Insights

The Importance of Visualising Qualitative Data
Learn More
Insights visualisation

The Importance of Visualising Qualitative Data

When talking about data visualisations, it’s pretty common to think about the mundane line graphs and bar charts. These types of visualisations can be great... when you’re talking about quantitative data. For qualitative data these more common formats aren’t always the best fit.
Learn More
Insights visualisation
The Importance of Visualising Qualitative Data

The Importance of Visualising Qualitative Data

September 14, 2021

By Katie Jones

Teal underline

Data is growing faster than ever before so much so that if we were to burn all the data created in one day onto DVD and stack them on top of one another we would reach the moon twice.

When thinking about data it’s natural to think of vast pages of numbers with little to no explanation on the real-world application or meaning behind them. 

But insights and important knowledge is locked away in words and sentences with no way to simply extract, summarise or present them,  unlike the simple line graphs and bar charts that their quantitative counterparts benefit from.  So how is this problem combatted? What is the best way to extract this information? And how best is it presented to be absorbed quickly?

“A picture tells a thousand words”- Frederick R. Barnard in 1921.

100 years later and this statement has become more true. Our brains absorb information visually over any other type of format.


What is Data Visualisation?

Data visualisation is the practice of graphically representing data and information. Utilising visual representations, such as graphs and charts, can help establish and communicate new insight or meaning from given datasets, allowing viewers to make more efficient data-driven decisions.

In general, visual methods such as bar charts provide us with a better summary of data than tables. Visual representation of the data during analysis is useful to identify relationships and patterns that are difficult to detect manually. The use of visualisation techniques is a continuous analysis process, even at the end of data collection.

There are typically two types of visualisations, exploration and explanation. Exploration data visualisations allow you to find the story of what the data is telling you. This is usually done when unsure on what information lies within the data and is usually completed by people who specialise in qualitative research. It is achieved by going through all of the data and extracting insights which will then suggest a direction for further research. 

Explanation visualisations are used to translate a story to an audience and are used to show the important findings. This is usually done at the end to present the findings of the data and insights you have gathered when the data is more accurate.

 

What is Qualitative Data?

Qualitative data describes qualities or characteristics. It is often gathered using questionnaires, interviews and observations, and is usually depicted in a narrative form.

Qualitative data differs from their close friend ‘quantitative data’, who deals with the hard cold numbers. Working hand in hand, qualitative data can add a new layer of meaning to help us navigate and analyse the world around us.

“Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted.” – Albert Einstein

Let’s look at an example. Here at Graphium, we’re really interested in understanding emerging technologies, but how do we go about our analysis? We can use quantitative data… How many companies work with this technology? How much money does it cost to develop? How many people have been researching this technology in the last 5 years?

This is all useful information that can help us build up a picture of the technology and its story so far. However, a lot of information is missing, particularly regarding its potential future impact. What other technologies show some sort or relation or dependency on the technology in question? How might they be impacted by the emergence of this new technology? What impact will it have on businesses in this sector? Who might be interested in adopting this technology? These are questions that can not be answered with numbers alone, and is where qualitative data analysis comes in. Helping us to understand the topic at a deeper level and allowing us to make effective data-driven decisions in this space.

 

Visualising Qualitative Data

When talking about data visualisations, it’s pretty common to think about the mundane line graphs and bar charts. These types of visualisations can be great… when you’re talking about quantitative data. For qualitative data these more common formats aren’t always the best fit.

However, visualising qualitative data is just as important as mapping quantitative data. Whether you’re a research analyst looking for the new and upcoming emerging technology, or you’re a student looking at research to write an essay, qualitative data visualisations allow you to visually see the relationships and content in a way that can be easily absorbed.

Due to the nature of qualitative reporting, it is often an afterthought to implement data visualisation, but the benefits that result from it are worthwhile.

Meaningful visualisation options for qualitative data do exist, utilising word trees, knowledge maps, charts, word clouds, and comparison diagrams can help make complex findings easier to understand and has the ability to convey concepts in a visual, easily digestible, summarised format.

 

The benefits of visualising qualitative data

There are many benefits of qualitative data visualisations when done correctly and if it has a purpose. If people can absorb information from the visualisation and want to ask more questions and find out more, then you have done it correctly.

Data visualisations can:

1. Identify missed data trends and patterns
This is one of the most valuable benefits of data visualisations as it is impossible to make predictions without having data from the past and present. By using data visualisations such as line graphs, bar charts and timelines it makes the data more natural for the human eye to comprehend and therefore makes it easier to identify trends and patterns. 

When it comes to qualitative data you can use visualisations such as knowledge maps to show the relationships and patterns between data. These interconnections allow you to see the growing interest which allow you to see the missed data trends and patterns which can then be visualised analysed further using other data visualisations such as heat maps.

2. Speed up the research process
Data visualisations can also speed up the research process. Instead of having to read through pages and pages of documents, it allows you to have a glimpse at a visualisation and absorb the information that is needed, then if you want to go into depth, you can read the text.

3. Make data more absorbable for the human eye
Data visualisations also make data a lot more absorbable for the human eye as it is not overwhelmed with too much text. Our eyes are drawn to shapes and colours rather than text because humans are visual creatures. We naturally understand things better through objects, colours and the relationships and interconnections between them rather than words and figures.

4. Optimise data-driven decision making
Laying out data in a visualised format makes it easier to analyse the information provided, which allows analysts to gain more knowledge from the presentation or text. This visualisation can also be paired with text to give additional context.

Big corporations such as Apple and Amazon depend on data and visualisations to justify their business decisions, simplifying the data to maximise their operations. Hospitals also strive to use data visualisations to increase efficiency and to better understand and improve their operating cost-effectiveness, customer satisfaction and patient health progress.

A knowledge map is a perfect example of a visualisation that can be very impactful when done correctly as it shows where knowledge can be found within a company’s organisation making it easily accessible and searchable. The correct use of colour in visualisation is necessary, as different colour lines make it easier for the user to analyse the information.

In the current competitive business environment, it has become imperative for companies to find meaningful correlations between data, and data visualisation tools can achieve this. By using data visualisations, companies can achieve many transformative benefits that could represent a quantum leap in critical operations.

 

Finding the right type of visualisation for you

‍‍Everybody absorbs information differently. As a graphic designer, I find the best way to absorb information is through visuals and a knowledge map is a great example of this because it allows me to see how all the information links together. But this may be different for you. The key is to find out what visualisations work best for you.

For example, someone who likes numbers may prefer graphs as they like to visually see the insights drawn out from the data. Whereas visual people may prefer a heatmap because they are drawn to colour and shape.

Effective visualisations push boundaries of spreadsheets and enable the audience to understand more data in less time. If you put a lot of effort into analysing and modelling your data but end up using the wrong type of visualisation to present your findings, your audience will not understand it. 

 

 

Examples of data visualisations and their benefits

‍‍There are many different examples of data visualisations and different benefits to go with them. At the start of a project visualising your data helps you understand it better and help you find the patterns and trends whereas at the end of the project after you have done all of the analysis, visualisations are then used to communicate your results more efficiently.

Efficient visualisations can either make or break your project. Before choosing a visualisation. You need to ask yourself a few questions. Who is the data for? Why do you need this data? How much data will there be? And what is the best way to visualise the data?

The most common form of qualitative data visualisations are word clouds and hierarchical charts. 

Below I will put together examples of these 2 visualisations as well as my favourite knowledge maps.

 

1. Word Clouds

Word cloud

A word cloud is a cluster of words put together. They are a good form of data visualisation as they are a fast and engaging way to see an overview of some text because it pulls out all of the topics and keywords within a document and produces them visually in a cloud shape.

 

2. Hierarchical chart

Hierarchical chart

A hierarchical chart is used to show relationships and allows you to position things from highest to lowest. It is mostly used within organisations to show where people are placed within an organisation. The top would be the highest position and the bottom would be the lowest position. The connecting lines show the relationship between each position.

 

3. Knowledge maps

Knowledge map of climate change

Finally knowledge maps, these are also known as concept maps. A knowledge map or concept map is a visual aid that shows where knowledge can be found. These maps are created using a variety of interconnected nodes.

When dealing with large amounts of complex data, not everyone is going to be an expert on every subject. By using a knowledge map you can visually search and identify a topic within the centre and see the relationships and linked nodes surrounding, allowing you to find and explore new topics.

The main benefits of knowledge maps are that they can improve team collaboration, identify knowledge gaps and improve the decision-making process.

 

 

The future of data visualisation

Data visualisations are entering a new era, they are no longer just an art. With evolving technology data visualisations now explore different horizons to perceive huge amounts of complex data which makes it easier for companies to make decisions.

Businesses generate huge volumes of data so therefore data visualisations are necessary to translate the information into digestible chunks. If your company doesn’t use data visualisations this may be something you should look into doing because data visualisations are an essential element of business intelligence and will put you above your competitors.

Artificial Intelligence (AI) and Machine Learning (ML) are also becoming effective in capturing critical insights from business data using data visualisations and can do the hard work for you, thus improving the effectiveness and accuracy of visualised data.

 

 

Conclusion

It is clear to see that we need data visualisations because the human brain is not well equipped to absorb so much raw and unorganised data and turn it into something that is usable and understandable.

Data visualisations are very important in any research-driven task whether working with qualitative or quantitative data as they influence the methodology of working data. Giving more insights, allowing the user to respond and find things quicker and easier and also display their findings in a more absorbable way.

Qualitative data visualisations such as knowledge maps and word clouds are a must as they show the relationship and content in a way that can be easily absorbed whereas quantitative visualisations such as bar graphs and line graphs are best used when displaying numbers. 

Overall it is clear to see that all types of data visualisations are important to engage with the data and to allow our brains to absorb as much information as possible, it is just the case of picking the right one.