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The complete guide to data visualization

Data visualization “brings data to life.” In other words, it allows data to be read through representations that are common in our everyday lives. Charts, diagrams, and maps are examples of mechanisms used for data visualization.

With the exception of maps, the first charts and images began surfacing in the 1950s with the use of computers centralizing data. Since then, new forms of data visualization have been developed and have become popular.

What is data visualization

Data visualization is the process of representing potentially complex or abstract information, such as numbers, statistics, trends, or patterns, through charts, maps, infographics, and other visual forms.

For American statistician Yale Edward Tufte, these representations consist of “communicating complex ideas with clarity, precision, and efficiency.” When data visualizations are done correctly, they provide clear information about data sets that, at first glance, are complicated to interpret.

These representations, which combine science and design to facilitate reader comprehension, allow for informed decision-making based on data analysis and can help find solutions to complex problems in various areas.

Types of data visualization

Although the field of data visualization is constantly evolving, below we list some established categories that are widely used and continue to be relevant in data analysis:


Examples of charts (Source: Qlik Demos)

Charts are visual representations of data used to communicate information clearly and effectively.

To do this, charts are made up of visual symbols, such as bars, lines, sections, and icons that help represent the data.

The most common types of charts include:


Charts help in identifying trends, patterns, relationships, and differences in data, making it easier for users to interpret and understand the information that is presented to them.

However, it is necessary to choose the chart model that best suits the phenomenon being measured and the objectives of the analysis. Choosing the type of chart that does not work with a specific type of data can distort or misrepresent the material.


An infographic is an explanatory representation that combines charts, images, and texts to provide an overview, summary, and visual view of a particular phenomenon or subject. In other words, it is the perfect combination of visual and text resources to convey one or more pieces of information more clearly, easily, and attractively.

It is not something new, but it became popular in the last 20 years thanks to increased access to digital technology and the internet.

In this context, infographics have gained space as a great way to present complex data in an attractive and easy-to-understand manner and have become an essential part of data communication in many areas, including journalism, advertising, marketing, education, and science.

The most common types of infographics are:

  • Statistical;
  • Informative;
  • Geographical;
  • Process-driven;
  • Comparative;
  • List-generating;
  • Infographic CV.


A diagram is a visual graphic representation of information. Similar to a chart, a diagram is a schematic way of presenting links between different elements that make up a set or system.

In general, they are used for mapping processes, identifying KPIs for decision-making, connecting ideas, planning projects, etc.

The most common examples are:

  • Flowchart;
  • Histogram;
  • Tree diagram;
  • SWOT analysis;
  • Fishbone diagram;
  • Mind maps;
  • Use-case diagrams;
  • Sitemap.


Examples of maps (Source: Qlik Demos)

Maps are schematic representations of a specific place, such as countries and cities. They are an essential tool for the analysis and visualization of geographic data.

Additionally, maps can be combined with other types of charts and infographics to provide a more complete visual representation of the data.

In a digital environment, interactive maps (such as Google Maps)  allow users to explore data in a more detailed and personalized wa and are becoming increasingly popular.

The most common types of maps are:

  • Heat map;
  • Dot map;
  • Line map;
  • Area map;
  • Bubble map.

What is the purpose of data visualization?

The main goal of data visualization is to make the information one wants to convey more understandable and accessible, helping to identify trends, patterns, and insights that may be difficult to perceive with just raw data.

In this sense, data visualization can be used to:

Making data easy to understand

Data visualization influences the way the brain processes information. Interpreting large volumes of data is not always possible, which makes the use of charts, diagrams, and other resources essential. With them, it is possible to organize this information and convey concepts in a more universal language for human comprehension.

And this applies to all types of data, even the most complex types. The great advantage here is the possibility of grouping and simplifying them so that even non-specialists can understand this datasets.

An infographic, for example, can present concepts to the general public on a particular specialized subject in a didactic and illustrated way, and in a language that they can easily understand.

Carrying out a more accurate investigation

The visual representation of information and the way it is grouped allow for a greater perception of patterns, thus facilitating a better notion of that data set compared to its raw form.

By simplifying the data that visualizations enable, it is possible to understand them more deeply. This is something extremely important, especially when it is necessary to outline an actionable strategy.

Identify trends and atypical phenomena

Another benefit that comes from simplifying the data and investigating it is the ability to identify trends and outliers. 

However, this is only possible with a good use of data visualization, choosing appropriate representations and using visual resources that aid in pattern and anomaly identification.

A practical example is the thermometers used in industrial machines. They feature a scale (degrees), a marker (pointer), and measure only one magnitude (temperature).

Although simple, this type of visual representation of temperature drastically reduces the chances of a critical event happening, as it is much more accurate than human perception of heat and is capable of alerting about atypical situations.

Data visualization here will serve to indicate the appropriate or critical temperature. For this, in addition to the pointer, it is also possible to use colors to help identify the atypical phenomenon. For example, the normal temperature range can be identified with the color green and the critical temperature range with the color red.

Identifying relationships and correlations

Data visualization can help identify correlations between different variables, which would be much more difficult to identify with just raw data.

For example, a scatter plot showing the relationship between price and sales of a product, it is possible to identify the ideal point where price and sales quantity are maximized, allowing informed decisions to be made about the product price.

By identifying relationships between the different sets of information, data visualizations can help reveal important insights and guide strategic decisions.

Telling a narrative with data

Data visualization can be a powerful tool for telling stories with data. By creating visualizations, data can be organized and presented in a way that communicates a clear and coherent narrative.

But for this to be effective, it is important to understand the target audience and the purpose of the visualization. A good narrative should be tailored to suit the interests and needs of an intended audience. When this is well done, data visualization can turn a set of numbers into an engaging and informative story.

Monitoring performance

Data visualization can be used to monitor business or systems performances with real-time or periodically updated data, allowing adjustments and improvements to be made when necessary.

For example, an e-commerce business can use data visualization to monitor the number of visitors to a website, daily sales, and user conversion rates. With this data, it is possible to identify patterns and trends and make informed decisions to improve business performance.

This is particularly important in environments where data is constantly changing and where goals and performance need to be closely monitored.

How to develop your data visualization elements

Let’s now move on to the essential aspects of creating visual elements:

Factor in the type of data

The type of data you are trying to represent is an important factor in choosing data visualization. For example, if you are working with categorical data, such as the best-selling car type in a region, bar charts or pie charts may be more suitable. If you are working with numerical data, such as store sales over time, a line chart may be more appropriate.

Consider the message you want to convey

The chosen data visualization should clearly convey the message you are trying to communicate. For example, if you are trying to compare values between different categories, such as product sales by department, a bar chart may be more suitable, while a pie chart will work better if you are trying to show the percentage composition of different categories within the whole.

Be careful not to distort the data

When creating data visualizations, it is important to ensure that the data is presented accurately and without distortion. Distorting the data can lead to misleading interpretations and wrong decisions.

One of the most common ways to distort data is by changing the axis scale so that the differences between the data are either amplified or diminished. This can be especially problematic when the difference between the data is small, but the axis scale suggests a larger difference. To avoid this distortion, use appropriate scales that ultimately represent data consistently.

Avoid unnecessary features

Edward Tufte, one of the first scholars to work with data visualization, coined the term “chartjunk,” which refers to unnecessary or confusing elements that can reduce people’s ability to interpret visualization tools, especially charts.

Common examples of unnecessary features include using cluttered images or other graphic elements in the background of visualizations, or even applying effects to charts such as gradients and shadows.

Consider the target audience

The target audience for your data visualization is also important. If you are presenting to a general audience, it may be more appropriate to choose a simpler and easier-to-understand visualization type, such as bar charts or pie charts. On the other hand, if you are presenting to a more specialized audience, a more complex visualization type that maintains technical intricacies (such as a scatter plot) may be more suitable.

Use more than one visual resource

If the project allows, use more than one element to enrich your argument. Infographics combine different types of charts and visual resources to create a narrative and are a good example of how this can be explored.

Another tip when choosing resources to communicate your data is to experiment with different types of visualization and see which one works best. Try creating several different visualizations and choose the one that best meets your objectives and the needs of your audience.

Qlik Sense is among the most widely used tools for data visualization.

If you’re looking to boost your data visualization capabilities in Qlik Sense, Cluster offers solutions that can help take your visualizations to the next level!

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