Principles of Good Visualization Design

Trustworthy

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Principle 1: Good Data Visualization is Trustworthy

The notion of trust is uppermost in your thoughts in this first of the three principles of good visualization design. This maps directly onto one of Dieter Rams' general principles of good design, namely that good design is honest.

Trust vs Truth

This principle is presented first because it is about the fundamental integrity, accuracy and legitimacy of any data visualization you produce. This should always exist as your primary concern above all else. There should be no compromise here. Without securing trust the entire purpose of doing the work is undermined. There is an important distinction to make between trust and truth. Truth is an obligation. You should never create work you know to be misleading in content, nor should you claim some-thing presents the truth if it evidently cannot be supported by what you are presenting. For most people, the difference between a truth and an untruth should be beyond dispute. For those unable or unwilling to be truthful, or who are ignorant of how to differentiate, it is probably worth putting this book away now: my telling you how this is a bad thing is not likely to change your perspective. If the imperative for being truthful is clear, the potential for there being multiple different but legitimate versions of 'truth' within the same data-driven context muddies things. In data visualization there is rarely a singular view of the truth. The glass that is half full is also half empty. Both views are truthful, but which to choose? Furthermore, there are many decisions involved in your work whereby several valid options may present themselves. In these cases, you are faced with choices without necessarily having the benefit of theoretical influence to draw out the right option. You decide what is right. This creates inevitable biases - no matter how seemingly tiny - that ripple through your work. Your eventual solution is potentially comprised of many well-informed, well-intended and legitimate choices - no doubt - but they will reflect a subjective perspective all the same. All projects represent the outcome of an entirely unique pathway of thought. You can mitigate the impact of these subjective choices you make, for example, by minimizing the amount of assumptions applied to the data you are working with or by judiciously consulting your audience to best ensure their requirements are met. However, pure objectivity is not possible in visualization. Rather than view the unavoidability of these biases as an obstruction, the focus should instead be on ensuring your chosen path is trustworthy. In the absence of an objective truth, you need to be able to demonstrate that your truth is trustable.

Trust has to be earned, but this is hard to secure and very easy to lose. As the translation of a Dutch proverb states, 'trust arrives on foot and leaves on horseback'.

Please Explain what means by "Good Data Visualization is Trustworthy." (In 150 Words)