How Quantitative and Qualitative Analysis Helps In Data Analytics

Data analysis is extensive, exploratory, and absolute complex. However, when we make a move back and try to interpret data analysis, we can immediately see it comes down to two things: qualitative and quantitative data. These two types of data are very different, yet, they make up all of the data that will ever be investigated.

In this blog, we will see how Qualitative and Quantitative Data helps in the Analytics process.

What Is Qualitative Data?

Qualitative data is non-statistical in nature and is usually unstructured or semi-structured. This data isn’t significantly measured using hard numbers used to generate graphs and charts. Rather, it is classified based on features, characteristics, labels, and other parameters.

Qualitative data can be used to ask the question “why.” It is investigative and is usually open-ended until further research is organized. Creating this data from qualitative research is used for interpretations, theorizations, developing hypotheses, and initial understandings. To learn in detail, about this domain, you can join data analytics online training.

Qualitative Data can be created by:

• Texts and documents
• Images and symbols
• Audio and video recordings
• Observations and notes
• Interview transcripts and focus groups

What is Quantitative Data?

Quantitative Data is statistical in nature and its structure indicates more fixed and defined. This type of data is calculated using numbers and values, which makes it a fit candidate for data analysis.

Whereas qualitative is accessible for exploration, quantitative data is much more concise and close-ended. It can be used to ask the questions “how much” or “how many,” accompanied by conclusive information

Quantitative Data can be created by:

• Tests
• Surveys
• Experiments
• Metrics
• Market reports

Also Read: How Is Data Analytics Making Vital Steps In The Field Of Education?

How Qualitative and Quantitative Data Helps In Analysis?

Now that we have split down the basics of qualitative and quantitative data, it’s time to study which type is better for data analysis and why.

Qualitative data is typically considered as unstructured data or semi-structured. This type of data is loosely formatted with very less structure. Due to this, qualitative data cannot be gathered and examined using conventional techniques. Understanding qualitative data can be time-consuming and costly, although there are some methods of “structuring” this data.

The growth of No SQL databases has made the collection and storing of qualitative data much more flexible, but, it’s still an upward climb before being capable to harness this data effectively.

Quantitative data is usually regarded as structured data. This type of data is formatted in a way so it can be immediately organized and searchable within relational databases. Since, qualitative data and structured data go hand-in-hand, this type of data is usually preferred for data analysis.

THE BOTTOM LINE

To boost your understanding of qualitative and quantitative data, study some of the ways in your life where both can be applied. Data Analytics is Perfect-fit for Professionals and this domain is growing rapidly

Start with yourself for an instance. To obtain qualitative data, consider parameters like the type of hair, the color of your clothes, and nose shape. For quantitative data, think of measurements like your weight, height, age, and shoe size.

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