It was interesting to see that although data visualization deals with data which is commonly understood as something quite objective, there is no definitive answer in presenting them. There are strategies to not only make data more easily (or difficult depending on intention) interpreted, but also instill a human reaction: most commonly a certain emotion or opinion.
The reading first examines what factors there are in "bad" data representations. The first is aesthetics that deals with simplifying representation. The more unnecessary visual information, the harder to read the data. However it also raises the question of memory and how merely simple graphics may not be memorable for the general public and how humans ultimately rely on visual stimuli and images to retain the data. Secondly, bad data is discussed where not the best kind of data is selected to represent a relationship.
Ultimately, a lot of data visualization is comparing data in terms of relationships: correlation being the main one. In the translation of data to graphical form, the author has lots of visual ways to intensify or neutralize certain relationships. For example, making trend lines steeper by narrowing the gaps of a certain axis. More interestingly, with optical illusions that can make the viewer omit certain information involuntarily.
Personally, this raises significant issues about ethics. We rarely see just a strand of raw data. Data is our primary way to make sense of the world but they are processed by scientists and graphic designers for the general public to comprehend quickly. I am sure most designers are aware of the power they hold in presenting such information. I fear to what extent the world can be brainwashed with malicious data visualization and how the general public can be more educated and aware that no data is ever objective.