Data visualization can achieve simple, substantial, efficient and aesthetic visualization:

Data visualization can achieve simple, substantial, efficient and aesthetic visualization:

To put it simply, good data visualization is the same as good products, which have a friendly user experience and cannot make people spend time looking at things in a confused way, or even be misled to draw wrong conclusions. Deliver the most accurate information in the simplest way and save people time to think. The simplest way is the most reasonable chart, which needs to be selected according to the comparison relation, data dimension, data amount.

To enrich a data analysis report or to explain a problem, it is rarely possible to complete a single graph, which requires multiple indicators or different dimensions of the same indicator to cooperate with each other to support the analysis concluded.

Efficient and successful visualization, while seemingly simple but profound, allows the observer to gain insight into the facts and generate new understanding, and the manager can quickly find and discover the decision-making path along the visual path you have planned.

Beauty needs beauty as well as accuracy and efficiency. The beauty is divided into two levels. The first level is the overall harmonious beauty with no extra elements. The elements in the graph such as a coordinate axis, shape, line, font, label, and title layout are properly arranged. The second layer is the visual beauty that makes a person cheerful, the color application is proper. Grasp the use of colors in visual elements to make the graphics more vivid, interesting, and the information expressed more accurate and intuitive. Color can help people to deeply classify, emphasize or dilute information, and the manifestation of vivid and interesting visual works often brings visual effect to the audience. Harmonious beauty is the foundation of visual beauty.

Also completes the data visualization is not easy, need to have a certain ability of data analysis, skilled use of visualization tools, good fine arts accomplishment, good user experience feeling, also can be transposed to the viewpoint of the audience's own works, light has theory is far from enough, still need a lot of practice training, solidify the theory into their feelings.

The data is not accurate and the conclusion is not clear, so the biggest difficulty of data visualization lies in the basic work beyond data visualization. If the data collection and data analysis are not done well, visualization is useless. Data visualization is to show complex data and information in highly abstract graphs, which requires logic and rigor. Multiple dimensions, multiple variables, and uncertain what information should be presented? There is too much data, and interactive rendering visualization is required. For example, we can make full use of hierarchical include relationships to display charts at different geographical levels. Compared with the UI graphical interface, charts have limited text and graphical guidelines, which cannot well explain the context of data.



Charts are highly abstract and require high quality of readers. Readers also need to understand the basic knowledge of comparison, similarities, and differences between the various charts.
It is not easy to select the right chart, all kinds of charts have their own advantages and limitations, such as the general histogram, grouping histogram, stacked histogram, horizontal bar histogram, bidirectional histogram and so on.

There are so many details to consider in a chart. The layout, elements, scale, units, legend, and so on all need to be reasonable. The details are not well processed, which will affect the visualization effect. For example, the broken line is too thin to be easy to observe, and the line is too thick and smoothes out the trend details. More serious problems may mislead the audience. For example, the scale selection is unreasonable and the line is too steep.


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