What is data governance and why should it be standardized?

Data governance is to standardize and share all data.

Compared with the data sharing platform of the traditional campus, the data governance and sharing platform of Sanmeng Technology adds standardized sharing support for source business data on the basis of supporting the data sharing of the central database, solves the problem of incomplete coverage of the standardization and sharing of traditional solutions, and truly realizes the standardization and sharing of full data. Sanmeng Technology uses advanced data item standard management technology to disassemble the data item level of the Ministry of Education's industry standard, and reconstruct the school-based data item standard according to the school's existing data situation, which can standardize the mapping of each source system data and register it in the information resource directory to realize the standardization and sharing of full data.

In recent years, data governance and exchange have become very popular, because the sharing of digital campuses in 2015 is actually only to meet the basic business sharing and portal display, and the construction of intelligent campuses and big data applications with higher data requirements and a wider range of sharing cannot be supported. Data normalization is a pre-processing step that allows data to be normalized to a specific range to ensure better convergence in backpropagation. Sanmeng Technology data governance and exchange platform first carries out data standardization (mechanized governance machines can be used), and then relies on information resource catalogs for sharing, which can realize full data standardization and sharing, intelligent data standard matching, one-stop data management, strict data privacy management, and diverse data value presentation.

Only by doing a good job in data governance, making data more accurate and complete, and safe and compliant, can we unleash the unlimited potential of data, tap more valuable data applications, and create value for university management.

At present, many companies propose that they do data governance based on data items, but only put forward the concept of data item governance, although the data item information query platform has been established, but the data item system functions such as the mapping of data items and real data, the linkage of resource modification, and the binding of detection rules have not been established, and the standards cannot be implemented in the actual data. Sanmeng Technology's AIOD data governance and exchange platform adopts advanced distributed technology and artificial intelligence technology, and integrates a mature data item standard governance system to establish a full data center for the school. At the same time, it helps schools establish inter-departmental information resource catalogs, break through data silos, and provide normalized data quality monitoring services and typical applications of campus data.

At present, there is no unified standard definition of its concept in the industry, we can think that data governance is essentially the process of evaluating, guiding and supervising the data of an organization (enterprise or department) from collection and integration to analysis management and utilization (EDM), and creating value for enterprises by providing innovative data services.

The Data Governance Institute (DGI) believes that enterprises not only need a system for managing data, but also a complete system of rules and regulations. Data governance basically covers all data-related content in the enterprise, so the entire enterprise, including workflows, people involved, and technology used, needs to be carefully considered to ensure data availability, consistency, integrity, compliance, and security, and ensure high data quality throughout the data lifecycle.

Data governance includes the following aspects:

1. Centralized data storage and management: In order to reduce the difficulty, cost and complexity of data governance, the system of centralized data management is established to reduce data replication and decentralized storage, and improve the concentration and integration of data.

2. Data storage has a reasonable term and method: Data storage has a clear life cycle management, and can adopt targeted and differentiated storage strategies at different stages of the data storage life cycle according to the importance of data and the access of data users.

3. Unified processing and integration of data: In order to meet the data standards and quality requirements set by the data governance organization, data needs to be processed and integrated using unified tools and rules.

4. Data is easily accessible: Data should be very convenient for data users to obtain and use, but in the case of meeting data standards, data quality and information security requirements of data governance.

5. Data access is fully controlled: Because of the importance and replicability of data assets, it is inevitable that the access, acquisition and storage of data need to be safely controlled to avoid the leakage of core assets of the enterprise and cause irreparable losses.

Introduction:

Data governance is a concept that emerged in the 90s of the 20th century, and the main goal of data governance was to clean up customer data, improve data annotation, and ensure the integrity of organizational data. With the continuous expansion of enterprise scale, the relevant theories of data technology management continue to mature and improve, and the importance of enterprise data governance has reached a broad consensus in the industry, that is, data is not only valuable but also an asset with competitive value for enterprises.

In order to make data consistent, accurate and timely to users of digital construction, so that data can be more easily understood by users, and maximize the value of data assets, enterprises need to manage the existing data.

Data governance analytics is:Filter and consolidate huge amounts of data, so that users can follow up the real-time situation of data, so that users can more accurately and quickly conduct reasonable analysis and judgment of data businessEnable data-driven businessto achieve the purpose of enterprise value-added. The data analysis platform system developed based on big data technology allows users to observe and analyze the data of different businesses more vividly by processing and filtering from multiple business systems to the Zhaozi Xianjian database, ODS intermediate library to the data warehouse model, and then binding the model components to realize the visual display of data.

Since it's data governance analytics, it's in"Classification and analysis".Before that, you need to do the data"Governance".。We usually need to put the firstData standardizationData standardization is the indexation of statistical data, and the data standardization processing mainly includes:Data cochemization processingwithDimensionless processingTwo aspects. With the continuous expansion of people's research fields, the evaluation objects faced by them are becoming more and more complex, and it is often unreasonable to evaluate things according to a single indicator according to the book, so multi-index information processing data is needed to obtain more accurate and useful business data that users want.

Data governance begins with identifying several key aspects of data:

Data definition and classification: Define and classify data by defining and taxonomizing different types of data, such as structured, semi-structured, and unstructured data, to better manage and leverage data.

2.Data ownership and responsibility: Clarify the rights and responsibilities of the data office, and determine which departments or individuals are responsible for collecting, maintaining, and managing specific types of data.

This helps ensure the legitimacy, integrity, and reliability of the data, and avoids confusion and conflicts in data management.

3.Data lifecycle management: Define the life cycle of data, from the creation, collection, storage, processing, sharing, and final destruction or archiving of data. This helps ensure data security, compliance, and validity, and properly plan data storage and backup strategies.

4.Data quality standards: Clarify data quality standards and indicators, including accuracy, completeness, consistency, and timeliness. This helps to assess and improve data quality, increasing the accuracy and reliability of data analysis and decision-making.

5.Data access and permission control: Clarify the access and control mechanisms for data to ensure that only authorized personnel can access and use specific data, and protect sensitive data.

6.Data privacy and compliance: Clarify data privacy protection and compliance requirements, including the compliant handling of personally identifiable information (PII) and sensitive data, and user privacy protection. This helps to comply with relevant laws and regulations, maintain customer trust and corporate reputation.

Identifying these key areas can help organizations better govern their data and ensure the quality, security, compliance, and validity of their data, thereby increasing the value of their data and their ability to support business decisions.

Data governance should first clarify the significance, historical value and quality of data.

1.The significance of data: clarify the useful information provided by data for enterprise decision-making and operation, including the quality, type, accuracy, and reliability of data, so as to deeply understand the value and use of data.

2.The value of data: Define the economic, strategic, and societal value of data, including its capabilities, innovation, and competitive advantage, in order to protect and enhance its value.

3.Data quality: Clarify the requirements and standards for data quality, including the accuracy, completeness, consistency, and credibility of the data, so as to fully grasp the quality of the data and optimize the data quality.

By clarifying the meaning, value, and quality of data, enterprises can clearly understand the importance, value, and quality of data, improve data governance systems and processes, improve the efficiency and quality of data management, and ensure the reliability and effectiveness of enterprise business decisions and operations.

There are two main types of data standardization processing: index consistency processing and dimensionless processing. 1. The consistent processing of indicators and the consistent processing of indicators mainly solves the problem of different properties of data imitation. For example, when we evaluate the role of a number of different indicators, the larger the value of a certain type of index, the better, we call it a positive index, such as the diagnostic coincidence rate, the average turnover of hospital beds, etc.; There is another category of indicators, the lower the value, the better, which we call inverse indicators, such as average length of hospital stay, perinatal infant mortality rate, etc.

In this case, if the combined effect of these two types of indicators with the same price, due to their different directions of action, the direct addition of indicators of different natures cannot correctly reflect the comprehensive results produced by different directions, at this time we need to carry out the consistency of the inverse index, change the nature and direction of the inverse index, and make the direction of action of all indicators consistent, so as to obtain appropriate results.