vs. unstructured data (5).

**When to Use **

Quantitative and qualitative data work hand-in-glove to enable researchers to carry out thorough and conclusive research. But when is the best time to use any of the data types?

Let’s quickly explore that.

**When to Use Qualitative Data**

Qualitative research helps people gain insight into topics that are difficult to understand, making it helpful in formulating hypotheses.

For instance, a brand feeling concerned about why it’s losing its customers to a new competitor might survey them to learn why they switched brands. Analyzing the survey results might give them insights into the possible causes, enabling them to formulate hypotheses.

A hypothesis is an assumption made based on some evidence. It’s not conclusive and requires testing to become valid.

**When to Use Quantitative Data**

Quantitative data helps researchers obtain objective and conclusive results, making it best suited for validating hypotheses. It uses statistical and mathematical techniques to validate the assumptions.

Quantitative research is always helpful in making some sense out of qualitative data.

The social media posts of a company’s customers are always text-heavy, could be overwhelming, and often tricky to analyze using mathematical and statistical methods.

Data analysts could sort them into categories—like sentiments (happy, sad and indifferent) or any other categories and use quantitative research methods to analyze them.

They could use percentage or fraction to find the proportion of their social media customer base that’s happy, sad, or indifferent, or use bar or pie charts to represent the data to make it intuitive.

**The Key Takeaway**

Qualitative data helps in formulating hypotheses, while quantitative data help validate them.

**Data Collections and Sources**

Last year, IDC predicted 75 percent of enterprises would rely on new and external data sources to enhance cross-functional decision-making capabilities in 2021 (6).

Where are the possible places businesses can collect and source data?

**Quantitative Data Collections and Sources**

Here are some of the sources of enterprise quantitative data:

- Financial statements
- Web traffic data
- Close-ended surveys
- Sales data
- Point-of-sale (POS) terminals
- Department of Labor datasheet

**Qualitative Data Collections and Sources**

Here are some of the sources of quantitative data:

- Online customer reviews
- Interviews and focus groups
- Social media posts
- Open-ended surveys
- Phone calls
- Emails
- Case studies
- Expert opinions
- Online forms
- Online using website crawlers or keywords extractors
- Company reports

**The Key Takeaway**

Most qualitative data are highly subjective.

The data often have some tint of emotions, personal taste, and sometimes biases—the tendency to lean in a certain direction, either in favor of or against a specific thing. For instance, political leaning could influence a citizen’s review of the president’s anti-crime policies.

Quantitative data are often objective. Quantitative analysis of the crime rate will give a more accurate picture of the president’s anti-crime policies’ effectiveness.

**Data Forecasting**

How easily can researchers rely on the data to forecast the future?

**Quantitative Data Forecasting**

Data analysts can easily forecast the future with quantitative data.

Quantitative researchers rely on patterns, pattern changes, historical data, time-series data, and relationships between variables to make forecasts. For instance, a marketing department might use historical data of revenue growth to predict next year’s revenue.

Quantitative data forecasts are objective, more accurate and reliable. Of course, because it uses systematic forecasting techniques, mathematical and statistical methods to make forecasts.

**Qualitative Data Forecasting**

Qualitative research often relies on expert opinions to make forecasts.

For instance, a doctor might predict a patient might get well in two to three weeks. The forecast relies on his experience, personal opinions, instincts, and not on hard facts, making it less reliable.

Qualitative researchers could also rely on images, scans, videos and sensors to make predictions.

**The Key Takeaway**

Quantitative research relies heavily on science to make forecasts, making it relatively more reliable than qualitative data forecasts.

It’s conclusive, and researchers could use them for generalizations and far-reaching decision-making.

- The government uses population growth forecasts to prioritize its policies and allocate resources more effectively.
- Businesses also rely on revenue and sales forecasts to benchmark performances.
- Metrologists use both qualitative (satellite images, remote sensors) and quantitative data (air pressure, wind speed, temperature and humidity) to forecast weather.

**Survey Questions Type**

Survey forms help collect qualitative and quantitative data. But how do data collectors typically frame the survey questions for both data types—close-ended or open-ended?

Here’s how they compare.

**Quantitative Data Survey Questions Type**

Quantitative research survey questions are typically closed-ended.

In close-ended surveys, respondents answer the question by selecting from a list of predefined options or rating scales like (for example, strongly agree, agree, strongly disagree, disagree).

This type of survey question is always clear to respondents, easy to answer, and provides researchers with consistent data to analyze.

Here are some examples of close-ended questions:

- Will you recommend this product? (Yes, No)
- How does our price compare to other sellers? (Higher, the Same, Lower)
- How do you rate the president’s anti-crime policies? (Very Effective, Effective, Less Effective, Ineffective)

**Qualitative Data Survey Questions Types**

Qualitative research questions are open-ended.

This survey question type doesn’t require a simple yes or no answer or selection of any multi-choice option but requires respondents to provide details or elaborate their points.

They’re always broad, take time to fill and enable analysts to see things from the respondents’ perspectives. The data is difficult to analyze unless it’s processed or sorted into measurable categories.

Open-ended questions help researchers gain profound insight into problems and in formulating hypotheses.

Here some of its examples:

- Why did you choose our products?
- How do you feel about our price?
- What do you think of the president’s anti-crime policies?

**The Key Takeaway**

Qualitative research questions are close-ended, making them more accurate, objective and easily quantifiable. In contrast, qualitative research questions are open-ended, making them susceptible to personal bias, less accurate, and challenging to measure.

**Data Analysis Methods**

How can researchers analyze quantitative and qualitative data for insights? Here’s what we found.

**Quantitative Data Analysis Methods**

Researchers often measure quantitative data using statistical methods.

Regression helps them to find the relationship between two variables. It seeks to measure how changes in one variable affect the other.

For instance, how marketing budget changes affect sales or how improved customer service funding impacts customer retention.

Researchers could also analyze quantitative data by grouping them.

For instance, the student affairs office could analyze the college population by classifying the students into males and females.

Other quantitative data analysis methods include:

- Frequency distribution (for finding the number of times a variable occurs in a dataset)
- Spread and dispersion analysis (for measuring the variability of data from its central value, for examples, how students’ math scores deviate from the class mean)
- Mean and median ( for measuring the central tendency of a dataset)
- Charts and graphs (for easy visualization of data)
- Fractions and percentages (for measuring the proportion of a dataset to the whole)

**Qualitative Data Analysis Methods**

Data sorting and classification is seemingly the most intuitive way to analyze qualitative data. Researchers can also use word clouds to analyze the qualitative data.

Word clouds are graphical representations of word frequency in a source text. It gives prominence to words that appear more frequently.

Here’s a word cloud analysis of this article.

Some other qualitative data analysis methods include:

- Data stacking (for declustering, grouping, and classifying big data)
- Data mining (for analyzing big data)
- Keyword extractions (analyzes the keywords in a text)
- Sentiment analysis (it’s helpful in opinion mining for determining whether data is positive, negative or neutral)
- Natural language processing (the ability of machines to read a text like humans)
- Audio to text processing (converts audio to text for further analysis)

**The Key Takeaway**

Quantitative data analysis uses numerical methods to analyze data, while qualitative data analysis methods are non-numerical.

The former often rely primarily on statistical analysis software like Minitab, SPSS, Stata, and others. Big qualitative data are easily analyzed using data mining software like Xplenty, RapidMiner, Oracle, and others.

Some other software for analysis for analyzing qualitative data are:

- Keyword extractors
- Word cloud generator
- Online polls software

**Data Presentation**

How are the best ways to present qualitative and quantitative data to the public?

**Quantitative Data Data Presentation**

Here are some of the best ways for presenting quantitative data:

- Pie charts
- Bar charts
- Histograms
- Scatter plots
- Line graphs
- Tables

**Qualitative Data Data Presentation**

Text and word clouds are often the best ways to present raw and unprocessed qualitative data.

**The Key Takeaway**

Quantitative data is numerical, so presenting it on graphs is intuitive, but this is tricky for raw qualitative data unless it’s processed.

Word cloud is best for presenting surveys, customer product reviews, or social media posts’ sentiments.

Researchers can only present them in graphs after sorting and counting the number of times they occurred. Calculating the percentages enables them to know the proportion of their happy, sad, or indifferent customers—if that’s the categories they sorted the sentiments into.

**Use Cases**

In what areas do quantitative and qualitative data find applications?

**Quantitative Data Use Cases**

Let’s take a quick look at five:

- Researchers use it to validate hypotheses
- Analysts use historical quantitative data to forecast the future
- Marketers use quantitative market research to gain objective and unbiased insights into the market.
- Governments rely on quantitative data for population census
- Academic institutions use quantitative data to compute students’ academic performance.

**Qualitative Data Use Cases**

Here are some qualitative data use cases:

- Researchers use it to gain insight into problems and formulate hypotheses.
- Governments use qualitative research to understand public opinion.
- Marketers use it to understand customer feelings about a product or new price.
- Businesses mine qualitative data to improve customer experience, grow profit, reduce cost, entrench operational efficiency and make better decisions.
- eCommerce marketers use qualitative data to provide a personalized shopping experience.

**The Key Takeaway**

Quantitative data finds practical applications when the intent is to arrive at a conclusive generalization, while qualitative research helps people gain more profound insights.

Qualitative data is more insightful than quantitative data.

**Quantitative vs. Qualitative Data: Wrapping It Up**

Just like other data types, quantitative and qualitative data are not in competition.

They work together to enable researchers to carry out research that’s both insightful and valid, and both have their distinct roles. Qualitative research digs into the problem to formulate the hypotheses, while quantitative validates them.

So, there’s no clear champion—the two data types are winners.