🧭💹Business Intelligence: Perspectives (Part XIX: Data Visualization between Art, Pragmatism and Kitsch)

Business Intelligence Series

The data visualizations (aka dataviz) presented in the media, especially the ones coming from graphical artists, have the power to help us develop what is called graphical intelligence, graphical culture, graphical sense, etc., though without a tutor-like experience the process is suboptimal because it depends on our ability of identifying what is important and which are the steps needed for decoding and interpreting such work, respectively for integrating their messages in our overall understanding about the world.

When such skillset is lacking, without explicit annotations or other form of support, the reader might misinterpret or fail to observe important visual cues even for simple visualizations, with all the implications deriving from this – a false understanding, and further aspects deriving from it, this being probably the most important aspect to consider. Unfortunately, even the most elaborate work can fail if the reader doesn’t have a basic understanding of all that’s implied in the process.

The books of Willard Brinton, Ana Rogers, Jacques Bertin, William Cleveland, Leland Wilkinson, Stephen Few, Albert Cairo, Soctt Berinato and many others can help the readers build a general understanding of the dataviz process and how data visualizations or simple graphics can be used/misused effectively, though each reader must follow his/her own journey. It’s also true that the basics can be easily learned, though the deeper one dives, the more interesting and nontrivial the journey becomes. Fortunately, the average reader can stick to the basics and many visualizations are simple enough to be understood.

To grasp the full extent of the implications, one can make comparisons with the domain of poetry where the author uses basic constructs like metaphor, comparisons, rhythm and epithets to create, communicate and imprint in reader’s mind old and new meanings, images and feelings altogether. Artistic data visualizations tend to offer similar charge as poetry does, even if the impact might not appeal so much to our artistic sensibility. Though dataviz from this perspective is or at least resembles an art form.

Many people can write verses, though only a fraction can write good meaningful poetry, from which a smaller fraction get poems, respectively even fewer get books published. Conversely, not everything can be expressed in verses unless one finds good metaphors and other aspects that can be leveraged in the process. Same can be said about good dataviz.

One can argue that in dataviz the author can explore and learn especially by failing fast (seeing what works and what doesn’t). One can also innovate, though the creator has probably a limited set of tools and rules for communication. Enabling readers to see the obvious or the hidden in complex visualizations or contexts requires skill and some kind of mastery of the visual form.

Therefore, dataviz must be more pragmatic and show the facts. In art one has the freedom to distort or move things around to create new meanings, while in dataviz it’s important for the meaning to be rooted in 'truth', at least by definition. The more the creator of a dataviz innovates, the higher the chances of being misunderstood. Moreover, readers need to be educated in interpreting the new meanings and get used to their continuous use.

Kitsch is a term applied to art and design that is perceived as naïve imitation to the degree that it becomes a waste of resources even if somebody pays the tag price. There’s a trend in dataviz to add elements to visualizations that don’t bring any intrinsic value – images, colors and other elements can be misused to the degree that the result resembles kitsch, and the overall value of the visualization is diminished considerably.

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📉Graphical Representation: Perception (Just the Quotes)

"We can gain further insight into what makes good plots by thinking about the process of visual perception. The eye can assimilate large amounts of visual information, perceive unanticipated structure, and recognize complex patterns; however, certain kinds of patterns are more readily perceived than others. If we thoroughly understood the interaction between the brain, eye, and picture, we could organize displays to take advantage of the things that the eye and brain do best, so that the potentially most important patterns are associated with the most easily perceived visual aspects in the display." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"When a graph is constructed, quantitative and categorical information is encoded, chiefly through position, size, symbols, and color. When a person looks at a graph, the information is visually decoded by the person's visual system. A graphical method is successful only if the decoding process is effective. No matter how clever and how technologically impressive the encoding, it is a failure if the decoding process is a failure. Informed decisions about how to encode data can be achieved only through an understanding of the visual decoding process, which is called graphical perception." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Pie charts have severe perceptual problems. Experiments in graphical perception have shown that compared with dot charts, they convey information far less reliably. But if you want to display some data, and perceiving the information is not so important, then a pie chart is fine." (Richard Becker & William S Cleveland," S-Plus Trellis Graphics User's Manual", 1996)

"[...] when data is presented in certain ways, the patterns can be readily perceived. If we can understand how perception works, our knowledge can be translated into rules for displaying information. Following perception‐based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey the rules, our data will be incomprehensible or misleading." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"Most dashboards fail to communicate efficiently and effectively, not because of inadequate technology (at least not primarily), but because of poorly designed implementations. No matter how great the technology, a dashboard's success as a medium of communication is a product of design, a result of a display that speaks clearly and immediately. Dashboards can tap into the tremendous power of visual perception to communicate, but only if those who implement them understand visual perception and apply that understanding through design principles and practices that are aligned with the way people see and think." (Stephen Few, "Information Dashboard Design", 2006)

"Perception requires imagination because the data people encounter in their lives are never complete and always equivocal. [...] We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out picture is clear and accurate. But is it?" (Leonard Mlodinow, "The Drunkard’s Walk: How Randomness Rules Our Lives", 2008)

"[…] perceptual accuracy decreases with distance, so columns that are to be compared should be side by side. Current linked micromap software requires the user." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)

"Sparklines aren't necessarily a variation on the line chart, rather, a clever use of them. [...] They take advantage of our visual perception capabilities to discriminate changes even at such a low resolution in terms of size. They facilitate opportunities to construct particularly dense visual displays of data in small space and so are particularly applicable for use on dashboards." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"For every rule in data visualization, there is a scenario where that rule should be broken. This means that choosing the best chart or the best design is always a trade-off between several conflicting goals. Our imperfect perception means that data visualization has a larger subjective dimension than a data table. Sometimes we only need this subjective, impressionist dimension and other times we need to translate it into hard figures. Striving for accuracy is important, but it’s more important to provide those insights that only a visual display can reveal." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"Color is difficult to use effectively. A small number of well-chosen colors can be highly distinguishable, particularly for categorical data, but it can be difficult for users to distinguish between more than a handful of colors in a visualization. Nonetheless, color is an invaluable tool in the visualization toolbox because it is a channel that can carry a great deal of meaning and be overlaid on other dimensions. […] There are a variety of perceptual effects, such as simultaneous contrast and color deficiencies, that make precise numerical judgments about a color scale difficult, if not impossible." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Beyond basic charts, practitioners must also learn to compose visualizations together elegantly. The perceptual stage focuses on making the literal charts more precise as well as working to de-emphasize the entire piece. Design choices start to consider distractions, reducing visual clutter and centering on the message. Minimalism is espoused as a core value with an emphasis on shifting toward precision as accuracy. This is the most common next step for practitioners. Minimalism is also a key stage in maturation. It is experimentation at one extreme that helps practitioners distill down to core, shared practices." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Communicating data through functionally aesthetic charts is not only about perception and precision but also understanding." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Our visual perception is context-dependent; we are not good at seeing things in isolation." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"People feel data. They don’t just process statistics and come to rational conclusions. They form emotions about the data visualization. We are not informed by charts; we’re affected by them." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

"Unlike text, visual communication is governed less by an agreed-upon convention between 'writer' and 'reader' than by how our visual systems react to stimuli, often before we’re aware of it. And just as composers use music theory to create music that produces certain predictable effects on an audience, chart makers can use visual perception theory to make more-effective visualizations with similarly predictable effects." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

"We see first what stands out. Our eyes go right to change and difference - peaks, valleys, intersections, dominant colors, outliers. Many successful charts - often the ones that please us the most and are shared and talked about - exploit this inclination by showing a single salient point so clearly that we feel we understand the chart’s meaning without even trying." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

"Your eyes and your brain always notice more dynamic visual information first and fastest. The implicit lesson is to make the idea you want people to see stand out. Conversely, make sure you’re not helping people see something that either doesn’t help convey your idea or actively fights against it." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)