The processing of data includes data collection, data analysis, and data visualization. Collection and storage are the basis of data processing, and all kinds of raw data collected within the enterprise must go through these processes to serve the internal decision-making of the enterprise. In the analysis and visualization stage, various information is processed and organized, which is used to guide decision-making and create greater value for the enterprise.
Data collection refers to the collection and statistics of various information, including surveys, observations, statistical analysis, and sampling. In the information age, the scope of data collection has been greatly expanded, in addition to the traditional fields of finance, accounting, sales, human resources, etc., including marketing, customer management and other fields. In the process of information collection, attention should be paid to the rational use of various channels and methods. For example, in the marketing department, it is necessary to be good at using various advertisements to publicize; In the human resources department, we should be good at using all kinds of information provided by employees to grasp the working status and ideological dynamics of employees in a timely manner; In the sales department, it is necessary to be good at using customer information to track the sales of customers, etc. During the collection process, attention should also be paid to the accuracy, reliability and completeness of the data to ensure the accuracy and reliability of the information.
Data storage refers to the extraction of data from a database and storage in a database for use by other systems. Data storage is an important part of data processing, which allows us to bring together all kinds of data in the enterprise for future use. There are two main types of data storage: database storage and file storage. Among them, database storage is the most important way, and file storage is the other main way to store data.
In the era of big data, data analysis can not only discover the hidden patterns behind the data, but also quantify those patterns.
For enterprises, the most important thing in data analysis is to integrate the large amount of data generated by various departments or business lines within the enterprise to find valuable information from it. Although the data analysis methods and tools required by different industries vary, they can be broadly divided into two broad categories:
One is structured data, which is stored in a database and needs to be analyzed and extracted by certain technical means; The other type is unstructured data, which needs to be processed and extracted by certain technical means.
As can be seen from the above two types, there are two main types of data that enterprises need to process: structured and unstructured.
Data visualization is the process of processing and presenting complex data in the form of intuitive charts, graphs, and images, with the goal of enabling managers and analysts to quickly understand and gain insight into the patterns and trends behind the data. In the practice of data visualization, it not only involves the screening, transformation and display of data, but also needs to design an easy-to-understand, informative and visually beautiful visual interface based on the actual needs and usage scenarios of users.
The above is about the processing of data, and in the future, as the operational needs of enterprises continue to change, we will put forward more related requirements on the basis of the above content.
Data processing refers to the transformation of raw data into a form that is convenient for computer processing through certain calculations, statistics and other methods. Data processing mainly includes two stages: data collation and data cleaning. In the data sorting stage, it is necessary to clean the original data, eliminate outliers and duplicate values, and extract useful information, so that the raw data can be used for the next calculation and analysis.
In today's era of informatization and digitalization, the ability of enterprise data processing and utilization has become a key indicator to measure its competitiveness. The data system built based on the graph database not only realizes the efficient collection of various data sources, but also carries out deep processing and intelligent storage of data through the careful design and implementation of the data layer and the exchange layer. This process encompasses data cleaning, integration, transformation, and standardization, ensuring data accuracy and consistency, and laying a solid foundation for subsequent analysis and retrieval.