Converting data into visual charts/shapes can actually be done with one tool. Since there are too many tools, according to the usage scenarios, the mature applications are divided into three levels for the time being:
Tier 1: Data reports, infographics
These are collectively referred to as infographics. An infographic is the visualization of data, information or knowledge, which must have a clear and accurate explanation or a very complex and large amount of information.
The representative is David McCandless in the press, who has written for The Guardian, Wired, The Independent and other publications. He often presents complex and abstract information with concise and beautiful images, and combines different data to show the connections. He once said on TED:
“Visualizations are not limited to numbers, concepts are equally applicable, such as political orientation maps. I try to incorporate various political orientations into a chart and show how they permeate from government to society, culture, impact on families and individuals, This in turn affects politics.
such as graphical representation of numerical values
Eyes flow, build time and space
Production of infographics:
Using the charts that come with PPT, you can make simple and intuitive data charts, but you need art design to attach humanistic flowers and birds;
PS+AI+icon, I planned to express ideas, display content, and required materials in the early stage, and then started to build components. The details of the chart, such as the length of the histogram, are roughly proportional to the data.
This type of data does not have high requirements on the number of dimensions of the data. Most of the data is used, and the focus is also on display.
Layer 2: Actual Data Application
Application class visualization, as mentioned above, displays and analyzes a bunch of data ranging from hundreds to millions. For enterprises, because these data are generated in their own production and operation process, they can reflect the historical situation, summarize the way of development, and play an auxiliary role in the current problems or the next decision-making in the future.
Such a tool can be solved by excel, report tools can be solved, and BI can also be solved. It is not the key point here to refine the specific scenarios. You can comment and exchange below.
The usual production process is: import data (excel)/connect to database (local/server) - select charts (combinations) - set analysis dimensions - beautify the display. For example, a visual report with a strong commercial flavor like this (produced by FineReport)
Of course, such a visual report of skill requires a certain aesthetic and skilled operation. Each block in the figure is a chart control. Drag a chart control into the form (dashboard), select the data field, and then combine and arrange the layout.
The third layer: data mining, data connection, relationship transfer
This can be understood as mining relationships from massive data.
Rough idea: The raw data goes through a series of preprocessing processes such as collection, extraction, cleaning, and sorting to form high-quality data. The data is then labeled, classified or predicted as needed, and data modeling is required to extract valuable and difficult-to-find information from a large amount of complex data. (details may vary)
It is more suitable to write advanced data analysis and mining tools and open source chart controls, such as R, such as D3.
Using R for visualization, it is relatively easy to make beautiful visual charts. I recommend the book R Graphics Cookbook. There are more than 150 recipes in the book, which are enough to deal with most types of data.
D3 charts can be customized, and the richness of beautiful charts kills most chart controls, but the required level is a little high.
Finally, to quote David McCandless again, "Cool visualization is crucial to conveying ideas to the masses", don't blindly pursue aesthetics, put the cart before the horse.