Top Data Visualization Mistakes and How to Avoid Them
Blog: The Tibco Blog
Reading Time: 2 minutes
Visually communicating quantitative content is key for organizations that are working towards digital transformation. Looking for insights by turning numerical data into graphics, or data visualization, is not an easy task. But neither is poring over spreadsheets and lengthy datasets looking for an insight’s needle in the data haystack. When done right, visual analytics can quickly and easily highlight trends and convey end results to users in highly powerful and influential ways. In this blog, we’ll give you a quick overview of what our advanced practitioners have picked out as the most common data visualization mistakes, and give you tips and tricks on how to avoid them.
Know your audience
One of the most important starting points is realizing that you cannot create visualizations that meet the needs of any and all audiences. When designing, you need to hone in on the questions and needs of your target audience. Imagine you are presenting to a group of environmental experts and you show them a simplified weather map. That type of simple, uninsightful information would cause your audience to question your understanding. However, showing them an enriched, dynamic visual that tells the story of how nations with the highest emissions also have the highest rate of climate change, to set up a conversation about causation versus correlation – now that would be something that would get their attention. Targeting the right message to your audience is the first crucial step when designing impactful data visualizations.
Choosing the wrong type of data visualization
Deciding on the type of visualization can be hard since there are so many “pretty” but uninformative visualizations. Strive for clarity and simplicity. Great rules of thumb for this are:
- To highlight and compare values, use a bar chart.
- To show change over time, or how one value affects another, use a line graph.
- To show relationships between values, use a scatter chart.
And please always assume that your viewers are not using a high-resolution screen. Most will be looking on their mobile devices so it’s best to design with that in mind. We recommend that you choose a data visualization tool that will actually recommend the best visualization for you like Spotfire.
Edit to Emphasize Ideas
Again, when you have so much data, it’s tempting to show it all at once. But that will just confuse the audience. The main point of data visualization is to make the point the presenter is trying to prove clearer. There are some basic rules that can help, such as:
– Don’t put every insight into one chart
– Only use written descriptions if they are needed to clarify what’s shown
– Use color sparingly and only to support your main points
It’s up to you to select and develop the visualizations that your company needs, so we recommend you use a tool like Spotfire that can help you automatically wrangle and prepare your data, select the best visualization type, and uncover insights from data from all your sources including traditional or Internet of Things (IoT) streaming sources.
For more data visualization mistakes and how to avoid them from the experts, please download this whitepaper: “Five Common Data Visualization Mistakes and How to Avoid Them.”
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