Abstract

Data governance is not a simple operational behavior, but a set of activities that exercise power and control over data asset management. It is a method of managing and protecting data, and a key to ensuring accuracy, integrity, security, reliability, and consistency.

Through data governance, enterprises can better master existing data, understand the value and usage of data, sort out the business logic of system applications, and better manage and use data. At the same time, it can also avoid the leakage of sensitive data.

This article will introduce all aspects of data governance in detail, including data source management, metadata management, data quality management, data classification and grading, as well as data assets and services, and optimize them based on actual application scenarios.

First, Data Source Management

Data source management is the foundation of data governance, involving the identification, integration, management, and maintenance of internal and external data sources of the enterprise. Effective data source management can ensure the accuracy, integrity, reliability, security, and availability of data, providing a reliable foundation for subsequent data analysis and decision-making.

Second, Metadata Management

Metadata management is an important link in data governance, involving the collection, maintenance, and analysis of metadata information of data. Through metadata management, data can be better understood and managed, thereby better supporting corporate decision-making and business needs.

2.1 Metadata Collection

Metadata is data that describes data, including business metadata and technical metadata.

Business metadata can be collected and managed manually. Business metadata information is recorded through documents, tables, and other forms, and then uploaded to the metadata management platform.

Technical metadata can be scanned and extracted automatically from data sources such as databases, files, APIs, etc., using automated tools, including data definitions, structures, relationships, and sources. When collecting, data sources can be configured directly, or probe scanning can be used to discover hidden data in enterprises, then collect metadata to better understand the internal data situation of the enterprise.

2.1.2 Metadata Maintenance

Metadata maintenance refers to the maintenance and update of metadata information, with a key link being the matching of business metadata and technical metadata. By matching business metadata with technical metadata, it is better to understand and use data. The matching method can be manual addition, or it can be automated using algorithms and rules. For example, similarity algorithms can be used to compare the names, comments, and attributes of metadata items to determine their similarity and matching degree.

2.2 Data Analysis

2.2.1 Data Lineage Analysis

Data lineage is divided into application level, table level, and field level, which is used to track the source and flow of data. Application-level data lineage mainly checks the call and flow of data between applications, while table-level and field-level data lineage mainly checks the processing and processing process of data.

From the perspective of data processing, data lineage analysis can help data developers understand the processing process and corresponding stages of data, identify the sources and destinations of data, clarify the classification of each node, view the changes and frequency of changes in input and output node data, and discover data quality issues and potential risks. Most data lineage analysis is presented from this perspective, showing the data map.

From the perspective of data flow, data lineage analysis can help business personnel understand which databases the system accessed, view which calls the system made to the database, the business meaning of these calls, the data situation of each business call, and the complete execution sequence of the calls.

III. Data Quality Management

The data quality management module can analyze the quality of data in the database. The indicators include integrity, validity, timeliness, consistency, accuracy, uniqueness, and many other dimensions. By formulating data quality analysis rules, the quality of data is evaluated, and finally, a data quality report is generated. Data quality management is a circular management process, first determining data quality indicators, which can be determined according to business needs and data characteristics; then specifying data, which can specify rules to match target detection data; then analyzing the target data and outputting a data quality report; based on the quality report, cleaning and processing data with targeted measures, and then re-determining data quality indicators for the next round of data quality detection. The aim is to continuously improve the reliability of data in a circular management manner, with the ultimate goal of enhancing the value of data in use, winning economic benefits for enterprises, and avoiding deviations in enterprise decision-making caused by poor data.

IV. Data Classification and Grading

Data classification and grading is the process of scoring the sensitivity of data, which can be realized through the following steps to classify and grade data:

Firstly, it is necessary to determine the data classification criteria. Enterprises should extract data types according to the business needs and data characteristics of the system, and for different business modules, determine the sensitivity level of data for the entire industry.

Next, it is necessary to formulate data templates, specify the type and sensitivity level of data for the data templates, and also formulate data template data filtering rules.

Finally, set up data scanning tasks, scanning data from data sources according to the established data templates, and matching the target data.

Data classification and grading is a key link in data governance, aimed at protecting sensitive data from being misused or leaked, helping enterprises comply with relevant laws and regulations and industry standards, and improving data management efficiency.

V. Data Assets and Services

Data assetization is the process of collecting, cleaning, processing, and integrating internal data resources of enterprises into valuable information that can be used for decision support, business expansion, and innovation.

In the data asset module, a data catalog can be established, which can be a technical metadata catalog or a business metadata catalog, facilitating users in quickly locating data assets. It is also necessary to establish data sharing and exchange mechanisms, i.e., data services, which can provide data assets to internal or external users through API services. At the same time, it is also necessary to establish authority management and service application and approval mechanisms to protect data assets from being stolen or leaked.

Summary

Data governance is a method of managing and protecting data, covering a variety of modules from data source management to data assets and services. It involves cross-departmental collaboration, strategy formulation, process optimization, and technical implementation. Enterprises can enrich and optimize it according to their actual application scenarios.