Data visualization is an art and a science. Much has been researched, studied, and published on the topic of what a great visualization is composed of. In this section, let's attempt to clarify some of these findings via some brief explanations, as shown in following list. As with everything in life, your mileage may vary, and there are always exceptions to rules. However, consider these general rules as condensed, combined, and paraphrased here as compared to the industry standards and academic research as you develop visualizations:
- Readers should be able to understand data visualization quickly. Make it easy for your consumer to read your data visualization. Don't use font sizes that are less than 10 pt if you can manage it, and use common fonts that are designed for ease of use. The key is that visualizations are not too flashy, complex, or artistic just for the sake of it. Do not use 3D in any way, it distracts and can mislead readers by distorting data. Follow the form over function principle, and the amount of time required to understand the data will work itself out.
- Consider the format for the reader. Print, computer screens, and mobile devices use large fonts for titles, and consider the format of consumers' devices. Scrolling is not good in most cases, and you should manage white space carefully.
- Every element should have a reason for being in a virtualization. Choose colors carefully. Use colors for categories instead of quantities; don't use colors randomly. Shapes, colors, legends, and labels should add to the understanding of the visualization.
- Be consistent in the placement of objects, elements, and colors. Your work is your digital brand, it literally is your product. Your consumers may not know you in person. Their perception of you and your skills is determined by how they relate to your visualizations. Do not use variety for variety's sake. The organization of elements and colors is important, as it helps readers navigate through the visualization.
- Use the correct context for data and consistent axes for elements on a chart. Help readers understand the data and what they are looking at. Do not mislead readers by using mismatched axes for charts that compare the same measure, nonzero axes, and other misleading tactics.
- Simplicity comes from clarity, and this helps readers understand data. Keep it simple! Some data sets are inherently complex. In such cases, try to show a part of the data set, or break up the visualizations to show the various aspects of the data. Be clear in your organization and approach, and simplicity (for readers) will follow.
- Consider calculating differences for readers instead of plotting multiple lines or bars in an effort to show the comparisons and differences. For example, a line chart of the city budget versus the city expenditures may be interesting, but an even better option is to use a bar chart, with a baseline set to the budget and bars above or under the baseline. Alternatively, you can set reference lines in a chart by using mean or target values.
- Choose the best type of view (chart) for the data. Read the following sections and understand the key elements of every major chart type and what the main objectives of each chart type are.
- Understand the different visualization primitives (color, length, area, shapes, position, direction, and angle) and how humans perceive them in terms of data visualization.
- Experiment with the Tableau Public Show Me feature to choose a view type and make changes of your own, such as selecting the shelf items that you need to include, and their characteristics. Experiment and refine, and think in terms of a visualization cycle of exploration rather than a linear route from data to visualization creation.
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.