Data Science and the Art of Data Visualization


In a previous post, I explored the role of the data storyteller in big data analytics, cautioning that a good editor is needed, and noting that a good data scientist edits with experiments.

Vincent Granville’s recent interview with Justin Langseth, the CEO and co-founder of the data visualization startup Zoomdata, explored another big data analytics role: the Data Artist.  “As much as organizations need people who can analyze data and draw conclusions from their data,” Langseth explained, “they also need individuals who can paint a picture with that data so that everyone can easily understand the conclusions.”

Langseth emphasized “the living, breathing nature of data.  Data isn’t static, and that means data visualizations shouldn’t be either.”  Data artists “use their data paint to create interactive and fresh visual views” of the dynamic data that is flowing throughout the organization.


In his recent blog post about data flow architecture, Robin Bloor explained that our applications (e.g., transactional apps, PC apps, office apps, BI apps, mobile apps, etc.) interact via the data flowing “between these apps, and sometimes through human beings to these apps (e.g., I get an email and it prompts me to update some data in some app).  The apps are all interrelated to some degree within the business they serve.  So we need to provide for a horizontal data flow.  Data (or possibly data plus instructions) comes into the business and we need to manage its passage from one application to another in terms of the service levels we need for applications to work well.”

This is why data visualization is crucial to our ability to see whether our applications are working well.  To slightly paraphrase what Morpheus told Neo in The Matrix, “Unfortunately, no one can be told what the data is.  You have to see it for yourself.”

“Instead of reconstructing representations of what they see with their eyes,” Langseth explained, “data artists need the ability to create new ways for our eyes to see the massive flows of data within an organization.  They need to be able to portray facts, flow, and patterns that are not necessarily visible to the average business decision maker.  The visualizations these artists create can be a powerful way to translate terabytes of data into meaningful business information.”

“It is art, and not science, that is the means by which we express what we see,” Jonah Lehrer wrote in Proust Was a Neuroscientist.  “The artist describes what the scientist can’t.”  Lehrer was not in any way being anti-science.  Instead he was echoing the call of C. P. Snow to form a third culture that closes the traditional communications gap between scientists and artists.  “When we venture beyond the edge of our knowledge,” Lehrer explained, “all we have is art.”

Often we look to data to help us explore beyond the edge of our knowledge.  Data scientists help us with how we analyze data.  Data artists help us with how we visualize data.  Just as a picture is worth a thousand words, a visualization is worth a thousand data points, brightly lighting our way so that we don’t lose sight of the point of big data analytics.

About Jim Harris

Jim Harris is the Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ), which is an independent blog offering a vendor-neutral perspective on data quality and its related disciplines. He is a recognized thought leader with more than 20 years of enterprise data management industry experience. Jim Harris is an independent consultant, professional speaker, and freelance writer for hire.

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