What Are Data Analysis Skills? Quantitative and Qualitative Examples

What are the differences between gathering and analyzing quantitative and qualitative information? Better yet, how can you develop these skills and use this knowledge in a real-world job?

Quantitative data involves measurable evidence, while qualitative data stems from observation and interpretation, often being more subjective.

“Quantitative data provides the hard numbers, trends and statistical insights that drive evidence-based decision-making. Qualitative data, on the other hand, captures the nuances, context and human experiences behind those numbers,” said Ben Tasker, the technical program facilitator for data analytics and data science programs at Southern New Hampshire University (SNHU).

Both of these types of data are vital in today's business decision-making, and the ability to work with them will help you build bridges between what you learn in the classroom and the workplace. As a data analyst, “these skills enable professionals to gain a comprehensive understanding of any situation, allowing for more informed and balanced decisions," Tasker said.

Why Are Quantitative and Qualitative Skills Important?

“Quantitative skills help in analyzing large datasets and identifying patterns or anomalies, which are crucial for making predictions and measuring performance," Tasker said. "Qualitative skills provide context and meaning, helping to interpret why those patterns exist.”

While qualitative data might offer insights into new customer demands and preferences, quantitative data may point to a change in market patterns. The ability to change course based on both kinds of data is important in a market that's changing quickly.

Combining Quantitative Skills and Qualitative Research

Combining quantitative and qualitative data analysis can give students of science, technology, engineering and mathematics, also known as STEM, a potential advantage.

"Complex problems often require a multifaceted approach," Tasker said. "Quantitative analysis can pinpoint the extent of an issue, while qualitative insights can uncover the underlying causes and potential solutions that numbers alone might miss."

For example, in marketing, numbers can show sales patterns, but listening to customer opinions and reasons can tell you what they feel and why they buy, according to Tasker. Using both types of analysis helps you create strategies based on data and that connect with your audience.

3 Examples of Quantitative and Qualitative Research

The application of quantitative and qualitative skills varies depending on the industry. Tasker gave some examples of how different industries use these skills:

Artificial Intelligence/Data Science

• Quantitative Example: A data analyst working in AI might examine big datasets to create prediction models that pinpoint client attrition rates.
• Qualitative Example: To understand why customers leave, data analysts gather user feedback or conduct interviews. This helps improve algorithms by providing context and aligning them with customer needs and preferences.

Environmental Science

• Quantitative Example: Sensors and monitors collect data on pollution levels, showing past trends and the impact of specific pollutants.
• Qualitative Example: Residents' observations reveal how pollution affects daily life, health and local ecosystems, helping find practical solutions for their neighborhood.

Healthcare

• Quantitative Example: Information from clinical trials, like reduced symptoms measured by tests, shows a new drug's effectiveness.
• Qualitative Example: Data from interviews with patients can show side effects or improvements in life quality that numbers don't capture.

What Other Skills Are Needed for Data Analysts?

Beyond technical abilities, Angie Woods, an SNHU career advisor, said that effective collaboration and communication are vital for those curious about how to become a data analyst. These additional skills allow analysts to translate complex data into clear, actionable information for colleagues who may not have a technical background.

“Students who blend scientific and mathematical skills with team leadership abilities are set to thrive in data analysis careers," Woods said. "These students bring a high level of precision to their work, using statistical methods and mathematical models to uncover accurate and actionable insights from complex data.”

According to O*NET OnLine, if you pursue a data analytics career as a business intelligence analyst, you'll need skills in mathematics and database management systems, which you can study in a data analytics degree program. In addition to these skills, you'll be expected to perform tasks such as analyzing related data, preparing analytical reports and developing information communication procedures, O*NET said.

In the field of data analytics, your ability to analyze both quantitative and qualitative data is crucial, as this combined approach helps organizations understand not only numerical trends but also human factors, resulting in more informed and balanced decision-making.

Ollie Burkett is a writer at Southern New Hampshire University, where he is pursuing his Master of Arts in English and Creative Writing. You can connect with him on LinkedIn.