Data visualization makes data legible

The exponential increase in available data means that there a lot more things we can measure and gain insight into using data visualization. For many people, this is an abstract notion. Data visualizations let us turn data into tangible, legible information. The relevant questions, then, are: why should we do this, what are the benefits, and how should we go about it?

What is data visualization?

As mentioned above, the amount of available external data has increased tremendously. Visualizing the data enables us to communicate a message.

Data visualization is a method of transforming abstract data into concrete information and knowledge. This information can be presented in various ways, such as reports, graphs, animations, maps, or dashboards.

Weather reports typically contain multiple types of data visualizations, such as maps and tables displaying predicted temperatures. Why are these types of visualizations used in weather reports? The following types of visualizations are used in the following situations:

  • Bar chart: this type of chart is well-suited to comparing several different variables with each other.
  • Graph / line chart: graphs, or line diagrams, are used to visualize trends that take place over a certain period of time.
  • Map: if geography is a relevant factor, a map is an effective visualization method.
  • Table: tables can be used to display numbers and values in detail.
  • Scatter plot: these can be used to display a spread of variables plotted on two axes, ie. time and frequency. This type of visualization can also be used to visualize a correlation.

Why is data visualization important?

Clear communication is essential when communicating a story or advice. Miscommunication happens all the time in text-only communication. We can write a message intending one thing, while the reader interprets something completely different. Who hasn’t made a joke over text that landed with a thud, or even worse, came across as a grave insult? So why is it that face-to-face communication is less prone to misinterpretation? That has everything to do with body language and tone of voice. Sarcasm, for example, is much easier to communicate in a face-to-face conversation than in a text message.

In short: in physical interpersonal communication, body language and tone support the message we want to convey. How did this audiovisual way of thinking originate? Our early ancestors were audio-visually inclined as a survival mechanism. Using eyes and ears, they could judge potentially dangerous situations.

Humans are still reliant on our eyes and ears. That’s why communicating using expressions and sounds works best. Theory or advice can be communicated both ways, but the best way is to combine sight and sound. We can do that by visualizing data and clarifying it in a face-to-face presentation: storytelling.

Data culture

Many companies find it difficult to create a data-driven culture inside of the organization. The advantage of such a culture is that the organization is more flexible, and can react quicker and smarter to certain events inside or outside the organization. One of the ways to start data-driven thinking is data visualization. Actively creating reports, graphs, and dashboards ensures that employees will start thinking from a data-based perspective. The organization can then take the first step in the direction of accepting a data culture and becoming an intelligent organization.

The creation of data visualizations can be promoted in the following ways:

  1. Create your own data visualizations and use them as examples for colleagues and employees. A good example inspires copycats.
  2. During the weekly scrum meeting, support the argument using data.
  3. During a sales pitch, show that data sells. Good data visualization ensures mutual understanding.

Create your own valuable insights

When visualizing data, it’s best to start with a design on paper or a whiteboard. Making a design ensures creative thinking, which is an important aspect of visualizing data. Once you’ve done this and you’re satisfied with your design, you can start the visualization process using various tools. When doing this, keep in mind the basic principles of data visualization:

  • Garbage in, garbage out: in other words, if your data is incorrect or incomplete, your data visualization will be incorrect or incomplete as well. Ensure that your data quality is up to snuff.
  • Less is more: keep your data visualization free from clutter and distracting, extraneous visuals. The goal is to communicate a message. The reader’s attention should be focused on that message.
  • Show, don’t tell: ensure that your target audience can understand your visualization. Who is it meant for? You can help using shapes, formatting, legends, and colors.


An infographic is a special kind of data visualization. The goal of an infographic is quickly and simply transmitting complex information. A good infographic is a combination of text and imagery that playfully communicates a story. Knowledge of analysis, design, and storytelling are important when designing an infographic.

Follow these steps when creating an infographic:

  • Subject: think of a subject you want to visualize.
  • Analysis: thoroughly research the subject and make a mind map.
  • Conclusions: think up interesting conclusions and facts.
  • Storytelling: create a storyline: an ordering of conclusions and facts.
  • Design: design the infographic using colors, text boxes, graphs, and images.
  • Visualize: create the visualization using your favorite tool.
  • Feedback: get feedback from people close to you and design and marketing professionals.

Effective data visualizations

In 1984, scientists Cleveland & McGill did thorough research into the speed and accuracy with which people can take in visualizations. They arrived at a logical ordering of effectiveness of observing quantitative data. Changes in position are observed most quickly and accurately. This is typically represented by a line chart or map. That’s followed by changes in length (bar chart), corners (pie chart), angle (line graph), surface (scatter plot), volume (3D), closeness, and color. Yet, many 3D graphs contain all the colors of the rainbow, despite the fact that this is the least effective method of data visualization. Keep it simple.

19 tools for data visualization

BI Tools Survey 2019As mentioned above, after designing a data visualization, you can get started building it using a tool. What tools are suitable for visualizing data, what are their pros and cons, and why? Passionned Group has done thorough research into the available tools and drafted a list of tools suited to data visualization.

Visualize your organization

Feel free to contact us and talk to one of our data visualization specialists. They’d love to tell you about the possibilities and pitfalls.

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Daan van Beek, Managing Director


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