Data Visualization Design: Best Practices and Tips

Tips to Improve Your Data Visualization Design

This section is divided into three parts that discuss data visualization tips.  

• Dashboard design 
• Colors 
• Charts 

Dashboard Design

• Choose the right format

There are so many ways to tell a story: pictures, videos, interactive reports, comics, presentations, and more. Also, the size is just as important as the format. The 16:9 aspect ratio is ideal for desktop screens, but infographics display better on mobile screens. Will you be doing a presentation, or is it a self-analysis report? The more you predefine these decisions, the better you can plan the design and narration.

• Pick the font style 

If your reports are not legible, they are useless. Plan out the font styles you'll be using on the dashboard. Fonts are the most common UX design data visualization decisions. You can also use a combination of two font styles, one for the header and one for the body. Make sure the font style you pick follows company norms, is available on your BI tool, and is consistently used across the design. 

• Visual hierarchy 

Visual hierarchy is the arrangement of things in order of importance. As a designer, you should experiment with re-ordering the grid to determine which version highlights and stands out the most. This step should be done at the prototyping stage before you design the layout and charts. 

• Less is more 

Less is more is not only a design tip it’s a lifestyle. In the data visualization context, less is more refers to eliminating any unnecessary parts and noise from your report. Take a step back and see if there is anything that can be taken out without affecting the message. Every additional piece increases the cognitive load and processing time on the brain. By deleting them, you present the user a clean and well-thought-out content.

• Whitespace  

Adding whitespace to your dashboard is another approach to eliminate clutter. It allows your design to breathe. Including appropriate amounts of whitespace in your report is essential. They act as the pause you need same as how commas are used in writing. 

Colors

Color has its section because this is one of the most important points out there in data visualization design. Colors play multiple roles on the overall dashboard and the chart level. Generally, companies have specific guidelines on what color to use and not. So even before starting the design, you should discuss colors with clients. Always ask stakeholders about their color guidelines. If they don’t have one, then follow a custom theme. This can save a tonne of time in the design.

• Use the same color for consistency  

If you’ve used red color to show the USA country, then make sure it stays the same across the report. Don’t use blue or other colors elsewhere to display the same country. This way red color can become a visual cue for the USA.

• Positive and negative colors 

Generally red is used for negative values and green for positive values. So if you’re using a dataset with negative positive values follow the common diverging scheme. Use the color's sentiments to your advantage. For example, if you’re presenting a temperature change dataset, you can make information easier by using blue for low temperature and yellow for high temperature.

• Restrict yourself up to five colors

More than five colors in a report can be a lot to look at. In such cases where you have more categories, use a sequential scheme. The sequential scheme has one color in different shades. Using the same color from light to dark shade brings balance and avoids an overload of colors.

• Leverage grey tones

The most important color in any report design is grey. Grey colors bring elegance to your work. It is a neutral tone, so it blends with any color without making it over. If you don’t like the sequential scheme then use your primary color with grey tones. No wonder grey is the king in the interior design realm. It is used more often than you realize.

• Color accessibility 

If your report is viewed by a mass audience, it is always a good practice to use colorblind-friendly palettes. Avoiding too many bold colors together. It can be overwhelming for anyone. 

• Use color scheme by data type

Use a single color scheme for continuous data, multi-color for the categorical, gradient to show range, and diverging color for differences. 

Charts

In earlier sections, we talked about users, and how to improve the design of a dashboard and colors. This section will go over everything you should consider while developing the perfect visual. 

• Choose the right chart type 

There are more than 100 visual types, and each has its own use case. I honestly feel that there are no bad visuals. There is a dataset for every visual type; you only need to choose the one that is appropriate for your purpose. I don't agree with the notion that pie charts are bad. I just don't think it's a good idea to use it to show 12 different categories in different colors. There are numerous ways to display the same information but choose the one that the end user can read with the visual that justify the data. 

There's one more side to this. If you want to choose a chart that the end user can understand, there are a handful of options. What about the remaining advanced charts like Sunburst, Sankey, Network graph, Horizon, and more? I like to divide charts into 2 categories: exploratory and explanatory.  

Exploratory charts are used to examine data and derive insights. They are made for analysts and well-versed professionals. Once you've identified the insight, you'll need to put on the hat of a data visualization designer and choose the best chart. 

• Label your data 

Data labels on charts should be simple; they should reduce clutter and bring clarity. Some common data label tips display values up to 3 digits. For example, 300, 3.00K, 30K, 300K, 3.00M, and so on. Hide the labels for too small values, and show first/last and min/max to simplify. Make sure to mention the correct format and scale of value. 

• Sort order

Sorting in charts should be done in a meaningful way. The common orderings are by value, by category, and by time. You can sort information in ascending and descending order. For example, if you use the month name as the axis, neither alphabetical nor by value order makes sense. Using the time order is the best approach.

Note: If you have “others” in the chart, it should always be positioned at the end. 

• Supporting elements  

What are the supporting elements? To make your visual more effective, use features like reference lines, remarks, patterns, and conditional formatting. However, keep in mind that the "less is more" notion only works if they emphasize the message. 

• Split the dashboard into small sections 

In an earlier section, I mentioned exploratory and explanatory charts and how you should modify a chart for the end user. Sometimes it’s possible that there is no simple visual to do the job in such cases, break the information into smaller visuals.  

A lot of information in one chart increases the computational load and is difficult to interpret. Your job is to make data communication easy. So if five visuals would help convey the same point more clearly, then use as many as necessary. 

Why Use Data Visualization?

Data visualization and storytelling have made their place in the data science field. People have recognized the power of effective design. It has become a must-have skill for analysts and developers. 

Reasons why you should learn and implement data visualization design principles: 

1. Tools are only a medium they don’t understand the data as well as you do. They can create visuals but not stories from them. This is where you as a data designer come in. 
2. Humans interpret visuals better. Our brain is better at processing images than words. Humans, unlike computers, cannot process large volumes of data. Hence design principles, help in translating raw data into valuable insights. 
3. Enhance your communications skills. Businesses want to make data-driven decisions for long-term growth, but how can they do so if they don't understand what the data is telling them? Good data visualization strategies help people make better decisions and gain the right insights. 
4. Give you control over the story. Take, for example, the Batman and Joker films. They are almost the same movie, but one is from the good guy's point of view and the other is from the bad guy's. In a similar way, data visualization skills give you the power to tell your side of the story.

When to Use Data Visualization?

So far, in this article, we've discussed what data visualization is, as well as how and why to use it. The obvious next question is when to use it. Let's try to figure out better how to plan your data visualization dashboard design process. 

You should divide the design into three sections: 

1. At the planning stage

The report's ideation phase begins now. Here, your task is to identify whether or not your user has a design guide. If yes, you will get color schemes, icons, and styles that are already set. 

If no, 

• Then, based on the user's vision, create a prototype design. 
• Create a color scheme, a style guide, a dashboard size, and an interface. 

The design is based on the initial vision, thus things may alter during report implementation. 

2. Implementation

Because you've planned the data visualization design system such as color, design, and layout ahead of time. This will be useful at this point. Your report will be a lot more clear and more informative. You will have all the measures there, just format things as per the report sketch. 

3. In the end

When you've finished your entire report. Take some time to go over everything again, jot down your thoughts, and highlight anything you believe is important. Take a break from the report and then re-check it as a layperson. Try to keep things as simple as possible. This is the section where you can decrease clutter and remove extraneous elements. 

After that, your design is finished. Simply wait for your user's final feedback before publishing. 

Data Visualization Design Principles

The fundamentals of data visualization design are almost all covered in the tips section. So I won't talk about them here. In this section, I'd like to discuss Gestalt principles. Gestalt principles explain why some charts are more effective than others. He divided them into six topics based on the basic principles that help humans understand the visuals easily. 

1. Proximity

Place objects close together to group information. This enables the viewer to visually connect objects. You can accomplish this by using whitespace and gaps between two items. 

2. Similarity

Our eyes are drawn to patterns, and when two things are similar, they automatically group them together. Similarity can help you present the odd one out. The most basic method is to use colors. 

3. Enclosure 

The enclosure creates borders around an object in order to distinguish it from other information. You can accomplish this by using borders and boundaries around the visible region. 

4. Closure

Humans eyes are good at sensing missing elements, according to closure. As a result, always complete the visual properly. So, if you've built a chart, make sure there aren't any weird spaces or parts missing from the visual. 

5. Continuity

The continuity principle is similar to closure. When things progress in the correct order, it becomes easier to connect them and they appear to be part of the same structure.

For instance, your header is in uppercase, while your paragraph is in lowercase. If you manage to follow this across the articles, it will correlate it, but if you do not, the reader will struggle to comprehend what the header is and what the paragraph is.  

6. Connection

Connection is, as the name suggests when you use a line to link two things together. It's common in process mapping, hierarchy, and other cases. It is easy to connect objects in steps using a line.

Best Data Visualization Design Examples

This report use all the effective data visualization principles. It’s using correct chart types, color scheme, story narration. Report is created by Jocelyn Rivera.

This is another example of good data visualization principles with proper chart type. You can use filters and buttons to make your report more detailed. This report is created by Gustaw Dudek.

This dashboard shows us how to use a color scheme, layout structure, visual hierarchy with actionable visualization. It is created by Federico Pastor.

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Data Visualization Tools

There are a lot of visualization tools on the market today. The top three tools, according to the "Gartner Magic Quadrant for Analytics and Business Intelligence Platforms 2022," are  

1. Power BI,  
2. Tableau, and  
3. Qlik

Other tools include Astrato, Visme, Canva, Flourish, Data wrapper, and more. Before using any of these platforms, a business intelligence analyst needs to become familiar with the tool interface and features. However, the recommended practices for data visualization remain constant. Simply follow the tips and principles outlined in this article.

Conclusion

In conclusion, data visualization tools are only a way to show information. It's up to the designer to come up with the strategy, narrate the story, and show the facts by using the data. Following the best practices minimize iterations and make decision-making quicker. 

Let's recap what we learned, 

• We understand how to begin the data visualization process. 
• What sort of questions should you ask before starting the report? 
• We covered three aspects of data visualization tips: dashboard design, colors, and charts. 
• You’ll learn why learning data visualization skills are important and when you should implement this. 
• Gestalt principles for data visualization and how they help humans perceive information better. 
• The common mistakes and tips to avoid them 

Finally, KnowlegeHut Top BI and Visualization Courses are a great place to learn more about data visualization design and best practices.