What is qualitative data analysis?
Often, businesses collect data that can't be analyzed easily. This data tends to come in the form of text; customer feedback, social media interactions, surveys, reviews, and so on. It can also come in more complex formats like audio, video, images, or other media types.
The majority of data that most businesses collect comes in the form of numbers. Numbers are very easy for a business intelligence system to collect, transform, and analyze. Not only is it easier to perform statistical analysis on numerical data, because numbers can be easily added, subtracted, multiplied, and so on, numbers are just simpler than other data.
In a numerical data set, the same number will always have the same meaning. Regardless of what it actually signifies, a 2 will always mean 2; a BI tool doesn't have to worry that it might actually represent a 4, or a 6.
That's not true for qualitative data sets. Text is a lot trickier to understand than a number. The same word can mean multiple different things, and context can change meaning dramatically. A BI tool can never be sure that a word actually means what it thinks it does, because there are so many nuances to language that only a human can pick up on them.
It isn't impossible for a BI tool to analyze text and other media data, though. It just needs special tools and techniques to figure out the meaning behind the qualitative data that it has collected. Techniques like semantic analysis and natural language processing can translate unstructured text into data streams that can then be analyzed and visualized.
There are some industries and departments where most of the data they receive is qualitative. In customer-facing roles like customer service or public relations, it's crucial to be able to analyze customer input in a programmatic way. Qualitative data analysis tools can help these teams to use data to drive the decision-making process in the same way that a numbers-focused team can.
Every approach towards qualitative data analysis will have its problems. Text analysis is extremely complicated, and even the best tools will have errors. Qualitative data analysis isn't intended to completely replace a human eye, but it can help a business separate the signal from the noise and leverage their qualitative data towards deeper insight.
Features of qualitative data analytics tools
Not every business intelligence tool can start analyzing text right out of the box. BI tools need a suite of specific tools and techniques to perform this complex analysis. Some of these features might be standalone, while others might work in concert with other tools within the software. Here are just a few of the major features common to most qualitative data analysis tools.
Sentiment analysis uses a blend of analytical techniques to estimate the general mood of a piece of text. Instead of analyzing a piece of text and trying to figure out exactly what the person who wrote it meant, sentiment analysis looks for words that imply a certain mood and extrapolates from there.
For example, a restaurant might want to sort through all of their online reviews to find all the negative ones. Instead of manually reading through all of the reviews, they might want to perform sentiment analysis on them.
A sentiment analysis program will scan text for certain keywords that suggest something about the deeper meaning of the text. Words like 'bad', 'terrible', or 'worst' often suggest a negative review, so a sentiment analysis might pick out all the reviews with these words and present them to the manager.
Sentiment analysis can find more complex ideas, as well. That same restaurant manager might want to find negative reviews where the reviewer disliked the food. The program might then look for words like 'gross', 'disgusting', or 'inedible', and surface these reviews to the user.
Many tools like this will assign a score to each review, depending on what it found. A review that met all of the algorithm's criteria for a bad review might get a score of 100%, while a review that the program decided was less severe might only score 50%.
Users can analyze these percentages just like any other kind of numerical data, which can help to find trends and patterns within the data. Tools like sentiment analysis help BI tools to turn complex text data into something that can be easily analyzed and visualized.
Similar to semantic analysis, topic modeling uses indirect means to try to analyze large amounts of text data. Unlike semantic analysis, which uses subjective 'descriptive' words to judge the emotion of a text, topic modeling looks for specific words to figure out what topic a certain text is about.
To go back to the example from earlier, a restaurant may want to find reviews that are about a certain dish, regardless of whether they're good or bad. Using topic modeling, they can look for all the reviews that mention this dish. From there, they can drill deeper into that subset of the data set to learn more about people's perception of that topic.
This process helps employees to get a better sense of what people are talking about. If 60% of a website's feedback surveys mention glitches, then that site's dev team knows that fixing glitches is an important use of their time. If a brand releases a new product line and they find that there are very few social media mentions of that line, they know that they need to rethink their marketing strategy.
Again, topic modeling is a tool that allows businesses to translate complex, unusable qualitative data into easy-to-process quantitative data. It allows users to see trends and patterns within their text, by abstracting that text into other statistics.
One major problem with qualitative data analysis is that it asks software to make subjective judgement calls about complex data sets. If a sentiment analysis finds that a certain review is 75% angry, there's no objective proof of that. It's just a number that the sentiment analysis algorithm made up. It may have made up that number based on good evidence and logical assumptions, but it's still an imperfect, subjective measurement.
Businesses put a lot of faith into these subjective measurements, because they base their understanding of the data on them. The average end user isn't reading every review to figure out whether they're positive or negative, they're relying on the data that their sentiment analysis program gave them.
Since they're so important to a business's understanding of their qualitative data, these statistics need to be generally 'correct'. Again, they can't be objectively correct, because no interpretation of qualitative data can be objectively correct. However, they do need to reflect reality well enough that they can be used to make decisions and drive insight.
There are a few ways that business intelligence tools can improve the efficacy of their qualitative data analytics suite. One method that many BI tools use, and one that's heavily advertised within the industry, is machine learning. Machine learning algorithms are especially good at analyzing complex, subjective data. Qualitative data analysis is an area where they can really shine.
A machine learning algorithm improves automatically as it processes more data. A machine learning algorithm designed for sentiment analysis, for example, will remember what a 'bad' review looks like, and look for similar reviews to that one in the future. As they train on more data, their perception improves, and they output more correct results.
One downside of machine learning algorithms is that they do need a fairly large data set to know what that data is supposed to look like. For example, a machine learning algorithm designed to pick out bad reviews will need a large initial set of bad reviews to know what to look for in later sets. Smaller companies might not have the amount of data required to ensure their machine learning algorithms get good results.
Advantages of qualitative data analysis
Qualitative data analysis has dozens of different use cases in hundreds of industries. Any business that collects text, pictures, video, or other media files from their users can leverage the power of qualitative data analytics to turn those data points into insight.
The main advantage of qualitative data analytics is that it allows businesses to turn data that would be otherwise unusable into useful, relevant data streams. Before tricks like sentiment analysis and topic modeling were possible, businesses had to analyze all their qualitative data by hand.
Thanks to these new analytical tools, businesses can analyze and visualize qualitative data in the same ways that they can analyze and visualize regular data. Users can incorporate qualitative data into their dashboards, and use it to drive insight and make decisions.
Qualitative data analysis saves time over alternatives. By analyzing data in a methodical, programmatic way, computer programs are able to handle large volumes of data much faster than human workers would be able to. For businesses that collect a lot of qualitative user data like product reviews or feedback, it's crucial to be able to analyze that data in a way that's time-effective.
Thanks to qualitative data analysis, businesses can access data about customer feedback in reviews in close to real time, which can help them find out about and react to changes in customer sentiment quicker than before. Many systems can track these analytics in close to real time, in concert with other real-time data streams.
Lastly, qualitative data analysis allows for one subjective analysis of data to become the preferred one across a company. Since qualitative data is subjective, different viewers will have different opinions on what it means. Any two analysts will only agree on the meaning of the same set of data about 80% of the time.
Even though a computer might not be able to interpret a qualitative data set in the same way as a human, the same algorithm will always analyze the data in the same way every time. When data is consistent, it's far more useful.
These algorithms always analyze their data using the same process, as well. It's important to know that each piece of data is judged using the same criteria every time, so that past data can be compared easily to present data. The only way that businesses can analyze their qualitative data the same way every time is through a computer program.
Business intelligence software - the best tool for qualitative data analysis
Business intelligence tools offer the most fully-featured feature suites for qualitative data analytics. Companies can use tools like Sisense and Tableau to analyze their text and other media-based data in ways that no other type of software would allow.
Business intelligence tools allow businesses to integrate the results of their qualitative data analysis with the rest of their data streams. For example, a company could combine a sentiment analysis of customer feedback with other customer-focused data streams, like survey results or sales data, in an attempt to find trends and spot relationships between the data sets.
Qualitative data is some of the most useful data that a business has access to. It brings information and perspectives that businesses could never access with numerical data alone. However, until recently, BI programs couldn't properly leverage it. Now, with the advent of powerful cloud-based BI systems, any company can use their qualitative data to drive insight.
Any business, large or small, can profit from better analysis of their qualitative data. Regardless of your size or industry, there's a tool that will work for you. Reach out today for a free consultation from our team of experts; they have years of experience in the BI industry and can help connect you to the tool that you'll love.