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What is the main purpose of data visualization? I believe that many inexperienced people are at a loss about this, so this article summarizes the causes and solutions of the problem. Through this article, I hope you can solve this problem.

The main purpose of data visualization is to gain insight into the phenomena and laws contained in the data, which has multiple meanings: discovery, decision-making, interpretation, analysis, exploration and learning. Concise meaning is to enhance the efficiency of people to complete certain tasks through visual expression.

The main purpose of data visualization is to convey and communicate information clearly and effectively by means of graphics. However, this does not mean that data visualization must be boring in order to achieve its functional use, or extremely complex in order to look colorful. In order to effectively convey ideas and concepts, aesthetic forms and functions need to go hand in hand, through intuitive communication of key aspects and characteristics, so as to achieve in-depth insight into rather sparse and complex data sets. However, designers often do not have a good grasp of the balance between design and function, so as to create a flashy form of data visualization, unable to achieve its main purpose, that is, to convey and communicate information.

Data visualization is closely related to information graphics, information visualization, scientific visualization and statistical graphics. At present, data visualization is a very active and critical aspect in the field of research, teaching and development. the term "data visualization" unifies the mature field of scientific visualization with the younger field of information visualization.

Correlation analysis

data acquisition

Data acquisition (sometimes abbreviated as DAQ or DAS), also known as "data acquisition" or "data collection", refers to the process of sampling the real world to produce data that can be processed by a computer. Usually, the process of data acquisition includes the steps of collecting signals and waveforms and processing them in order to obtain the required information. The components of the data acquisition system include sensors used to convert the measurement parameters into electrical signals, which are obtained by the data acquisition hardware.

Data analysis

Data analysis refers to the process of detailed study and summary of data in order to extract useful information and form conclusions. Data analysis is closely related to data mining, but data mining tends to focus on larger data sets, less on reasoning, and often uses data originally collected for a different purpose. In the field of statistics, some people divide data analysis into descriptive statistical analysis, exploratory data analysis and confirmatory data analysis; among them, exploratory data analysis focuses on finding new features in the data, while confirmatory data analysis focuses on the confirmation or falsification of existing hypotheses.

The types of data analysis include:

1) exploratory data analysis: it refers to a method of analyzing data in order to form a test worthy of hypothesis, and it is a supplement to the traditional statistical hypothesis testing methods. The method is named by John Tucky, a famous American statistician.

2) qualitative data analysis: also known as "qualitative data analysis", "qualitative research" or "qualitative research data analysis", refers to the analysis of non-numerical data (or data) such as words, photos, observations and so on.

After 2010, data visualization tools are basically based on tables, graphics (chart), maps and other visual elements, data can be filtered, drilled, data linkage, jump, highlight and other analysis means to do dynamic analysis.

Visualization tools can provide a variety of data presentation forms, a variety of graphic rendering forms, rich human-computer interaction, dynamic script engines that support business logic, and so on.

Different from ordinary Dashboard or Reporting products, the BI front end of Yonghong Technology is discovery-oriented: rich in interactive means and powerful in analysis. Users can further interact with data (Interactive), filter (Filter), drill (Drill), Brush (Brush), Associate (Associate), transform (Transform) and other technologies, so that users can: grasp information, find questions, find answers, and take action.

Data governance

Data governance covers the people, processes, and technologies required to create a consistent enterprise-level view (enterprise view) of data for specific organizations, and data governance is designed to:

1) enhance consistency and confidence in the decision-making process

2) reduce the risk of being subject to regulatory fines

3) improve the security of data

4) maximize the revenue-generating potential of data

5) assign responsibility for information quality

Data management

Data management, also known as "data resource management", includes all subject areas related to managing data as a valuable resource. For data management, the formal definition put forward by DAMA is: "data resource management refers to the formulation and implementation process of architecture, policies, norms and operating procedures used to correctly manage the entire data life cycle requirements of an enterprise or organization." This definition is quite broad and covers many occupations that may not have direct technical contact with low-level data management, such as relational database management.

data mining

Data mining refers to the process of classifying a large amount of data and selecting relevant information. Data mining is commonly used by business intelligence organizations and financial analysts; however, in the field of science, data mining is also increasingly used to extract information from the huge data sets generated by modern experimental and observation methods.

Data mining is described as "the extraordinary process of extracting implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases". Data mining related to enterprise resource planning refers to the process of statistical analysis and logical analysis of large transaction data sets to find patterns that may be helpful to decision-making.

E-commerce data

E-commerce data visualization, one of the best ways to obtain information is to quickly grasp the main points of information through visualization. In addition, e-commerce data present data visually and reveal amazing patterns and observations, patterns and conclusions that cannot be seen through simple statistics. "through visualization, we turn information into a landscape that can be explored with our eyes, a kind of information map. When you are lost in the information, the information map is very useful." This is especially true in the e-commerce industry.

After reading the above, have you mastered what is the main purpose of data visualization? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!

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