Creating effective data visualization is both a science and an art. It should not only generate hypothesis and reveal insights but also communicate information effectively. It must strike a balance between the art and science elements of it. This means visualization must both:
- Be visually appealing
- Maintain fidelity to the structure of the data
Accomplishing both can be a bit of a challenge. First data visualization isn’t just about displaying data. It’s about displaying data in a way that is easier to comprehend—that’s where the real value lies.
Finding the best way to visualize the data is often considered as an after thought rather than a critical piece of the process. Poor data visualization can lead to confused messages ultimately, poorly executed and ineffective decisions. So here are five key factors to consider when designing and developing data visualization that has desired impact on the intended audience whether it is to draw insight or make better informed decision and take appropriate actions.
1. Start with a clear 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 in mind is essential when it comes to planning how you will use the visualization.
With visualization, the gaol will generally be to impart insight and knowledge gained through data wrangling and exploration to the right people, to use it and make a difference.
This strategizing should start right from the discovery phase of the project, as the first step of putting together a plan for data-driven transformation. Just as you are clear what the goals of your data gathering and analysis are, you start thinking about the form and format. followed by methods and techniques that will be most effective to present it. Further, it’s also critical to assess the technical feasibility of the technology stack you are planning to use, which includes its limitations and challenges such as performance, data processing and device compatibility.
2. Tell a clear story
Data storytelling is an essential part of getting your message and meaning. Like all stories, a data story will have a beginning, a middle and an end. And also like a lot of stories, they wont necessarily 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 novel story, we aren’t worried about giving away the ending. A data-driven story should be told more like a newspaper story – shouting your key findings in a headline at the top, and then backing it up with evidence as the reader gets drawn in.
3. Do not overdo amount of information
It can be very easy to overdo the amount of information you can cram into your graphs, infographics or dashboards. It important to identify the key messages in a data set and present them in a way that isn’t cluttered by extraneous and unnecessary details.
Overly busy graphics and visualization tire the eye and the brain – and they don’t sick in the mind nearly as those which makes a simple and concise point, backed up with relevant and up-to-date facts and stats.
4. Fit your visualization to your audience:
Data often tells different stories to different audience. Part of the skill of building a narrative with data is understanding audience. While a detailed breakdown of the different machinery parts will be valuable to an engineer, a business executive would needs a more concise but broader overview of the situation. Not if and when an individual machine might break down, but rather how the company’s machine are working as a whole, and if they are helping or hindering the company when it comes to hitting the goals.
Both kinds of information might be available in the dataset, but needs to be presented differently to meet the needs of each audience.
5. Set the Context
Usually, the story your data should be telling, is what the abstract graphs and statistics mean in the real world. This means your data must be grounded by its real-life impact – what difference will the data make to the lives of your customers or the audience you are presenting it to.
6. Consider Colors Carefully
Color is a great tool when used well. When used poorly, it can not just distract but misdirect the reader. Use it wisely in your data visualization design. Select colors appropriately. 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 colors. If colors are too similar (light gray vs. light, light gray), it can be hard to tell the difference.