Good data visualization can express any kind of data and make it easy to absorb. Here are a few tips and tricks to ensure you always know how to express data visually, the right way.
Data visualization allows you to express data to your teams in intuitive and contextualized ways. When done right, data visualization can instantly express even large and complex amounts of data to its audience.
That’s why we put together the most important data visualization best practices. Following these will help you visually express your data quickly, beautifully, and effectively.
Let’s dive right in.
1. Use the right data visualization tools
The best way to streamline and automate your data visualization process is by using the right tools for the job. Whereas before data visualization tools were limited to simple user-made graphs and visuals, nowadays you can access powerful AI-based tools to analyze data, like Polymer Search.
Polymer Search is an AI-powered data analytics tool with robust and dynamic data visualization options. Instead of looking up your data and inputting it into a graph maker, with Polymer Search you can drop your spreadsheet filled with data, and let Polymer’s AI automatically transform it into a robust and flexible database. Then, you can automatically transform and visualize your data into interactive pivot tables, data tags, and multiple graphs and chart options.
And you don’t always have to rely on yourself to find the right insights. Polymer’s "auto insights" proactively show you auto-generated insights to help you learn and keep up with your data.
Even when you have the best tools for the job, you still need to consider who you’re showing your data to. Knowing your target audience can help you find the right way to express data, properly contextualize it, and ensure the message is clear.
Someone who already has a lot of technical knowledge and is familiar with your data will likely need little introduction or contextualization along with even large datasets. However, someone unfamiliar with your data or how you keep track of it will likely need simpler, more contextualized visualizations.
Always design your data visuals with your target audience as a starting point. Establish beforehand the level of familiarity they have with the data, and let that inform your data visualizations.
If you’re presenting the data, favor intelligent visualization options that allow you to adapt it to your audience on the go, and make it easier to explore and explain the data.
3. Choose the right kind of data visualization
Now that you have your tools and your audience, you need to decide the right visual for the job. Although there are countless ways to express data, these will give you a solid foundation.
Here are some of the most popular ones:
The bar chart expresses values with horizontal or vertical bars on x and y axes. This is probably one of the most basic and prevalent ways to express data since it’s intuitively simple and easy to understand.
Best practices for bar charts:
Starting your Y-axis at zero allows you to visually represent your data more effectively. You can have a clean and minimal design with just labels or icons for each bar or add details like extra tags and lines to show growth.
However, when it comes to color, less is more. Use color sparingly and only to better express the data. Check out these bar chart examples to learn more.
Just like a bar chart, the line chart expresses values on an x and y axes but connects them together into a line. This chart is useful when keeping track of fluctuations and movements and comparing them at different time intervals.
Best practices for line charts:
Since the line and the bar charts are made very similarly, they share many of their best practices on color and design. Lines, however, can quickly overcrowd the graph, so try to stay below 6ish and never above single digits.
A pie chart is a circular graph with internal divisions used to represent the percentages and proportions of a whole. Its simplicity makes it one of the go-to graphs when breaking down data coming from one source.
Best practices for pie charts:
As always, keeping the design clean and minimal is usually ideal for pie charts. Help your audience understand your chart better by showing fewer, similarly-sized segments, and by keeping your chart as a perfect circle instead of tilting or distorting it.
Like line charts, area graphs show the change of values at different time intervals, although it connects all dots together into a continuous line. This makes it perfect to see how data connects with each other into data snapshots.
Best practices for area graphs
Area graphs work better when the top of all layers remains fully unobstructed. Use matching and coordinating colors to bring uniformity to the chart and make it easy to read.
Scatter plots place a variable along both vertical and horizontal axis, and use dots to represent each value. This is useful when showing the relationship between variables and identifying trends.
These graphs are useful when presenting a lot of data, since it allows the user to focus on each data point and immediately contextualize it.
Best practices for scatter plots
Use your scatter plot when you need to present multiple datapoints, since using fewer datapoints will make the graph look too empty and devoid of context. Like with any other graph, keep the start of your Y axis at zero, and have a clean visualization by showing few regression lines.
Using several predictable patterns can often be better at portraying information than using one more complex visualization you have to explain.
The human mind likes patterns and actively seeks them to understand and even predict the world around it. By using patterns, you can “train” the observer to absorb the data in a specific way and rhythm. This makes the process of absorbing information far more intuitive and prevents mental fatigue by keeping your data simple and predictable.
When planning your data visualization strategy, repeat visualization styles as much as possible, and break down different datasets at a steady and predictable rhythm.
5. Pick the right colors
Colors are powerful yet often overlooked parts of data visualization. Although sometimes they’re merely used to differentiate between values, colors can play a larger role in expressing data.
You can tie colors to specific values or metrics to make it easy for your audience to keep track of it across different graphs and charts. Using more neutral colors can help diminish the visual importance of certain values and ensure they’re not overshadowed.
Color psychology should also be implemented into your data visualization methods whenever possible. By using basic color psychology, you can easily highlight important values, help contextualize data, and even express if different values are desired or not.
6. Keep users engaged
It can be hard to keep your audience engaged, especially when showcasing large datasets to users who aren’t too familiar with it. Keeping track of data and how it changes can quickly overwhelm users if they need to put effort into deciphering each visual.
To avoid this, keep your visuals as simple and clear as possible. Favor streamlined ways of presenting data, even when that means using more than one chart or graph. You can also use interactive data visualization techniques to allow users to play around with the data and instinctively discover it.
Finally, ensure you also stay engaged with the help of AI-based tools like Polymer, and its proactive AI insights and suggestions to help you keep data fatigue at bay.
7. Highlight key data points
Not all data is equally important. Depending on what you’re presenting, you’ll want to highlight different data points from your visualization to help guide your users through your data, while keeping it contextualized.
Besides using color, as previously mentioned, you can highlight data with specific visual cues, from simple arrows and circles to advanced animations that help showcase the right information at the right time.
8. Be careful with your text
Your graphs and charts should contain the text it needs to be understood and nothing more. Since the purpose of data visualization is to show your data, keeping your text to a minimum can help the data be easier to interpret.
Use your text only to express the most basic parts of your charts that can’t be expressed any other way, like when labeling values or explaining discrepancies.
Outside of those scenarios, if you’re finding yourself writing down a paragraph or text, that’s probably a hint towards you using a chart or graph instead.
9. Design for a mobile experience
More and more people are preferring their phone over their computer to do business in. This poses a big problem for some outdated data visualization options, especially those relying on static images to express data.
This can be an easy thing to overlook, especially when data visualization is designed by data scientists exploring their data with a couple of full-sized computer monitors. Although the user may be able to zoom into your graphs and drag your images around to examine the data, it’s better if you design the data using mobile optimization as your basis.
Upgrade your data visualization software to a smart tool like Polymer, that can toggle between different visualization options, and automatically adapts to a mobile or PC setting.
10. Keep It Simple
When designing different charts and graphs, it’s easy to get carried away and overdesign or overshare data. However, the more data you show, the more muddle your message becomes, and the harder it will be for your audience to keep track of it.
Avoid this by defining why you’re visually expressing your data the way you’re doing it beforehand. Once you’ve defined the why, you’ll want to define how you should show your data to make it easy for your audience to understand it fully.
Although you should avoid under designing your data visualizations, it’s often better to keep things simpler and instantly recognizable. Instead of using fancy and complex graphs, favor using simpler data visualization techniques, and using the right tools to keep your data fluid and flexible.
Data visualization is deceptively complex, especially when you don’t have the right visuals or the tools to create them. Fortunately, nowadays with interactive data software and robust AI-powered tools, interacting with and expressing your data is becoming easier than ever.
Harness the power of AI and transform your spreadsheets into powerful and intelligent databases with the help of Polymer Search. Polymer helps you analyze your data deeper than ever before, and allows you to easily manipulate and express your data according to your needs.