With the rapid development of the Internet and the advent of the era of big data, data classification and classification have become an important link in data management. The purpose of data classification and classification is to make data more orderly, easy to manage and use. This article will introduce the concepts, processes and methods of data classification and grading.
1. The concept of data classification and classification
Data classification and classification refers to the classification and classification of data according to certain standards and rules, so as to facilitate the management and application of data. By classifying and grading data, operations such as organization, archiving, retrieval and sharing of data can be realized, and the utilization value and security of data can be improved.
2. The process of data classification and classification
1. Determine the classification criteria:
First of all, it is necessary to determine the standards and basis for data classification. The criteria for data classification can be determined based on factors such as data attributes, content, usage, and security level. For example, it can be classified according to the type of data (text, image, audio, etc.), content (personnel information, financial information, etc.) and security level (public, internal, confidential, etc.).
2. Develop classification rules:
According to the determined classification standards, formulate corresponding classification rules. Classification rules can be a set of specifications that conform to data attributes and characteristics, and are used to guide the specific operation of data classification and grading. The classification rules should consider the actual situation and needs of the data to ensure the accuracy and practicability of the classification results.
3. Data classification:
Classify the data according to the classification rules. It can be done in two ways, manual classification and automatic classification. Manual classification refers to manual classification of data according to the classification rules, which is suitable for situations with small data volumes; automatic classification refers to the use of computers and related algorithms to automatically classify data, which is suitable for large data volumes. In the process of data classification, the data needs to be marked and recorded for subsequent management and application.
4. Data classification:
On the basis of data classification, classify data. Data classification refers to dividing data into different levels according to the security level and sensitivity of the data. Data can be divided into different levels such as public level, internal level, and confidential level according to actual needs and security requirements. Data classification needs to consider factors such as data confidentiality, integrity, and availability to ensure data security and compliance.
5. Data management:
Manage the classified and graded data. Data management includes operations such as data storage, backup, archiving, retrieval and sharing. Data storage requires selection of appropriate storage media and technologies to ensure data security and reliability. Data backup and archiving are to prevent data loss and improve data recoverability. Data retrieval and sharing is to facilitate the use and sharing of data and improve the value of data utilization.
6. Data update and maintenance:
Data classification and grading is a dynamic process that requires continuous updating and maintenance of data. As data increases and changes, the classified and graded data needs to be adjusted and updated. At the same time, it is also necessary to regularly check and maintain the data to ensure the accuracy and integrity of the data.
3. The method of data classification and classification
1. Attribute taxonomy:
Classify according to the attributes and characteristics of the data. For example, data can be classified according to its type (text, image, audio, etc.) and format (structured, semi-structured, unstructured, etc.).
2. Content taxonomy:
Classify according to the content and domain of the data. For example, data can be classified according to the subject (personnel information, financial information, etc.) and field (medical, financial, educational, etc.).
3. Use classification method:
Classify according to the purpose and function of the data. For example, data can be classified according to its application scenarios (business decision-making, scientific research, etc.) and functions (analysis, mining, recommendation, etc.).
4. Security Taxonomy:
Classify according to the security level and sensitivity of the data. For example, it can be classified according to factors such as data confidentiality, integrity and availability.
Data classification and grading is an important link in data management, and it is of great significance to improve the efficiency and quality of data management and application. Through reasonable classification and grading processes and methods, orderly management and effective use of data can be realized, and better data support and services can be provided for enterprises and individuals. I hope this article will be helpful for readers to understand and master data classification and grading.