Shulou(Shulou.com)06/03 Report--

Why should data governance be carried out?

First of all, data is valuable. According to Accenture's "average GDP growth rate of various industries before 2035", the manufacturing industry has only 2.1% in terms of natural growth, but after the data and the resulting artificial intelligence bonus, this figure has risen to the second place of 4.4%, and the value of the data is considerable.

However, the application environment of data is risky. Facebook's privacy leak directly reduced Facebook's market value by $64 billion, and Zuckerberg was questioned by Congress.

In addition, the application environment of data is inefficient. Why is the application environment of data inefficient? First, the data is unknowable, users do not know what data they have, nor do they know what the data has to do with the business, although they are aware of the importance of big data, but there is no key data that can solve the problems facing their business or do not know how to find it. Second, the data is not available, the data needs a long development process, resulting in the needs of business analysis, difficult to be quickly met. Third, the data is uncontrollable, there is no unified data standard makes it difficult to integrate and unify the data, there is no quality control to make use of massive data, there is no effective management of the entire big data platform management process.

From the analysis of the above three points, it is concluded that one of the goals of our data governance is to be compliant and efficient to generate data value. Establish a harmonious and complementary relationship among data owners, users, data and supporting systems, coordinate and lead data management at all levels from an organization-wide perspective, and ensure that all kinds of internal personnel can get timely and accurate data support and services.

(2) how to generate data value in compliance and efficiently?

We believe that to be compliant and efficient to generate data value must be not only a technical thing, but also need to build a full life cycle, full-depth, omni-directional governance system, including data governance organization system, data governance tools, data governance control process.

Through the data governance organization to establish management methods, develop workflow, determine roles and responsibilities. Data governance tools mainly include data standard management, metadata management, data quality management, data asset management, data security management, and the coordinated operation of each module to ensure that the data of the data platform is consistent, safe and effective. The process of data governance management and control runs through the whole process of data governance system, realizing the idea of platform data management.

3 organizational system of data governance

The purpose of the construction of the data governance organization is to establish the organizational structure of data governance, clarify the roles and responsibilities at all levels, ensure the implementation of various management methods and workflows of data governance, and promote the orderly development of data governance.

The entire organizational structure of data governance can be divided into three layers:

1. Data governance committee: the decision maker of data management. Take the lead in data governance, formulate policies, standards, rules and procedures for data governance, and coordinate responsibility conflicts.

two。 Data governance center: the operator of the data platform. Responsible for submitting the requirements of data standards and data quality rules and business specifications, supervising the landing of various data rules and specification constraints, and being responsible for the formulation of the overall data control process in data governance.

3. Business units: data providers, data maintainers, data consumers. Responsible for specific implementation matters.

4 data governance management and control process

The purpose of data governance management and control process is to enable the solution to be landed in an orderly manner. Take the development of data standards as an example:

The data standards management coordinator organizes data providers and executors to participate in the collection and collation of data standard attributes, and negotiates the first draft of data standards according to the actual situation of the enterprise.

After many discussions and enrichment of the first draft of the data standard, the audit draft of the data standard is submitted to the data standard management decision maker.

After the discussion and review of the data standards management decision-makers, the data standards management coordinator revised and improved the data standards again, and completed the release of the data standards.

5 data governance management and control tools

If you want to do good work, you must first sharpen its tools. The purpose of data governance control tools is to help enterprises better implement the specification. It is generally believed that data governance should at least cover the following functional areas: data asset management, data standards management, metadata management, data quality management, data operation and maintenance management and data life cycle management.

Data standards: under the promotion and guidance of the data governance organization structure, the whole process of data standardization can be implemented with the help of standardization management and control process, following the data standards established by consensus.

Metadata: the centralized management mode is adopted to manage the metadata, and the enterprise metadata is logically centralized, that is, the metadata management module, as the unified publishing source of the company's metadata, centrally manages the metadata and provides the functions of centralized creation, maintenance and query of the metadata.

Data quality: carry out a series of management activities such as identification, measurement, monitoring, early warning and other management activities that may cause all kinds of data quality problems that may be caused by each stage of the data life cycle, such as planning, acquisition, storage, sharing, maintenance, application, demise, and so on. and the data quality can be further improved by improving the management level of the organization.

Data asset: a set of business functions for planning, controlling, providing data and data assets, including the development, implementation, and supervision of plans, policies, programmes, projects, processes, methods, and procedures related to data, thereby controlling, protecting, and increasing the value of data assets.

Data security: provide effective authentication, authorization, access and audit of enterprise data through planning, formulation and implementation of data security policies and security policy measures.

Data operation and maintenance: including data assets operation and maintenance, data quality operation and maintenance, with the help of operation and maintenance tools to improve the overall efficiency of enterprise data operation and maintenance.

6 conclusion

Today, when the value of data assets is highly recognized and developed, data governance not only needs to be implemented in the enterprise as a management function, but also should become a kind of corporate culture. Data managers at all levels of the enterprise must constantly communicate, educate and promote the importance of the value of data assets and the business contribution of data governance functions. To enhance the awareness of data users of data governance and the recognition of the benefits of data governance is to continuously improve the enterprise data management mechanism, fully tap the value of enterprise data, and enhance the core competitiveness of enterprises.

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