What is the main purpose of data visualization?

The main purpose of data visualization is to gain insight into the phenomena and patterns contained in the data, which has multiple meanings: discovery, decision-making, explanation, analysis, exploration and learning. The concise meaning is to enhance people's completion through visual expression efficiency of certain tasks.

#Data visualization mainly aims to convey and communicate information clearly and effectively with the help of graphical means. However, this does not mean that data visualization must be boring to achieve its functional purpose, or extremely complex to look colorful. To effectively communicate ideas, aesthetic form and function need to go hand in hand, by visually conveying key aspects and features, thereby enabling deep insights into rather sparse and complex data sets. However, designers often fail to strike a good balance between design and functionality, creating flashy data visualizations that fail to achieve their primary purpose, which is to convey and communicate information.

Data visualization is closely related to information graphics, information visualization, scientific visualization and statistical graphics. Currently, data visualization is an extremely active and critical aspect in the fields of research, teaching, and development. The term "data visualization" unifies the mature field of scientific visualization with the younger field of information visualization.

Related 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 in order to produce data that can be processed by computers. Typically, the data acquisition process includes the steps of acquiring signals and waveforms and processing them in order to obtain the required information. The components of the data acquisition system include sensors that convert measurement parameters into electrical signals, and these electrical signals are acquired by the data acquisition hardware.

Data Analysis

Data analysis refers to the process of studying and summarizing data in detail 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, focuses less on inference, and often uses data that was 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 discovering new features in the data, while confirmatory data analysis Focus on the confirmation or falsification of existing hypotheses.

Types of data analysis include:

1) Exploratory data analysis: refers to a method of analyzing data in order to form a worthy test of hypotheses. It is a test of traditional statistical hypotheses. Supplementary means. The method was named by the famous American statistician John Tukey.

2) Qualitative data analysis: Also known as "qualitative data analysis", "qualitative research" or "qualitative research data analysis", it refers to the analysis of non-numeric data such as words, photos, and observations. Analysis of data (or information).

After 2010, data visualization tools are basically based on visual elements such as tables, graphics (charts), maps, etc. The data can be filtered, drilled, data linkage, jump, highlight and other analysis methods for dynamic analysis. .

Visualization tools can provide various data presentation forms, various graphic rendering forms, rich human-computer interaction methods, dynamic script engines that support business logic, etc.

Different from general Dashboard or Reporting products, Yonghong Technology’s BI front-end is discovery-oriented: rich in interactive methods and powerful in analysis functions. Users can further interact with data (Interactive), filter, drill, brush, associate, transform and other technologies, allowing users to: master information, discover problems, Find the answers and take action.

Data Governance

Data governance encompasses the people, processes, and technologies required to create a consistent, enterprise-level view of data for a specific organization. Data governance is designed to:

1) Enhance consistency and confidence in decision-making

2) Reduce the risk of regulatory fines

3) Improve data security

4 ) Maximize the revenue-generating potential of data

5) Designate information quality responsibilities

Data Management

Data management, also known as "data resource management", includes all aspects related to Subject areas related to managing data as a valuable resource. For data management, the formal definition proposed by DAMA is: "Data resource management refers to the development and execution process of the architecture, policies, specifications and operating procedures used to correctly manage the entire data life cycle needs of an enterprise or institution." This definition is quite broad and covers many occupations that may not have direct technical exposure to low-level data management work (such as relational database management).

Data Mining

Data mining refers to the process of classifying and sorting out large amounts of data and selecting relevant information. Data mining is typically used by business intelligence organizations and financial analysts; however, it is also increasingly used in science to extract information from the vast data sets generated by modern experimental and observational 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 as it relates to enterprise resource planning refers to the process of statistical and logical analysis of large transactional data sets to look for patterns that may aid decision-making efforts.

E-commerce data

E-commerce data visualization, one of the best ways to obtain information is to quickly grasp key information through visualization. In addition, e-commerce data also reveals surprising patterns and observations through visual presentation of data, patterns and conclusions that cannot be easily seen through simple statistics. "Through visualization, we turn information into a landscape that can be explored with the eyes, a kind of information map. When you are lost in information, information maps are very practical." This is especially true in the e-commerce industry.

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