How to spot a great infographic: 4 data visualization tips
By: Dan Yarmoluk, Microsoft Guest Author
Although data visualization as a topic has been around for a while, in the recent era of data science, it has received increased attention. To be a successful data scientist today, not only is it necessary to be skilled in storing, managing, and analyzing large amounts of data, but also the ability to visualize the data effectively. To that end while there are excellent tools that can aid in that process, it is important to highlight the characteristics that make for an effective visualization:
Graphical Integrity: A visualization that may appear to be visually impactful but may not be completely truthful is said to lack graphical integrity. The purpose of visualization is to quickly communicate data summary or key trends in the data that allows the audience to spot trends or perform comparisons. A visualization lacking in graphical integrity may give a certain impression visually but the same may not be supported at the data level. A common metric such as the Lie Factor, which is the ratio of rate of change in the graphic to rate of change in the data, is commonly used to assess graphical integrity issues in visualizations.
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