Data Visualization: Data Viz vs Data Avatar

Slouching wooden figurine

Running wooden figurine

What does it mean to be a Data Visualizer?  It is mutually exclusive with a graphics/UX designer, data scientist, or a coder?  This last week I attended Action Design DC, which focused on motivating people to take action by presenting information as something familiar we could feel empathy for.  In that something, an avatar/figurine/robot, a fish tank, a smiley or frowny face, etc., we couldn’t help but recognize a reflection of ourselves because the state of that something was determined by data gathered from ourselves.  In other words, anything from our pulse to exercise time to body temperature to that last time we got up from our desk is used to determine the state of say a wooden figuring, where little activity may result in a slouching figure, while reaching a goal activity results in an ‘ative’ figurine.  I co-organize Data Visualization DC, and so for me and the people around me this presentation begged the question, “Is this data visualization?”

The term “Data Visualizer” recognizes someone who creates data visualizations, so we are really exploring what is a data visualization versus graphics design, classical graphs, or in this case shall we say “Data Avatar”?  If the previous posts can be used as evidence, to create a data visualization requires an understanding of the science and the programming language of choice, along with a certain artistic creativity.  The science is necessary to understand the data and discover the insight, a toolbox of visualization techniques helps when there is overwhelming data, and the story may have many nuances requiring sophisticated interactive capability for the user/reader to fully explore.  For example, the recent news of the NSA PRISM program’s existence has created interest in the sheer number of government data requests to Google, or a few months ago the gun debate following the tragedy in Newtown Connecticut resulted in some very sophisticated interactive data visualizations to help us understand the cause and effect relationships of states’ relationships with the law and guns.

ActionVenDiagramA UX designer may have knowledge of the origin of the underlying data but doesn’t necessarily have to, they can take what they have then focus on guiding the user/reader through the data, and they may only architect the solution.  A pure coder cares only for the elegance of the code to manipulate data and information with optimal efficiency.

Some may argue that a data scientist focuses solely on the data analytics, understanding the source of the underlying information and bringing it together to find new insights, but without good communication a good insight is like a tree falling in the woods with no-one around.  Data scientists primarily communicate with data visualizations, and so you could argue that all data scientists are also data visualizers, but vice versa?  I argue that there is a significant overlap, but they are not necessarily the same; You do not need to know how to create a spark plug in order to use it inside an engine.

The line in the sand between a data avatar and a data visualization is in compelling action versus understanding, respectively.  A data visualization is designed to communicate insights through our visual acuity, whereas a data avatar is designed to compel action by invoking an emotional connection.  In other words, from the data’s point of view, one is introspective and the other extrospective.  This presumes that the data itself is its own object to understand and to interact with.

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Sean Gonzalez

Co-Founder, Secretary at Data Community DC
Sean Gonzalez is a data scientist, data visualizer, co-founder of Data Community DC, and organizer of Data Visualization DC. Sean consults for organizations whose data has become complex over time, or organizations who have recently come into large complicated unintegrated data sets. Sean has expertise in machine learning and AI primarily from his DoD background.

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  • Jules Weiss

    Nice work, Sean:)