During the epidemic, the digital age accelerated, and most enterprises were left behind before they were fully ready for the digital transformation.

As the digital economy has become a part of the socio-economic center, data has become a de facto social component. With the help of a large amount of data, enterprises can analyze almost all business data and turn the previously imagined business model into reality.

In the past, people can only rely on text when analyzing the development of an enterprise, and often turn a simple enterprise business analysis into several pages or even dozens of pages of digital content, which is not only easy to produce errors and omissions, but also waste all managers' time on analyzing numbers.

The chart made by data visualization is no longer composed of data and text like the traditional analysis scheme, but integrates all the data and text that are difficult for human beings to deal with into an intuitive chart to simply and effectively show the business information of the enterprise. dig out the hidden value behind the development.

In the traditional business model, because it is difficult for enterprises to quantify their development and management, they rely more on experience to make decisions, which is easy to produce risks, and it is not easy to explore new development models.

After the initial completion of the information construction, enterprises begin to store business information, which is stored in the data warehouse in the form of data. Therefore, for current enterprises, * data as the business core and visualization as the means of transformation * , it can help enterprises to complete the digital transformation to the greatest extent and truly complete the transformation in the new era.

Enterprise managers can view business execution through data visualization, and then review and summarize the problems found. * data visualization can easily distinguish the logical relationships between different business data because various charts can be related to each other.*

At this stage, the digital transformation has just started, and the tools that can complete data visualization are still limited. Generally speaking, visualization tools can be divided into * personal visualization tools, enterprise visualization tools, or code visualization tools and low code visualization tools * according to different ways.

When you need to use it, you can load data directly from the data warehouse, saving a lot of time to find data and achieve a certain degree of automation. Operators only need * simple drag and drop * to make a variety of complex charts.

Data visualization is made to solve problems, so in the process of actual production and analysis, we must closely follow the enterprise business process, understand business indicators and what professional direction they belong to, and maximize the accuracy of data analysis. improve the quality of chart display information.

After receiving the data visualization requirements, we must first know who the audience is after the completion of the chart, make a preliminary planning plan for the project, and find out the problems to be solved, the information we want to see and the key points.

If you can, it is best to dock with the demand object again to make sure that there is no problem with the planning. Attention must be paid here. If the data direction of the planning is not what the other party wants, the efforts at that time are just a waste of your time and energy. it may even be asked to start all over again.

Data visualization, never forget the data. No matter how good the pre-planning is, no matter how close the relationship between business indicators and requirements is, you can't analyze anything without data.

The next step is to check the confirmed data with the previously planned indicators, associate the different data, think about the key information that may be used in data analysis, and put the sorted data into an alternate form.

Data visualization analysts must understand all the mainstream chart types, know what kind of analysis each chart is suitable for, and which type of information can be displayed. For example, line charts, bar charts, etc., can easily show the trend of things. But if you show the trend of sales volume over a certain period of time on the pie chart, then this chart does not make any sense.

In the process of data analysis, many novices will have a misunderstanding, often fill a variety of visual charts with several screens, thinking that all the information can be intuitively displayed to the user. In fact, users do not need that much content, and they tend to prefer content design that is clear at a glance and see key information at a glance rather than complex information display.

In addition, in the whole visual chart page, the color should not be too rich, and the color had better not be too bright, put the color with strong color contrast on the key information, present the change with clear logic, and highlight the key parts. so that users have a better experience, this is what they most want to see.

Finally, back to the data analysis itself, as a necessary means of digital transformation, analysts can choose to attach their own information thinking from the business logic to the finished visual chart to help users better distinguish the meaning of the chart.