Data Analysis in Qualitative Research

A seductive thought advancing towards AI, ML and predictive analytics is that more data is better. Why wouldn’t it be? As data is the new fuel of the coming digital economy, the amount of data matters. But does quantity guarantee quality data? How does it impact businesses?

Every organization uses data extensively today. Although not all data is used for analysis; analysis cannot be done without data. The technologies required for data analysis exist in a wide range. This helps explain the varied usage – by organizations and vendors.

Qualitative data is generally more tedious to analyze. It falls within the realm of human analysts due to the high level of contextual understanding and social intelligence. Qualitative methods include interviewing groups of people, holding focus groups, and conducting in-depth observations.

Here are some aspects of Data Analytics that you should consider:

Data Analytics for Marketers

Over the past decade, data analytics has revolutionized the marketing industry. For marketers, big data is a weapon that gives companies a competitive advantage in modern markets. Consumers are willing to pay for personalized goods and services. But, companies that keep track of all of their customers’ online activities can be a bit tricky. Thus, the data helps in gaining detailed insights into consumer behavior and can help from initial consumer interest to final purchase.

Marketing Analytics details enable proper spending planning on the right platforms. They increased the speed and improved the execution of marketing campaigns. Using data, marketers can predict what customers want and create proactive customer experiences. Marketers would make appropriate investments in profitable channels. This way they value their customer as a human being and not just any other number on a spreadsheet.

Data analytics for business

Strategic thinking and planning is essential to growing your business, especially during difficult times. With the introduction of data-driven decision-making, businesses have increasingly turned to data analytics to aid in decision-making.

Entrepreneurs and business leaders should capitalize on the shift from traditional to modern methods by providing their analysts with the tools they need. These tools will help uncover insights and design an up-to-date process that will propel the business forward. Identify your goals and use them as a baseline, determining what data you need to collect and analyze.

Create a data-driven culture in your organization by ensuring that all data is analyzed, understood and implemented in the decision-making process. A solid data strategy is key to effecting meaningful change in your organization.

To provide in-depth insights, start with assumptions and inferences, then add contextual data to make it meaningful. Making good data-driven decisions requires a combination of qualitative and quantitative analysis, along with data to back up your assertions and conclusions. It is crucial to keep up to date with the latest innovations that can help you outshine your competitors. The team in charge of this task is your business analysis team.

Understand the “why”

Let’s start with an example. Businesses can use social media analytics to select influencers for customer engagement segments. But the tricky part is understanding why the particular influencer is attracting customers in the first place. And that requires a diagnosis of social behaviors.

Organizations are experimenting with contemporary ways to better understand their stakeholders.

By involving your potential customers in qualitative research such as interviews, focus groups, and observation, you can diagnose the “why.”

Customizing and optimizing services can get the most out of your customer service.

Understanding “Big Data”

For decades, the term “big data” has been used to describe data characterized by high volume, high speed, wide variety, and other extremes. Especially with the evolution of Internet usage and computing power, data traffic provides a rich source of information to improve decisions. But it also creates challenges for organizations in how they store, manage and analyze big data.

It’s no longer a secret that big data is the engine of business intelligence. But again, professionals struggle to manage big data with its dynamic nature of multiple audio, video and content. They need insights from advanced analytics. The big data approach enables data analysis and synergy across a range of small and large data sources – both highly organized and extremely quantitative (structured) data and qualitative (unstructured) data. The small data approach generates valuable insights by using multiple analytical methods only with less data.

Qualitative data reveals how your customers feel and what they expect from you. Analysts find solutions to problems by looking at your customer experience (CX) and putting the customer at the center of everything. However, to extract meaningful insights, data analysis must be effective. For this, you will need the right qualitative data analysis tools.

Determine the best data collection methods and frame the questions in a neutral format. To get the best results, once you’ve finished analyzing the data, you need to think about how to represent it with a compelling narrative.


According to Gartner, 70% of organizations will shift their focus from Big Data to Small Data. Big Data will leverage available data, either reducing the volume required or extracting more value from diverse and unstructured data sources.

There is a wide range of qualitative data analysis software available to meet various customer requirements. Even so, to analyze your data, many of these tools require you to code it manually. SO, why wait to start?

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