Data visualization is the study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information"
Data visualization is closely related to information graphics, information vizualization, scientific vizualization, and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching, and development. According to Post et al. (2002), it has united the field of scientific and information visualization. As demonstrated by Brian Willison, data visualization has been also been linked to enhancing agile software development and customer engagement.According to Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose to communicate information" Indeed,Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not merely communicate clearly, but stimulate viewer engagement and attention.
KPI Library has developed the “Periodic Table of Visualization Methods”, an interactive chart displaying various data visualization methods. It details 6 types of data visualization methods: data, information, concept, strategy, metaphor and compound.
I think that Fernanda Viegas and Martin M. Wattenberg are not wrong by stating that a good visualization should stimulate viewer engagement and attention. Of course, the first aim of the creators of data visualization is that people choose to pay attention to a certain graphic and not to the other thousands of possibilities they have. And this is something that we don't do consciously, so it has to appeal more to our instinctive response more than to our rational mind. This is why it is so important for graphics to be attractive to its public.
YanıtlaSilNevertheless, once the graphic has attracted the viewer's attention, it is time for the second (and principal) aim of its creators: to transmit certain information. To achieve this, the graphic has to be clear and simple, although still keeping every important piece of information (this may require an explanatory text that accompanies the graphic).
So, in my opinion, both things are equally important: the main aim of the visualizations is to transmit information, but this wouldn't be achieved if it doesn't attract the viewer's attention.
Firstly Thank you for your comment. I also believe that the both things are important to describe the aim of the visulazations to transmit information.
SilHi lkm,
Sili agree with you. I think data attractian is often crucial.
Fernanda Viegas and Martin Wattenberg suggest in their article "How to make data look sexy", that sometimes emotions and subjectivnes can attract people and provoke them to discuss about data. But amount of emotions cannot be excessive. I attach link for this article for the case, that zou would like to read it: http://articles.cnn.com/2011-04-19/opinion/sexy.data_1_visualization-21st-century-engagement/2?_s=PM:OPINION