Five Principles of Data Visualization

Here are the top 5

1. Know your target audience.

   Be aware of who you are preparing the data for — what level of knowledge are the people at, which key figures are important and how trained is the target group in handling data? It is always recommended to define a clear goal at the beginning of what should be achieved with the visualization.

   
   
2. It brings structure, the magic seven. 

   Bright and bright colors, background images, overloaded tooltips, and a small novel. 
When you really get going, you often want to use all options to make the dashboard as informative and fun as possible. It's easy for us to ignore what can actually be effectively processed by our brain.
   
In fact, capacities are limited. This is where the magic seven principle comes into play. It means that our comprehension is able to process a maximum of seven units of information at the same time. So no more should be on our dashboard.

   
   
3. Beware of the Minimalism Trap
   
Overcrowded dashboards are therefore confusing, confusing and therefore off topic. 
Anyone who now uses the eraser internally and thinks axis labels, currencies, titles or other descriptions are superfluous, will probably create a sizeable dashboard in terms of design. However, informative added value is almost certainly lost. 
So the middle way does it.
   

Tip: First, hide all design elements, labels, etc. This helps to focus on the essentials. Then go through the elements step by step and add necessary or supporting information. For colors, the following applies: as little as possible, as much as necessary.
   
   
4. It's the shape that counts.

   The key point of a meaningful visualization is, of course, the correct form of presentation.
 The selection here depends on which data and how many different variables are to be displayed. We have the most popular charts and what they are suitable for Summarized here.
   
   
5. Don't mislead. 

   If data is to be used operationally, quality and trust are essential. 
It is therefore important, on the one hand, to check all values for plausibility at the beginning and thus ensure that the figures are correct. 
On the other hand, it is essential to add any confidence-creating elements such as source references or a time stamp for the last data update when presenting. Above all, axes should always be evenly scaled, otherwise comparisons can quickly become misleading.
   ‍




Checklist for a successful visualization:

+ Do I show everything that is essential for the specified goal — nothing more and nothing less?

+ Is the content clear and unambiguous?

+ Are the labels correct, are units of measurement or currencies shown?

‍