Right Visualization Series with The Data Whisperer &#8211; Flying Snakes
Highlighting a large volume of time series data results in a fast, yet virtually impenetrable graphic. Twisting, turning, and slithering through the air, I call this flying snakes. When asked to describe the pattern, a sharp reader might call out the mysterious orange hued line that disappears in September and rebounds in December. Or perhaps, the lack of pattern among the four products ranging from zero to ten. Most will say, “I have no idea.” The few seconds the analyst took to create flying snakes, will result in a large effort for the reader to decode. But more likely, the reader will ignore it. Small multiples provide a more inviting approach. Within the same amount of space as the first graphic, here each variable has its own chart that facilitates intra and inter comparison. Note that the axes are all the same and each chart is clearly labeled. I have also dropped out the low volume products, which could be mentioned in a footnote. The reader can answer questions like which is: stable, growing, falling, seasonal, and just plain crazy? Is there one that might have a data problem? Intuitive, high-density graphics like small multiples take more time and thought for the analyst to create, but provide the reader with a high degree of intimacy with the data so they can better understand your story.
Highlighting a large volume of time series data results in a fast, yet virtually impenetrable graphic. Twisting, turning, and slithering through the air, I call this flying snakes.  When asked to describe the pattern, a sharp reader might call out the mysterious orange hued line that disappears in September and rebounds in December. Or perhaps, […]
Highlighting a large volume of time series data results in a fast, yet virtually impenetrable graphic. Twisting, turning, and slithering through the air,
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["ViT", "R", "programming_languages:R", "AI"]
["AI", "R", "ViT", "programming_languages:R"]
Chris Arnold (popularly known as the Data Whisperer) has been making data talk since the age of mainframes. Spanning quantitative and qualitative analysis, he has built high functioning quant teams and data mart solutions across pharmaceutical, automotive, and financial services industries; within risk, operations, and marketing domains. He has also conducted ethnographic research in the Amazon basin and focus groups for ‘pay now-die later‘ funeral insurance plans.

He currently leads Wells Fargo’s enterprise-wide Knowledge Service practice, with teams in India and the Philippines. Besides, Chris is a Lecturer of Data Visualization in UC Berkeley’s data science program.