Kavita Behera is a Product Manager at PowerViz and publishes #TheWeeklyChart on LinkedIn. Her specialization is in Power BI and data visualization. She came from a research background. She loves training her dog and creating infographics.
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Data Visualization Design: Best Practices and Tips
Published
23rd Jan, 2024
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In this article, we will talk about why data visualization design is essential and how you can create a clear visualization.
Data visualization design has become one of the most demanded skills in the data science industry. An analyst's responsibility is no longer only to extract and analyze data but also to deliver it to the end user in the best possible manner. There are a lot of BI tools out there that let you make charts and graphs, but the data visualization skills put life into them.
The field of data visualization design has grown a lot in recent years, giving rise to a new set of specialized skills. If you want to learn more about data visualization and storytelling, KnowlegeHut offers Top BI and Visualization Courses.
What is Data Visualization?
Data visualization refers to presenting data in the form of graphs. This method helps understand a large piece of data in a meaningful way. Any medium, including charts, pictures, infographics, and even animation, can be used for data visualization. Assume you have 10,000 rows of daily sales data. You cannot process this raw data. Instead, dividing this data on a monthly and weekly basis allows you to spot patterns and gain insight. The better you design this visual, the more insight it will give you. There are many data visualizing tools available in the market. Consider picking the best one like Venngage for creating visually compelling representations of your data.
Data Visualization Design Process - How to Design Data Visualization?
Before you begin designing data visualization, make sure you consider all of the following points. They are an important part of telling the perfect story.
1. Know your audience
The ultimate purpose of your report is to communicate, but you can't get started unless you know who your audience is. Telling a story to an adult and a child is different. Similarly, different user groups are seeking different information, so you should present keeping them in mind. Ask these questions to know more about the users.
Whether the audience is a group or a mass audience?
How it helps: In small groups, you can connect with them and take better feedback, whereas you cannot do this with a public report. Here you should think big in terms of the accessibility and technicality of the report. Not everyone is well-versed in reports.
Do they come from technical or non-technical backgrounds?
How it helps: You can decide how much technical jargon to use. ROI and EBITA are known by the finance team but other departments need more context.
How often they are planning to use this report?
How it helps: It helps you decide what time intelligence metrics to use. How to design the navigation system.
What is the purpose of this report?
How it helps: Without the end goal, you cannot start the work. Understand the user's problem and how your report can solve it. For example, the marketing team every month wants to check campaign numbers. It becomes a hassle for them to source data from different platforms and does the math. They maintain one excel file for one month, so the previous month's comparison becomes more complicated. A good solution for them is to connect the data from multiple sources and maintain a single source every month.
2. Focus on messaging
Everything from data is a fact, but not everything is worth discussing. While creating the visualization, your focus should be on the main topic and collecting all the data to back it up. This will draw the reader's attention to the issue at hand and assist them in making judgments.
3. Convert data into insight
Headers and captions can help you better explain your stories. Instead of just describing the visual, they should explain what is the highlight.
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:
- 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.
- 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.
- 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.
- 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
- Power BI,
- Tableau, and
- 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.
Frequently Asked Questions (FAQs)
1. What is data visualization in UX design?
Data visualization is a technique to convey the data into actionable insight that is easier to understand.
2. What are visualization techniques in design?
Data visualization techniques refer to using different chart types to display different data types.
3. What are the steps in the data visualization design process?
To start the data visualization design process, start with understanding your user, and the aim of the project and then follow the advised best practices to enhance the design.
4. Which is the best visualization tool?
Power BI and Tableau are the 2 dominating data visualization tools available in the industry. Some other popular tools areQilk Sense, Astrato, Visme, Canva, Flourish, Data wrapper, and more.
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