Tips for Better Data Visualization
Creating compelling data visualization is both a science and an art. It should not only uncover insights and awareness about the subject in question but also communicate it effectively. It must strike a balance between the art and science elements of it, which means visualization must both:
- Be visually appealing
- Be faithful to the structure of the data
Accomplishing both can be a bit of a challenge. First, data visualization isn’t just about representing data. It’s about presenting data in a way the target audience can absorb easily and follow the cue along the way — that’s where the real value lies. It also holds power to expands its reach to a broader audience.
Finding the best way to visualize the data is often considered an afterthought rather than a critical step in the process. Most commonly, poor data visualization can lead to confusing messages, ultimately poorly executed and ineffective decisions. Here are five key factors to consider when designing and developing data visualization that has the desired impact on the intended audience, whether it is to draw insight or make well-informed decisions before they act.
1. Start with a strategy
“Getting visualization right is much more a science than an art, which we can only achieve by studying human perception” -Stephen Few
As with every aspect of design and development, having a clear strategy and end goal is essential when planning how you will use the visualization output. With visualization, the goal will generally be to present information and learning gained through research, data mining, and exploration to the right people to whom it will benefit and make a difference.
Planning for visualization should start right from the discovery or exploratory phase of the project, as the first step of putting together a plan for data-driven visualization. Just as you are clear on the goals of your data wrangling and analysis, you have to start thinking about the form and structure, then the methods and techniques that will be most effective in presenting it.
Additionally, some experimentation, in the beginning, will give a lot of foresight on the right tools and frameworks that will be suitable for the visualization design. It will also come helpful when you are dealing with challenges down the line, such as performance, rendering, compute power, and device compatibility, to name a few.
2. Tell a clear story
Data storytelling is an indispensable part of getting your message across in any visualization project. Like all the stories, a data story will have an opening, middle and conclusion. Further, like a lot of stories, they won’t certainly come in that order.
In fact, with a data story, it’s often best — essential even to start at the end. That’s because, unlike a movie or fiction, we aren’t worried about giving away the ending. A data-driven story should be narrated more like a story in a newspaper article — shouting your critical findings in a headline at the top, highlight significant pieces, and then backing it up with evidence, stats or subtle facts as the reader gets drawn in.
3. Less is more
Limiting the amount of information is vital. It can be very easy to overdo the amount of information that you can cram into your graphs or infographics. But it is necessary to identify the key messages in a data set and present them in a way that isn’t cluttered by too much information. Overly busy graphics and visualization tire the eye and the brain — and they don’t stick in mind nearly as those who make a single and straightforward point
4. Tailor it to the audience
Data often tell different stories to a different audience. Part of the skill of building a narrative with data is an understanding of the audience. For example, while a detailed breakdown of the various machinery parts will be invaluable to an engineer, a business executive would need a more concise and specific but broader overview of the situation. For him, it’s not if and when an individual machine might break down, but rather how the company’s machine works as a whole, and whether they help or hinder the company when it comes to hitting the goals.
Both kinds of information might be available in the dataset but need to be presented differently to meet each audience’s needs.
5. Set the Flow
Context helps set the tone and flow of your data story. Usually, the story your data visualization should be telling is what the abstract graphs and charts mean in the real world. It means the real-life impact of it must ground your data — what difference will the visualization make to the end-user you are presenting to?
6. Set Colors Wisely
Color is a great tool when used well. When used poorly, it can not only distract but misdirect the reader and be a source of confusion. Select color palettes with good judgment or sense and take color contrast into considerations. Some colors stand out more than others, giving unnecessary weight to that data. Instead, use a single color with varying shade or a spectrum between two analogous colors to show intensity. Make sure there is sufficient contrast between primary colors. If colors are too similar (light blue vs. light, light blue), it can be hard to tell the difference.