"For a visual to qualify as beautiful, it must be aesthetically pleasing, yes, but it must also be novel, informative, and efficient. [...] For a visual to truly be beautiful, it must go beyond merely being a conduit for information and offer some novelty: a fresh look at the data or a format that gives readers a spark of excitement and results in a new level of understanding. Well-understood formats (e.g., scatterplots) may be accessible and effective, but for the most part they no longer have the ability to surprise or delight us. Most often, designs that delight us do so not because they were designed to be novel, but because they were designed to be effective; their novelty is a byproduct of effectively revealing some new insight about the world." (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)
"The key to the success of any visual, beautiful or not, is providing access to information so that the user may gain knowledge. A visual that does not achieve this goal has failed. Because it is the most important factor in determining overall success, the ability to convey information must be the primary driver of the design of a visual." (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)
"A beautiful visualization has a clear goal, a message, or a particular perspective on the information that it is designed to convey. Access to this information should be as straightforward as possible, without sacrificing any necessary, relevant complexity. [...] Most importantly, beautiful visualizations reflect the qualities of the data that they represent, explicitly revealing properties and relationships inherent and implicit in the source data. As these properties and relationships become available to the reader, they bring new knowledge, insight, and enjoyment." (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)
"The first requirement of a beautiful visualization is that it is novel, fresh, or unique. It is difficult (though not impossible) to achieve the necessary novelty using default formats. In most situations, well-defined formats have well-defined, rational conventions of use: line graphs for continuous data, bar graphs for discrete data, pie graphs for when you are more interested in a pretty picture than conveying knowledge." (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)
"A persuasive visualization primarily serves the relationship between the designer and the reader. It is useful when the designer wishes to change the reader’s mind about something. It represents a very specific point of view, and advocates a change of opinion or action on the part of the reader. In this category of visualization, the data represented is specifically chosen for the purpose of supporting the designer’s point of view, and is presented carefully so as to convince the reader of same." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"All sorts of metaphorical interpretations are culturally ingrained. An astute designer will think about these possible interpretations and work with them, rather than against them." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"An informative visualization primarily serves the relationship between the reader and the data. It aims for a neutral presentation of the facts in such a way that will educate the reader (though not necessarily persuade him). Informative visualizations are often associated with broad data sets, and seek to distill the content into a manageably consumable form." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"Bear in mind is that the use of color doesn’t always help. Use it sparingly and with a specific purpose in mind. Remember that the reader’s brain is looking for patterns, and will expect both recurrence itself and the absence of expected recurrence to carry meaning. If you’re using color to differentiate categorical data, then you need to let the reader know what the categories are. If the dimension of data you’re encoding isn’t significant enough to your message to be labeled or explained in some way - or if there is no dimension to the data underlying your use of difference colors - then you should limit your use so as not to confuse the reader." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"Communication is the primary goal of data visualization. Any element that hinders - rather than helps - the reader, then, needs to be changed or removed: labels and tags that are in the way, colors that confuse or simply add no value, uncomfortable scales or angles. Each element needs to serve a particular purpose toward the goal of communicating and explaining information. Efficiency matters, because if you’re wasting a viewer’s time or energy, they’re going to move on without receiving your message." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"In data visualization, the number one rule of thumb to bear is mind is: Function first, suave second." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"Practically speaking, this pattern and pattern-violation recognition has two major implications for design. The first is that readers will notice patterns and assume they are intentional, whether you planned for the patterns to exist or not. The second is that when they perceive patterns, readers will also expect pattern violations to be meaningful." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"The advantage of redundant encoding is that using more channels to get the same information into your brain can make acquisition of that information faster, easier, and more accurate." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"The best visualizations will reveal what is interesting about the specific data set you’re working with. Different data may require different approaches, encodings, or techniques to reveal its interesting aspects. While default visualization formats are a great place to start, and may come with the correct design choices pre-selected, sometimes the data will yield new knowledge when a different visualization approach or format is used." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"[...] the terms data visualization and information visualization (casually, data viz and info viz) are useful for referring to any visual representation of data that is: (•) algorithmically drawn (may have custom touches but is largely rendered with the help of computerized methods); (•) easy to regenerate with different data (the same form may be repurposed to represent different datasets with similar dimensions or characteristics); (•) often aesthetically barren (data is not decorated); and (•) relatively data-rich (large volumes of data are welcome and viable, in contrast to infographics)." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"Ultimately, the key to a successful visualization is making good design choices." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
[...] visual art, primarily serves the relationship between the designer and the data. [...] it often entails unidirectional encoding of information, meaning that the reader may not be able to decode the visual presentation to understand the underlying information. [...] visual art merely translates the data into a visual form. The designer may intend only to condense it, translate it into a new medium, or make it beautiful; she may not intend for the reader to be able to extract anything from it other than enjoyment." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
"[...] you should not rely on social or cultural conventions to convey information. However, these conventions can be very powerful, and you should be aware that your reader brings them to the table. Making use of them, when possible, to reinforce your message will help you convey information efficiently. Avoid countering conventions where possible in order to avoid creating cognitive dissonance, a clash of habitual interpretation with the underlying message you are sending." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)
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