Big data is one of the biggest topics of the modern era. With big data, there has also been an increase in data visualisation as a method of presenting and making sense of this vast amount of data. But not all data visualisation techniques are the same.

Data visualisation is among the most powerful mechanisms for presenting data and the advances in technology have created unique ways of doing it. Long gone are the days of simple pie charts, as interactive and unique visualisation techniques are becoming the forefront and viewers are becoming increasingly aware of what they like and what they don’t appreciate.

The following guide will help you understand the importance of data visualisation, the different ways or presenting data as well as some of the most common tools used in the industry. You’ll also learn about the key concepts behind a winning data visualisation technique and the mistake you should try to avoid.

Winning Data Visualisation Techniques

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In this article, you will learn 1) what data visualisation is, 2) why data visualisation is important, 3) what methods exist for data visualisation, 4) key concepts behind a winning data visualisation, and 5) the biggest mistakes to avoid in data visualisations.


Data visualisation is about the presentation of data in a pictorial or graphical format. It is about providing vast amounts of information in a coherent and short manner with the focus being on the presentation. While data visualisation can deal with written information, the focus is often on using pictures and images to get the message across to the viewer.

Furthermore, data visualisation isn’t narrow in its use when it comes to data. It is possible to visualise all sorts of information – you can communicate your concepts and hypotheses to other people. Nowadays it is even possible to add technology to data visualisation and opt for interactive visualisation methods.

Visual representation of information is an age-old method of sharing ideas and experiences. Charts and maps, for example, are great illustrations of some of the early data visualisation techniques.


As mentioned above, humans have used data visualisation techniques for a long time. Images and charts have proven as an effective method for communicating and teaching new information. Research has shown that 80% of people remember what they see, but only 20% of people remember what they read! It can even pass on ideas and events to future generations. The technological developments have further enhanced the opportunities data visualisation brings to people.

Perhaps the most important benefit of using data visualisation is its ability to help people take in data quicker. You can highlight a large volume of data in a single chart and people will be able to spot the key points quickly. In written format, it could take hours to analyze all of the data and make connections.

Furthermore, this ability to present a vast number of data points is another big benefit of data visualisation. A single chart could potentially highlight a number of different things and people could form different opinions over the data. This can naturally open up new avenues for businesses. People might be able to spot something unexpected from the data.

Visual presentations of data improve the ability to interpret information as well. Finding connections from a plethora of data and information is not easy, but graphs and charts can provide the information in a matter of second. A single glance can provide the needed information.

All of the above can enhance communication and effectiveness in the workplace or educational establishment. Data visualisation is universally considered an easy and effective way to outline data and therefore it can improve the way people share information and learn.

The below video is a great example of mapping data in a beautiful manner:


The development of technology has led to the explosion of data. This in turn has increased the ways data can these days be represented. In general, many data visualisations fall into two different types: exploration and explanation. Exploration type helps people find the story behind the data, while explanation explains the data readily to the people looking at it.

Furthermore, there are different methods available for creating the two types. The most common data visualisation methods include:

  • 2D area – This method uses geospatial data visualisation techniques, often relating to the position of things on a specific surface. An example of a 2D area data visualisation includes a dot distribution map, which could displays information such as crime in a certain area.
  • Temporal – Temporal visualisations are presentations of data in a linear manner. The key is that temporal data visualisation has a start and a finish. An example of a temporal visualisation could be a connected scatter plot, which could display information such as the temperature of a certain area.
  • Multidimensional – You could also present data through multidimensional method by using two or more dimensions. This is among the most used methods. An example of a multidimensional visualisation could be a pie chart, which could display information such as government spending.
  • Hierarchical – Hierarchical methods are used for presenting multiple groups of data. These data visualisations usually present bigger groups with smaller groups inside. An example of a hierarchical data visualisation includes a tree diagram, which could display information such as language groups.
  • Network – The data can also be presented in relation to one another within a network. It is another common method to present large amounts of data. An example of a network data visualisation method could be an alluvial diagram, which could display information such as changes in medical professions.

The above shows the plethora of choice, which can provide both huge a amount of opportunities as well as the headache of picking the right method for presenting your data.

There are also a number of tools used for data visualisation. These can make gathering data easy, as well as streamline the way the data is used.

Some of the most common tools include:

  • Google charts – Google’s products are quite well known in the data industry and Google Charts is a handy tool, especially for first-time users.
  • Datawrapper – This is an online tool, which can help you with the creation of interactive data visualisations.
  • RAW – RAW’s benefits include its plethora of ready-made types that allow you to present your information in a clear and quick manner. The platform is open source, so you are able to provide your own custom layouts as well as use other’s designs.
  • Infogram – Infogram is another great tool for first-time users. It allows users to create different charts and infographs and the system is easy to use.

These are not the only tools available and you can find a number of other options in both free and paid software. It is a good idea to look around a bit to ensure you are using software that fits your data visualisation goals the best.


Anyone who’s ever seen data visualisations knows there are good and bad designs. Many of the benefits of data visualisation can easily be undone if the information is not presented in a correct and suitable manner – certain projects require a specific approach.

No matter what your information is about, there are certain concepts you need to keep in mind when utilizing data visualisation. The following is the collection of the key concepts behind winning data visualisation techniques.

Knowing your audience

The first thing you need to do before you present the data is to think who is going to view the data. Knowing your audience will be crucial in order to find the right methods for data visualisation.

Although data visualisation is generally a way to simplify data, the audience might still have different knowledge levels of the subject and you need to prepare for this. If your data visualisation is aimed at a professional audience, then you can use much more niche methods as well as use special jargon to explain the data. On the other hand, a general audience might require a much more explanatory approach to the same data.

It’s also important to be aware of what the audience expects from your data. You need to know what are the key points they want to take home from the data as well as the main objective you have for presenting the data. In addition, you also need to keep in mind what you want the purpose of your data to be.

Having enough understanding of the data

In addition to knowing your target audience, you also need to understand the data inside out. If you don’t understand your data properly, the chances are you won’t be able to convey it to viewers in an effective manner.

You won’t be able to include all the information from your data, so you need to be able to find the key information and present it in a coherent manner. You also need to be certain that the data connections you draw from the data are correct and not imaginary – incorrect data is an absolute no-go for data visualisation!

You’ll also be much more able to draw unique and interesting data connection from the information if you properly understand it.

Telling a story

Your data visualisation should also aim to convey a story. You don’t want the data to be a set of information that is just presented on its own, but rather have a message behind the use of the data. This could be about introducing different narratives and about painting a certain image for the viewer to see.

Using a story will often mean the viewer gains more insight from the data. It can help the viewer understand the new connections as well as delve deeper into the information.

In fact, data visualisation techniques are a great storytelling tool. The saying ‘Image can tell a thousand stories’ is correct and you should use it to your advantage. Storytelling through data sets isn’t difficult, as you can use colors, fonts and the presentation as part of your storytelling method.

In order for the data visualisation to succeed in storytelling, the above point of understanding the data is crucial.

Keeping it simple

Data visualisation has developed a great deal in recent years and as the above showed, there are many tools and systems for you to use. Having access to different unique methods doesn’t necessarily mean you need to use them all. Furthermore, large amounts of data should not automatically mean all of the information is essential.

In short, you need to keep your data visualisation methods simple and straightforward. You don’t want to include too much data or use too many different techniques just for the sake of it.

If you think about it through the storytelling lens, it is important to understand that each element in your visualisation should be part of the essential story. If the data or the element, like a picture of a certain thing, doesn’t add anything important to the story, then you should not include it to your presentation.

Having too many elements in your visualisation can actually end up damaging the finished product and take away from the data. You also need to remember the benefit of data visualisation is all about being able to present large data in an instant. If your visualisation seems laborious then you need to go back and see whether you’re using the wrong data presentation or including too much information.

Proper awareness of platform requirements

Finally, a winning data visualisation technique also understands the technical aspects. People are now viewing and accessing information through different platforms and it is important you keep this in mind. Just like you need to be aware of the target audience, you also need to consider the ways people view your data visualisation.

You need your visualisation to easily adjust to platforms such as mobile, tablet or computer. If your users are only going to view the data through a mobile, then you naturally will benefit more from a mobile-focused approach rather than creating the data around laptops.

In addition to considering the platform’s interface options, you also want to think about accessibility issues. It can enhance user experience greatly if your data visualisation allows proper zooming in and out for people with sight problems. You could also consider different color options for color-blind people. Accessibility is all about enhancing user experience and ensuring your data visualisation is available for all.


While the above key methods will help you create a winning data visualisation strategy, you also need to stay clear of some common mistakes.

Wrong information

As mentioned briefly above, mistakes in data are a huge turn off for viewers. You need to ensure people who are looking at your data have the correct information available. It is your job to ensure people can use the data from your charts and images without the need to double-check the information.

Incomplete information

In addition to ensuring all the information is correct, you also need to present complete data. The viewer must find the relevant information in its entirety, you cannot use data visualisation to cheat or present incomplete information.

Data visualisation can and should tell a story, but the story needs to have the complete and correct information – not a presentation of the data as you see fit.

Oversimplified data

Although you need to ensure your data is presented in a simple manner, it doesn’t mean you should oversimplify it. First, you need to keep in mind the audience – don’t use common, oversimplified language if you are presenting the data to professionals. On the other hand, don’t fill the text with jargon if the viewers are unlikely to be aware of it.

But in addition to this, you can’t expect your audience to understand the connections without clearly presenting them. You can’t omit information just because it seems obvious to you the link was there – remember that your audience will only see the data you present, not the full data set you were able to use!

Inappropriate visualisation

When you are presenting the data, you need to think carefully about the way you present the data. The context is very important when it comes to things such as the font, the colors and the images. For example, if you are presenting information over deaths due a specific illness, a brightly colored, cheerful imagery can seem insensitive.

Inappropriate visualisation also involves with using techniques that make it hard to view and understand the data. For example, you might use bubbles to represent the different spending levels in your department, but if you don’t consider the appropriate differences in size, the bubbles can look misjudged and inaccurate.

Forgetting annotation

Over simplification might also result in lack of annotation. When you are presenting data, it is easy to assume the viewer would know what each aspect of the image is displaying. But simple, added annotation can improve user experience and ensure the viewer is aware of all the data points within your data.

As an example, you might have a chart showing how your business’ sale of bicycles in the past decade. If there’s a big drop or a boost in the data, an annotation explaining the reason behind this sudden change will ensure the viewer gets this additional information.


Hopefully the above has explained you the essential points about the importance of data visualisation. A number of different methods as well as programs can help you to present your data in a unique and compelling manner.

It’s important to understand that underneath all the cool and quirky methods, data visualisation is all about the data – you need to find a way to outline the information in a correct, clear and concise manner. When you find the correct formula, data visualisation cannot only be informative but also aesthetically pleasing.

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