September 22, 2023

Qualitative vs Quantitative Data: Analysis and Reporting Differences

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Sept. 21, 2023: Copy edits and tweaks. Added key takeaways for UX. Included new infographics and key takeaways.

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Key takeaways

  • Qualitative Data, generally, is data that cannot be represented easily in numbers.
  • Quantitative Data is anything that can be counted or should be counted.
  • Qualitative vs. quantitative data is less of a debate or more of an understanding of the best use cases for each—and learning to leverage both types.

When beginning a report, the type of data that is presented holds nearly all of the significance of what is being reported. Do you know what kind of data you are working with? If you bring in the wrong data set, you won’t get clear results and the reports will suffer. But what is the right kind of data? Comparing qualitative data vs. quantitative data, we’ll show you the differences and how to best leverage them in business.

There are two major types of data in reporting: Qualitative and Quantitative. These two types represent the overall presentation and attainability of the data in relation to its connection with the report being made. Before starting a report, any business needs to know what kind of data it’s dealing with.

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What is qualitative data?

Qualitative Data illustration of head with lightbulb on red background

Qualitative Data, generally, is data that cannot be represented easily in numbers. It can’t be counted. This data primarily references the qualities of something, its various individual parts, and other qualifiers that can differentiate objects from one another. This means it measures appearance, usually by using relevant keywords such as color, pattern, depth, or hue. It can also describe sensations, emotions, and worded ideas or written work in the form of documents. Gathering qualitative data can be done in the form of observational reporting or surveys where answers are transcribed to text and placed in a data set as abstracts.

What is quantitative data?

Quantitative Data illustration of head with lightbulb on green background

Quantitative Data is anything that can be counted or should be counted. It’s numbers and statistics in basic form and function that provide an immediate understanding of how much of something there is. This also accounts for properties that are specifically numbered, such as the number of assets that a single object has of a certain category. It could be the number of nails in a door, or the number of people who gave an opinion in a survey. Quantitative data exists normally as pure numbers, which makes it very easy to work with for comparisons and statistical pattern recognition.

How qualitative vs. quantitative data are used in reporting and research

Qualitative and quantitative data can be used together to break down complex data sets with many entries. The qualitative data can draw comparisons between different objects based on the qualitative properties of each object. Qualitative data is saying “There is a green door”, and quantitative data says how many green doors there are. That information requires both sets of data to overlap and work together to find an answer.

When making reports, the content of the report should match the data being used. If a report requires compiling different ideas into a related dataset, qualitative data can be used to see how many different results there are, regardless of the number, when taken from a larger data set. The frequency or total of each individual idea can be left out if the purpose of the reporting is simply to log the number of unique new data points that were cataloged or need to be reviewed. Reports on abstract data, such as written work or primarily visual designs also fall under qualitative data.

If a report needs to show hard numbers, then it needs quantitative data. This is useful for showing amounts, frequency patterns, and percentages based on increases or decreases over time. Applications for quantitative data are easier in basic computing as they can be added and subtracted or run through any other set of calculations to achieve a different result. Qualitative data often has less numerical value tied to it. It’s not about numbers, but ideas that aren’t always equal or capable of being calculated or expressed mathematically.

The more abstract the data, the more difficult it can be to report on. Who decides whether an opinion is good or bad? Or whether it’s worth taking into consideration? Researchers who only look at statistics may struggle to comprehend how to order and organize qualitative data because it is not as easily defined. The company should create agreeable guidelines on what traits should be desirable from qualitative data. Qualitative data needs a reference for acceptability the same way quantitative data does. If a report or research is trying to achieve high numbers in quantitative data, the same standards need to apply to reports on qualitative data.

What companies do with qualitative data

Companies will use qualitative data to take in opinions on their progress as a business in order to gather a reliable sample size among their consumers, customers, or clients. Future plans can be adjusted along the lines of those reports as they come in. The reviews can be cataloged based on overall positivity and scaled down with more negative reviews at another end of the data set.

Even though qualitative data doesn’t always have a numerical value, it can still be ranked and sorted along the ideas that the data represents. Red products can be categorized differently from blue or green products in an inventory system. This type of sorting can also lead to more expansive cataloging efforts where products or services are arranged based on similar features and traits instead of by the number of units, cost or weight. These features are often more relevant to the consumers who want them and can take the form of keywords which can be implemented into search engine optimization.

The problem with qualitative data is that it’s unstructured, which means all listing and ordering has to be done by the researchers or reporters working with it. They need to determine what values and traits within the data are desirable for the goals of the company. Once those standards are set, the data can become more structured, at least semi-structured, where all the data has a contextual relation that will make sense on review.

AI’s impact on qualitative data

More businesses are relying on AI (artificial intelligence) to handle reporting. With quantitative data, this process is easy. Computers are built to handle numbers and complex processes and calculations to total them and work with them in various ways. Making numerical reports or statistical reports is easy for a computer. The data that gets generated can then be adopted by the users or made into more abstract, graphical representations of what the data was.

However, AI can’t do the same with qualitative data as easily. Numbers don’t have changing definitions or context-based meanings like words and language do. Unless an AI is programmed to handle all the qualifiers and traits that come with qualitative data, its ability to sort or make sense of a dataset would be limited. At best it could automatically sort the items in the data set based on similar traits. AI programs are being taught to detect colors and color patterns. This is what allows algorithms to determine popular trends on social media sites. They use the numerical ratings of views or likes as well as the quality of the content itself which it will want to match up with past content to form a reliable pattern.

The issue with qualitative data is that it is not always reliably similar enough to match past records or datasets. For example, if a YouTube video does extremely well, it will inevitably be imitated. An AI program can determine which videos those are and automatically compile them, then replicate them based on the images associated with the video. The AI will create a video very similar to the ones it compiled, but it won’t have a point or the same feeling to it. The same thing is being experimented on with AIs writing scripts after being fed data from hundreds of hours of a TV show to find patterns. AIs are only good at work which is provably correct, but qualitative data can conform to taste and preference. It can bend by design and be interpreted differently between two pairs of eyes.

Because qualitative data is not absolute, AI can’t calculate with it as easily as with quantitative data. Steps are being taken to teach certain AIs how, though. By combining qualitative and quantitative data, an AI can detect patterns based on the number of occurrences and then calculate the likelihood of patterns based on which factors have the highest number of repeated values. An AI should be able to read hundreds of reviews, see how many use “positive” language, and then determine an overall ranking based on averages. This is how AI can work with qualitative data.

The difference determines the use case

Qualitative vs. quantitative data is less of a debate or more of an understanding of the best use cases for each—and learning to leverage both types. If you count how many times the phrase “quantitative data” was said in this article, that itself would count as “quantitative data.” But, your opinion on the aspects of qualitative data would count as a piece of qualitative data. One can be counted and the other can be judged. These two types of data sets are invaluable for any business. All businesses deal with numbers, even in small amounts, and deal with ideas, whether they’re grand or simplistic. Knowing how to properly research and report on them is key.

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