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Discover the 4 key traits of a great data visualization

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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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Design: Maintaining a balance between memorable and effective visualization is key. Often, we see visualizations that may be memorable, but not necessarily effective in communicating the data. A challenge with using very unusual design is that people may take notice and remember the visualization, while not being able to relate to it. On the other hand, using a very common design risks the creation of visualization that may not stand out amongst others. If deviating from the common accepted design conventions and charts, it is the responsibility of the visualizer to educate the audience about understanding the design.

 

Interactivity: With modern interactive visualizations and dashboards, a user is able to customize the visualization by adjusting the parameters provided. A visualization that is meant for an interactive experience and if not interacted with, is an opportunity lost for data communication. The “how to” and “how much” are both equally important in interactivity. A design that does not make it obvious that there is embedded interactivity may prevent a user from knowing the right way to interact with the visualization. A visualizer also needs to decide on which data dimensions a user can interact with. Too much interactivity may get overwhelmingly complex for usability. Similarly, too less interactivity may be a barrier in realizing the full potential of interactivity.

 

Color: Instead of enhancing the visual appeal, adding color to visualizations may in fact do harm, if not done right. About 8% of male population suffer from color blindness, which is the inability to distinguish between the pairs red-green or blue-yellow colors, with the former being more frequent. In addition, for someone with a normal vision as well may find it difficult to appreciate more than 6 or 7 colors in the same visualization. Using a color blind safe palette as well as showing the right number of colors is essential.

 

The idea of communicating complex datasets, utilizing visualization is very effective in data storytelling. Employing known compatible tools and interoperability, such as Microsoft Power BI and maintaining graphical integrity, design, interactivity and color are concrete steps into harnessing the power of data visualization. Embrace these fundamentals to highlight nuances and complexity for the next step of organizational understanding around your data.

Dan Yarmoluk is all about creating and driving IoT automation, condition monitoring and predictive maintenance programs with technology, analytics and business models to drive added value and digital transformation. He is an expert in field services and distribution for a wide range of industries from oil and gas to precision agriculture. He writes frequently on data science, IoT and business models in a variety of publications. He has an MBA and finishing his graduate degree in Data Science.

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