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"Time management: Any creative work quickly swallows up all the available time." I believe in the 80 / 20 rule in that 80% of a project takes up 20% of the time and the final 20% part of a project takes up 80% percent of the time.  Research for me is the 20% of the time part and the visualization was the 80% of the time.  This proved true in doing our Du Bois visualizations.

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"Principle 3: Good Visualization Design Is Elegant - Elegance is concerned with creating an aesthetic that will appeal to your audience and endure, sustaining positive sentiment throughout the experience, far beyond just the initial moments of engagement." - For me, elegance means design that is sleek and minimalist. The facts should stand out and be readily consumed by the viewer and you should not have to dig into the graphic to get the information.

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“Rather than get consumed by the inevitability of biases rippling through your work, and perhaps seeing this as good reason not to undertake it, your focus is better directed towards ensuring your path is trustworthy. In the absence of a single objective truth, what can you do to secure trust in your subjectively selected truth?”

This quote is a good reminder that we all have biases, and these present themselves in our work based on the choices we make. It is important to remember that no design decision is without consequences. For everything a designer chooses to include in a data display, there is something they omitted. These should be conscious decisions, not arbitrary. A little after this quote in the reading is my new favorite Dutch proverb, “trust arrives on foot and leaves on horseback.” Care in design decisions will support and sustain trust. Carelessness can have long lasting implications in how the audience perceives the designer’s work.

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“You are the custodian with a responsibility for being faithful to the data you have and the subject it embodies. You need to be careful with your handling of the data and transparent with what you decide to do with it.”

Here is another quote that serves as a reminder to be careful, this time with the data itself. Making sure to provide enough context regarding the data is important to avoid ambiguity and confusion as the audience interprets the data display.

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“As you will see later, data itself can only tell us so much; often it just tells us where interesting things might exist, not what actually explains why they are interesting.”(page 36)

This quote rings very true for me, in my experience working with data. I can think of times where my team has put in a lot of effort to collect data and then when we finally are able to analyze it we are left with more questions and unknowns. This quote is helpful for me in thinking of data visualization because there is a drive to explain everything with data visualization and it’s helpful to remember that this is impossible. And that if a data leads to more questions that is a good thing.

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“Working with data, the second stage of the process, is arguably where trust is most at stake. You are the custodian with a responsibility for being faithful to the data you have and the subject it embodies . You need to be careful with your handling of the data and transparent with what you decide to do with it. “ (page 42)

This quote is helpful because this feels like the heart of what data visualization is for. Data visualization can make information that is inaccessible and make it accessible. And that is a responsibility, too, to use data visualization to be truthful and honest.

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"For those new to the field, one of the first things to grasp is the idea that any notion of perfect in data visualization does not exist." This was a very good start to the reading for me because it set the tone. While I have viewed many, many graphs and other types of data visualizations I have never given too much thought to what goes on behind the scenes. I always just took what I saw at face value.

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"Judging relevance is a subjective and contextually driven matter relating to the potential usefulness of your visualisation: am I providing my audience with access to the most useful understanding about the subject?" While there were questions I could ask myself in the reading up to this point this is the one that struck me as to the responsibility of the person creating the visualization.

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"the essence of quality in architecture is framed by the social relevance of the work, not the eventual form or workmanship towards that form" - the author connects this idea to his eventual three-pronged qualities of good data visualization: that the product is ultimately trustworthy, accessible, and elegant.  These seem like useful measures by which to evaluate data visualization decisions.

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‘Kill your darlings’: “When something is not working, learn to kill it.  Otherwise, such preciousness will impede the quality of your work.  Being blind to things that are not working, or ignoring constructive feedback from others, will prove destructive.”

I selected this quote primarily because it is a problem I suffer from.  I often struggle to ‘let go’ an idea or design plan that is not working as intended.  Rather, my knee-jerk reaction is to push that much harder in order to force the idea/design to work.  In some instances, this perseverance may be appropriate and applauded, however for other occasions it becomes foolish and counterproductive.  This quote serves as a healthy reminder of knowing the difference between “pride and prejudice”, to quote Jane Austen.

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I have selected a two-part quote: “There is an important distinction to make about the relationship between trust and truth.  Achieving trust is an aim, presenting truth is an obligation.” Related to this idea about building trust: “You are the custodian with a responsibility for being faithful to the data you have and the subject it embodies.  You need to be careful with your handling of the data and transparent with what you decide to do with it.”

At a time when ‘truth’ and ‘facts’ have been distorted and intertwined with personal preference and biased beliefs, the need to clarify the importance of building trust and protecting the truth couldn’t be greater.

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"Trust is something a visualiser
must try to nurture through accuracy and transparency, eliminating doubts or legitimate dispute from a viewer."

Credibility is huge in the presentation of data. Being aware of possible misinterpretation of a presentation is key and, obviously, finding all ways to avoid data inaccuracy is huge. Like Kirk says, trust is slow to be established, but easily dashed.

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“When working with a complex or complicated subject, your instinct might be to seek to simplify it. Simplifying is a reductive process that translates a complex or complicated state into a simplified form, usually by eliminating details or nuance. There are situations that will warrant making the process of understanding quicker and easier, though this is not a universal goal. Not everything can or should be simple.The process of simplification might risk the subject being oversimplified to the point of obscurity. In removing important subtleties and technicalities this can be just as detrimental to the perceived accessibility as leaving a complex or complicated subject too intellectually demanding. What if your audience are sufficiently sophisticated with the capability and motivation to handle the learning process required in grasping a hard topic? By simplifying things, they would be denied that learning opportunity and denied access to relevant understanding. An audience in this case may justifiably feel patronised when faced with an oversimplified portrayal.”

This explanation of when and how to strive for simplicity when linked to the various needs of differing audiences helps me think about how to frame the level of explanation in each data visualization.

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“Elegance is most conspicuous when it is missing. This is when a visualisation's design lacks cohesion and inspiration, especially across the colour and composition elements that so inform its appearance. By contrast, as expressed by Rams' principle 'Good design is as little design as possible', elegant design accelerates you to the content and to understanding. In his book The Shape of Design, designer Frank Chimero references a Shaker proverb: 'Do not make something unless it is both necessary and useful; but if it is both, do not hesitate to make it beautiful.' In serving the principles of trustworthy and accessible design, you will hopefully have covered both the necessary and useful. As Chimero suggests, if we have served the mind, our heart is telling us that now is the time to think about beauty.”

I love the equal emphasis on elegance and beauty as having value on par with need and usefulness. It reminds me that data visualization can and should appeal to the mind and heart with content and elegant beauty.

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'You cannot expect just to land on a great solution by
chance if your working practices are chaotic and confused. You will be aided by some additional
practical tips and good habits to employ across the whole process.
The quality of your decision making is the main difference between a visualisation that
succeeds and one that fails."
This quote seems pretty obvious, but since visualization is also artistic, it seems worth connecting this to the artistic process. Just because one is 'making pictures,' so to speak, doesn't mean it is not a rigorous process. Representing emotions and human inner conditions through painting, drawing, etc. requires a fine sensitivity to detail and nuance, both inner and outer, or the effectiveness of the method of communication. I'm seeing that data visualization is very similar, except what is being expressed can be objectively understood by others even before you represent it visually. The visual is an aid, and a way of framing and focusing, of telling the story you want it to tell.
Slipping in another quote on this theme:
'You need a design eye to design, and a non-
designer eye to feel what you designed. As
Paul Klee said, "See with one eye, feel with
the other."' Oliver Reichenstein, Founder of
Information Architects (iA)

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"data itself can only tell
us so much; often it just tells us where
interesting things might exist, not what
actually explains why they are interesting.
Talk to smart people who know a subject
better than you or people who do not know
the subject but are just smart."
What a great point. It is easy to think we know what we need to know about the data we are using, especially if it is in our own field of education. But there are so many nuances. For example, I have experience DOE staff saying that they don't think we should really look at achievement data because the tests are not accurately reflecting the real knowledge and experience of the students. That's a tough one to hear, but very interesting to note and consider when choosing to represent that kind of data to particular audiences.

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"There will never be just one single possible solution to a problem" (p.31).

This speaks to me for a host of reasons. The chapter reinforces the notion that visualizing data effectively is a process, that involves a series of steps including having curiosity, wrangling with the data you have, juggling the myriad options of what to do and determining a shape for your design solution. In other words, there are many creative ways to present data. An important part of the "process" includes making a series of decisions and organizing one's thinking into a cohesive picture that tells a story for an audience in a specifically orchestrated way.

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"The term process contrasts considerably with procedure.  The process outlined ... provides a framework for thinking, rather than instructions to learn and follow.  A good process offers adaptability and removes the inflexibility of a defined procedure.... There will be plenty of occasions when it is necessary to revisit decisions or redo activities in a different way, especially in light of mistakes...What is more important is how gracefully you fail and how quickly you recover" (p. 31).

Feeling unsure about how to piece together or tell the story of data can be rather ubiquitous - though this is not what is often expressed. Too often the interpretation of data is reported in such a manner that is suggestive of the absolution of the analysis or conclusions being offered. It is important to keep in mind that maybe it makes sense to consider a different lens or explore different conclusions when analyzing or synthesizing the data.

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"You need a design eye to design and a non designer eye to feel what you have designed. As Paul Klee said, "See with one eye, feel with the other." This quote reminds me the data visualization process is creative, critical and emotional all at the same time. Is my work eye catching and simple, purposeful and provoking?

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"In what format will your viewers need to consume your work? Are they going to need work created for a print output or a digital platform? Does this need to be compatible with a small display as on a smartphone or a tablet? Will any viewers have visual impairments that need accommodating?"
I need to remember Universal Design, as well as how I will make descriptors of my data visualizations for screen readers to aid understanding and accessibility for those with visual impairments.