The Most Effective Web Data Visualization Practices

Data visualization is widely used on the web today. And, by the way, it is applied by a great variety of industries. The matter is that it allows understanding the ideas and essence linked to different stuff.

Here is an explanation for this: our brains perceive info better when it is represented in the form of graphs and diagrams (compared to the tables for instance). The reason for it lies in the visualization aspect. So, cool data visualization aims at presenting the info in order to make it understandable for humans. If it is done well, people will significantly improve their experience.

Accordingly, there are certain practices that can be used in order to suggest effective data visualization. They will be considered in the material below.

Practice 1: There is Sense Behind Visual Salience

Such an option makes it possible to concentrate and guide the experience. What is more, it helps to protect the potential audience from data overload which is also really important.

Visual salience aims at pointing some details out. At the same time, other peculiarities may not stand out that great thanks to the implementation of this practice. If we speak about the way to control visual salience, it is necessary to consider color and size here.

Color schemes turn out to be a nice solution in particular. Here is how they are able to help:

• Cool colors allow to move less relevant data into the background;
• The warm ones, on the contrary, assist with highlighting the most important info.

As for the size, the big stuff is used when it is vital to draw attention to something that is significant. It works in just the opposite way with what is not that essential.

Practice 2: Interactivity That Is Being Introduced for Simplified Exploration

Interactivity should be included in case you would like your visitors to dig a bit deeper and investigate. In other words, it should allow for the integration of extra data which can be taken away otherwise.

It is also cool when the interaction is being used as a kind of hook. So, it is great when a user can somehow participate in the data that is being presented to him.

Practice 3: Applying Position, Length, and Color for the Right Purposes

Well, position and length should be used to deal with the quantitative data while color has to highlight and encode categorical stuff.

Here is what has to be considered in connection with this. Encoding data by position should be a top decision when we would like to represent quantitative info. The position-based kind of encoding allows the viewer to compare in a better way. What is more, this process tends to take less time. Length should also be used to deal with this kind of data.

As for color, it has to be applied when we intend to highlight that different pieces of info do not belong to the same category.

Practice 4: Visualization Has to Be Linked to Who is Dealing with It

Visualization is an awesome tool and there are many purposes of using it. However, when speaking generally, it is applied to suggest context and describe connections within data.

So, the choice of color and context have to be wise enough and be based on who will interact with the visualization.

If this is a complete novice who is working with certain data, it has to be represented in a well-structured form and be engaging enough. On the contrary, the audience of a more advanced level can operate with the view of data that will promote further discovery and investigation. It is okay to add a lot of details (but this cannot be done when speaking of the newcomer kind of audience).

Practice 5: Structural Elements Should Be Both Discreet And Clear

This may sound like really hard work to implement but here is what is meant by such a statement:

• Data and non-data elements should contrast with each other;
• Structural stuff like lines and borders that do not clarify anything have to be taken away;
• Grids and axes must be softened (this will make it possible to avoid competing for attention to data that is more important).

What Else Has to Be Considered When Speaking of Effective Data Practices?

Follow the tips suggested below that are also about to turn out to be helpful.

1. It is a great idea to try to create visualization that will be linked to messaging and hierarchy.

It is going to be far easier for the reader to follow the data you are making an attempt to deliver in this case. He will deal with the stuff you plan to represent in a gradual manner, and perception will be awesome in this case. For example, when you are suggesting a title, it has to provide the reader with the basic insight he will deal with later.

2. Try to stick to finding the balance between clarity and complexity

It is also a sort of challenge to work on but it is going to be a nice solution if you aim to achieve a better understanding and perception speaking of the audience that will be dealing with the visualization stuff you are delivering.

3. Do not forget that the audience is likely to use mobiles in order to open the stuff you suggest.

This is a mistake that is made by a lot of designers. Do not repeat it and pay enough attention to this aspect. A great challenge for mobile viewers lies in the use of static visualizations.

In order to achieve fine results in presenting visualization stuff on the mobiles, choose responsive visualizations (Highcharts or D3.js) or introduce different variations of the same static visualization for mobile, desktop, and print.

4. Impose context onto the chart.

It is going to be really helpful in giving the reader an opportunity to realize the importance of the data.

5. Label data series directly on the chart.

The common statement the designers tend to stick to is as follows: each visual element that encodes data should be labeled. The matter is that it does not work this way all the time.

A lot of designers use legends in order to introduce the meanings. But the problem is that it seems to be effective just to them. The reader is likely to have trouble with it, and this will be simply inconvenient for him. Why? A person dealing with the data has to scan back and forth trying to catch the meaning of every piece of data and it leads to facing too much pressure from the whole process. Well, direct labeling may turn out to be very helpful in this case.