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Hard Data vs. Soft Data: The Difference

Hard Data vs. Soft Data
Maryia Stsiopkina

Maryia Stsiopkina

2022-05-257 min read

Whether a business owner or an individual, you’ll probably admit that knowledge, information, and data are just as essential to your survival as food, water, and other necessities. And for businesses, it’s not just any data but smartly selected and properly extracted pieces of information that define what further actions a company will take to grow and flourish.

To avoid getting lost in the multiple types of data out there, it is traditionally divided into two major categories: hard and soft data. There are many misconceptions and myths surrounding the hard data vs. soft data debate, so let’s clarify things.  

In today’s blog post, you’ll learn what hard data is, what criteria you can use to define it, and some examples. Then, we’ll go over soft data, its peculiarities, and its importance. Finally, you’ll see the key differences between the two data types and learn the best way to harvest them. Let’s dive in.

What is hard data?

Sometimes, the distinction between hard and soft data may be seen as vague, however, there are still specific attributes that define hard data. Let’s briefly describe hard data before delving into the details.

Hard data, also known as factual data, is proven and methodologically acquired infromation taken from official or organizational sources that are corresponding and almost independent in the ways they were measured.

First of all, hard data is always based on facts and quantifiable results coming from reliable and valid sources. This sort of data is essentially retrospective, meaning that valid and provable results can only be achieved over a period of time. Hard data is normally presented in numbers, tables, and graphs.  

When gathering hard data, you should follow a profound research methodology and strict rules. There are two hard data collection methods: secondary and primary

Secondary data collection

Secondary data collection involves pulling information from credible sources related to the area of your interest, such as books, newspapers, journals, scientific reports, and others. Since there are many such sources, you should establish strict criteria to select secondary data. It plays a crucial role in how valid and reliable the gathered data will turn out to be. 

You may set a list of criteria that will include the date of publication, author credentials, reliability of the source, the impact it had on the area of your interest, and other parameters that you deem valuable for your study. 

While secondary data collection is less time and effort-consuming, it doesn’t produce any fresh and unique data, thus not contributing to the research expansion to a significant extent.

Primary data collection

On this note, we’re approaching the primary data collection method. In simple words, primary data is the unique findings of your study.

This data methodology relies on quantitative analysis and mathematical calculations. Some methods of quantitative data gathering and analysis are questionnaires with closed-ended questions, regression analysis and correlation techniques, and others. 

Since the methods used to collect hard data are purely scientific for the most part, there’s barely any room for bias and subjective interpretations of the results. In other words, only indisputable facts are dealt with. Due to a high level of standardization of quantitative methodology, it’s way easier to generalize the results and compare findings.  

Why is hard data important? 

Now that we’ve discussed hard data and its collection methods, let’s look at why it is important and what purposes it serves. Since hard data generates actual and viable results, usually covering an extended period of time, it’s a solid ground for statistical analysis, optimization, and medium or long-term forecasting

For instance, you might want to explore the stock market performance over the last year to predict its development trends for the future. For this, you can scrape hard data from related platforms scouting for specific numbers and statistics. This information will give you insight into how the stock market was developing during the set time frame, however, it won’t explain why it was behaving the way it did. It prompts us to turn to additional data sources, such as soft data. 

hard data for long-term forecasting

Hard data is used for statistical analysis, optimization, and medium or long-term forecasting.

What are hard data examples?

The main hard data examples can be defined by the means it was gathered and the sources it was derived from. Therefore, we can identify two main categories: technology-generated data and data gained via methodological research. 

Technology-generated data

Increasingly, data generated by applications and technological devices is becoming the dominant type of hard data. We might even say that this is hard data in its purest form since it can easily be traced back to the source, measured, and verified. Technology-generated data can be gathered across mobile applications, phones, computers, smart meters, call records, traffic monitoring systems, bank transaction details, and many more. 

Data gained via methodological research

Another example of hard data is the information collected in the course of scientific methodological analysis. Such data can be gained through telephone calls, polls, controlled experiments, surveys, etc. 

Here, it’s important to note that hard data can only provide answers to the who, when, and what questions implying concrete answers while totally omitting the reasoning behind them. Another crucial aspect of quantitative research is that the sample you intend to study must be representative enough to ensure that your selection portrays a larger group of people.

What is soft data?

Now that we’re settled with the definition of hard data let’s compare it to soft data.

Soft data is usually described as subjective data lacking the precision of hard data. It normally results from semi-scientific methods, such as those lacking formal randomized sampling and conditions or those based on myth or rumor. Soft data is mostly descriptive and is used to interpret hard data.

Unlike hard data, soft data is qualitative and doesn’t follow a typical research process. Soft data is based on sentiments, opinions, impressions, assumptions, and interpretations - in other words, on the things typically ascribed to humans. It’s almost impossible to measure it or quantify it in actual numbers. And for this reason, soft data has a bad reputation of being not completely trustworthy. 

However, even despite the lack of scientific evidence, soft data is commonly used to complement hard data to get the complete picture. Due to the personal nature of soft data, it helps businesses gain a deeper understanding of their customers’ actions, motivations, needs, and reactions. This, in turn, contributes to building an optimal strategy on how to interact with clients and meet their expectations. Therefore, combined with hard data, soft data plays a crucial role in strategic planning. 

soft data for strategic planning

Soft data plays a crucial role in strategic planning.

What are soft data examples?

The ways in which soft data can be gathered are very similar to the ones of hard data, however, with certain peculiarities. Overall, there are two primary examples of soft data according to the sources and collection method. They are data gathered via focus group studies and online-generated data. 

Soft data gathered via focus group studies and interviews

Similar to the hard data produced in the course of methodological analysis, soft data relies on analogous techniques and methods like interviews and focus groups. However, the drastic difference lies in the type of information being collected. Instead of getting factual results, the process is based on open-ended questions. It involves gathering opinions, ideas, sentiments, assessments, experiences, and other subjective information that can be neither proved nor disproved. 

Due to these characteristics and their very personal nature, the results of such findings can’t be generalized and considered representative in any way. 

Online-generated soft data

Online-generated soft data includes feedback, product reviews, customer satisfaction, and other forms of online content mainly generated by internet users and customers. Combined with sentiment analysis, this information can be handy as valuable insights into your customer preferences and needs may be drawn from this data. 

Hard data vs. soft data: The difference explained

When comparing hard data vs. soft data, we can distinguish five critical parameters based on which we can define the type of data in question. They are research questions, type of information gathered, sources, generalization capacity, and application. Let’s look at the hard data vs. soft data distinctions in more detail. 

Research questions

As we discussed, one of the differences between soft and hard data lies in the nature of the questions asked. While hard data implies close-ended questions requiring concrete and factual answers, soft data deals with the reasoning and in-depth explanations behind the former.

Type of the information gathered 

As it follows from the previous paragraph, the nature of the questions defines the material gathered in the course of study. In the case of hard data, we deal with facts that can be scientifically and mathematically proven and measured. When it comes to soft data, we work with opinions, sentiments, interpretations, and other subjective matters. 


Hard data may be technology-generated, meaning it comes from applications and technological devices and is also collected in quantitative research. On the other hand, soft data is gained via qualitative analysis, or else it originates in online sources, such as reviews, polls, customer feedback, etc. To successfully grasp data at scale from that many sources, you need to implement Web Scraper API.

Generalization capacity

If gathered using proper research methods, the conclusions and findings drawn from hard data can be easily generalized and deemed somewhat representative. Since most of the time it consists of personal opinions and sentiments, soft data is hard to generalize.


For all the reasons mentioned above, hard and soft data serve different purposes. While hard data, based on dry numbers and mathematical calculations, can be used for pretty accurate statistical analysis, it’s helpless to explain deeper reasons and motifs behind particular facts and events. And at this stage, we need soft data to perform an in-depth contextual analysis and answer the question of why.

The main differences between hard and soft data are conveniently summarized in the table below. Hard data vs. soft data:

Hard data Soft data
Research questions Close-ended questions. Who, what, when? Open-ended questions. Why?
Type of information gathered Facts that can be proven and measured. Opinions, interpretations, sentiments, etc.
Sources Quantitative research, technology-generated factual data. Qualitative interviews, polls, case studies, online reviews and feedback.
Generalization capacity It can be easily generalized. Hard to generalize.
Application Statistical analysis, optimization, medium and long-term forecasting. Contextual analysis, strategic planning.

Automating data gathering with web scraping

As we noticed, a huge portion of hard and soft data is technology and online-generated. While traditional scientific research methods are not always straightforward to implement in a business setting, web data is a true goldmine available to everyone.

However, the versatility and volumes of hard and soft data on the web may be overwhelming and hard to navigate, let alone collect and analyze it. In this case, automated data collection, also known as web scraping, comes to the rescue. 

Automated data gathering solutions, such as Scraper APIs, can effectively crawl search engines and web pages in real time and extract data according to the set parameters. 

Examples of hard data that you can collect with Scraper APIs include price information, product position, URLs, pagination, etc. And when it comes to soft data, you can easily scrape product reviews and customer feedback. 

Scraper APIs are covered with cyber insurance and packed with valuable features that help extract data efficiently:

  • Integrated proxy rotator. IP rotation ensures a high scraping success rate and smooth data retrieval. 

  • JavaScript rendering. Due to this feature, you’ll be able to extract hard and soft data from the most challenging targets, including JavaScript-heavy websites. 

  • Convenient data delivery. You will get the parsed data directly to the storage space of your choice. 

Proper web scraping tools make it easy to combine two data streams, i.e., hard and soft data, to build an accurate picture and make well-informed business decisions. 


Hard and soft data are two essential data streams that perfectly complement each other when it comes to business data analysis. While hard data, based on precise mathematics and calculations, is a solid ground for statistical analysis and forecasting, soft data, bearing a human touch, is the common thread between companies and their customers. It provides valuable insights into their motifs and behavior, helping to shape commercial strategies beneficial for businesses and their clients. 

If you found the topic of hard data vs. soft data interesting, you can check another related article about data-driven decision-making for price intelligence.

About the author

Maryia Stsiopkina

Maryia Stsiopkina

Senior Content Manager

Maryia Stsiopkina is a Senior Content Manager at Oxylabs. As her passion for writing was developing, she was writing either creepy detective stories or fairy tales at different points in time. Eventually, she found herself in the tech wonderland with numerous hidden corners to explore. At leisure, she does birdwatching with binoculars (some people mistake it for stalking), makes flower jewelry, and eats pickles.

All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.

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I’m interested


  • What is hard data?

  • What are hard data examples?

  • What is soft data?

  • What are soft data examples?

  • Hard data vs. soft data: The difference explained

  • Automating data gathering with web scraping

  • Conclusion 

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