Hello everyone, I am Wang Laoshi. This year, the Ministry of Industry and Information Technology will carry out data security management certification. The company is "lucky" to carry out pilot implementation. In order to ensure data security compliance and meet the data management level certification, a lot of work has been done this year. action. Then I will share with you some experience in data governance.

Data Governance Background

policy background

domestic

• In November 2019, the Fourth Plenary Session of the 19th CPC Central Committee made it clear for the first time that data should be used as a factor of production to participate in society distribute.
• In March 2020, the "Opinions of the Central Committee of the Communist Party of China and the State Council on Building a More Perfect Factor Market Allocation System and Mechanism" pointed out that it is necessary to accelerate the cultivation of the data factor market, promote the opening and sharing of government data, enhance the value of social data resources, and strengthen data resources Integration and security protection.
• The "Opinions of the Central Committee of the Communist Party of China and the State Council on Accelerating the Improvement of the Socialist Market Economic System in the New Era" issued in May 2020 stated that it is necessary to accelerate the cultivation and development of the data element market, establish a data resource list management mechanism, improve the definition of data ownership, open and share Standards and measures such as , transaction circulation, etc., to give full play to the value of social data resources. Promote the construction of digital government, strengthen the orderly sharing of data, and protect personal information in accordance with the law

abroad

In December 2019, the United States released the "Federal Data Strategy and Governance" In
February 2020, the European Union released the "European Data Strategy" In
September 2020, the United Kingdom released the "National Data Strategy"

Summarize

Many countries have upgraded their data strategy to a national strategy
Data governance is an important guarantee to promote the realization of data value
Open data sharing is the basic condition for releasing data value
Data quality management is the key link to release the value of data

technical background

DIKW pyramid model

DIKW Pyramid Model: Data is the foundation, and data quality is the root of data value

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technical challenge

• Information silos: isolated island phenomenon is prominent, business systems "fight against each other" and data is scattered, and the data is already business-oriented
• Standards are not uniform: no unified data standards have been formulated, and there is a large gap between the business standards and technical definitions of each system
• Data quality is low : Good and bad data in various systems, low quality and inconsistent data format
• High cost of data management: It is difficult to efficiently organize and manage data
• Difficulty in data sharing: There is no global data sharing mechanism for data to achieve data interconnection
• Data security: Data security Growing problem
• Multimodal: Data is unavailable (unstructured data), unusable (low quality), not usable (not effectively integrated) problematic

The Goals and Significance of Data Governance

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Data Architecture: Reasonable
Data Quality: Improvement
Data Standards: Authoritative
Data directory: clear
Data flow: sharing
Data Value: Magnify
Data Security: Controllable
Data assets: clear family background, clear bottom number

Goal: Accessible, manageable, well-governed, visible, controllable, and coexistent to
enhance the use value of data

Implementation method introduction

Introduction to PAI Implementation Methodology

P: Process-oriented process
provides workflow and templates
to disassemble the data governance work into six stages: demand research, general design, detailed design, data development, deployment operation and maintenance, and training, and specifies the input and output content of each stage and Template
A: Automation provides
products and tools
Products (enabling) include: asset management platform, tag management platform, resource service platform, BI
tools (efficiency improvement) include: batch implementation of tools for data access, development, scheduling, etc.
I:Intelligence provides
unstructured data processing and analysis capabilities, and structured data governance efficiency and quality enhancement capabilities
Unstructured: text, video, voice and other processing capabilities, map construction capabilities
Structured: data standardization, modeling, Data Fusion Processing

Four major capacity building

Gathering: data aggregation capabilities, in the face of various data sources, diverse data types, and different data timeliness requirements, data governance can first connect all kinds of data to the platform, and "incoming" is the first step.

Governance: Data governance capabilities in a narrow sense, including data standards, data quality, metadata, data security, data life cycle, and master data. The core is to ensure the unification of data standards, use metadata to grasp the distribution of data assets, impact analysis and blood relationship, continuous improvement of data quality, safety and reliability of data assets, elimination and destruction mechanism of data assets, and unification and use of core master data.

Communication: data pull-through integration capabilities, the original business data is scattered in various business systems, and data organization is based on the premise of satisfying business flow. Subsequent data requirements are carried out based on actual business objects rather than each business system, so data needs to be reorganized according to business entities. For example, a government unit’s comprehensive analysis of people usually involves: property, education level, five social insurances and one housing fund, tax payment, family members, etc., and ID numbers need to be used to connect the housing management bureau, transportation bureau, education bureau, human resources and social security bureau, taxation department, etc. Bureau, Health and Health Commission and other commissions and bureaus data. Data integration capabilities are the basis for follow-up analysis to meet diverse needs, the foundation for the accumulation of data assets, and another focus of platform construction.

Use: data service capabilities, data assets can only be effective if they are truly empowered by the front-end business, so how to enable business departments to quickly find and conveniently use the required data assets is another core capability of the data governance platform.

P: plan, standards, planning, and process formulation; D: do, assisting the implementation of product tools; C: check, double-check assurance of business technology; A: action, continuous optimization and improvement of data quality and services.