A Guide To Creating Effective Charts - Issue 192
How to choose the right chart for analysis and avoid common data visualization mistakes
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The event brings together over 250 data experts and more than 100 sponsors and exhibitors from analytics, data science, BI, data quality, monitoring, and data engineering. The conference will feature keynotes, case studies, panel discussions, technical talks, Q&A sessions, fireside chats, and roundtables.
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Today, data storytelling can reach new heights with all the fancy data processing and visualization tools, letting you create charts in seconds. With that advantage, however, it can often lead to overloaded dashboards with an unnecessary volume of charts that fail to illustrate data effectively and can overwhelm the audience.
In this post, I’ll cover basic visualization principles for selecting the appropriate types of charts - comparing when to use line charts vs. area graphs, tables vs. pie charts, and column charts vs. horizontal bar charts. I will also highlight common charting mistakes to avoid and share my favorite sources for learning best practices in visualization.
When selecting the appropriate chart for analysis or metric, there is a world of options for best practices. Choices range from tables and bar charts to matrices and from bold and bright color palettes to soft and subtle ones. This makes the analytics role very challenging - as a messenger, your success hinges on your ability to communicate the story in a manner that is both appealing and credible to the audience.
While we may have different preferences for chart types and formatting, rules are rules, and we must follow data visualization concepts.
The decision on which chart to use depends on several factors, including the size and format of your data, the number of variables involved, and the nature of their relationships.
How to pick the right chart
The best guide on visualizations available is this “chart chooser,” which you can download here:
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