1 Why should data governance?
First, the data is valuable. According to Accenture released "in 2035 before the industry's average GDP growth rate", simply look natural growth, the manufacturing sector is only 2.1%, but by then the data derived therefrom as well as the addition of artificial intelligence, this figure was promoted to second were 4.4%, the value of data is considerable.
However, the application of environmental data is risky. Facebook privacy breaches, a direct result of Facebook market value has shrunk to $ 64 billion, Zuckerberg also subject to congressional inquiries.
In addition, the application of environmental data is inefficient. Why use environmental data is inefficient? One is data agnostic, users do not know what data they have, do not know what these data and business relationship, although aware of the importance of big data, but was not able to solve their own problems facing business critical data or do not know how Look for these data. Second, the data is not available, data requires a lengthy development process, resulting in demand for business analysis, it is difficult to be quickly satisfied. Third, the data is not controlled, there is no uniform data standards lead to a unified data integration is difficult, there is no quality control resulting in massive amounts of data is difficult to use, there is no effective management of large data management processes throughout the platform.
From the above analysis of three points, it reached a data governance is our goal: compliance, data efficiently generate value. Establish a harmonious complementarity between the data owner, user, and data support system, coordinated from the perspective of the whole organization, at all levels of command data management work to ensure that all categories of personnel inside can be timely and accurate data to support and service.
2 How to compliance, data efficiently generate value?
We believe that compliance to efficiently generate the data value is not just some technical things, but need to build the whole life cycle, full-depth, comprehensive management system, including data governance organization system, data management tools, data management control three aspects of the process.
By establishing a data governance organization and management approach, the development of work processes, determine roles and responsibilities. Data management tools include standard data management, metadata management, data quality management, data asset management, data security management, coordination of operations of each module, ensuring consistent data platform, safe and effective. Data governance process management and control processes throughout the entire data management system, the data management platform of ideas.
3 Data Governance organization system
Build a data governance organization aims to establish a data governance organization structure, clear roles and responsibilities at all levels, protect their data management approach to governance, implementation of workflow, data management work to promote the orderly conduct.
Organizational structure of the entire data management can be divided into three layers:
1. Data Governance Committee: decision-makers data management. Policy is responsible for leading data management work to develop data governance, standards, rules, procedures, coordination stewardship conflict.
2. Data center management: data platform operators. Responsible for submitting data standards and data quality rules and business rules, monitoring the situation of the data floor rules and norms constraints, and is responsible for regulating the flow of data in the overall data governance to develop.
3. Each business unit: data providers, data maintainer, data consumers. Responsible for specific implementation issues.
4 flow control management data
Data governance management and control processes in order to make the program truly orderly landing, data standards, for example:
Collection and collation of data standards management coordinator for the organization and implementation of data providers who participate in standard data attributes, data and negotiate a draft standard in accordance with the actual situation.
After discussing the first draft of the standard data and rich many times, standard data form submitted to the draft audit standard data management decision makers.
After modifying the data standard management decision-makers to discuss the audit, carried out by the data standard data standards management coordinator again perfect and complete data standard release.
5 Data management control tools
We must first of its profits. Data management control tool is designed to help companies better regulate the execution ground. It is generally accepted that data governance should at least cover the following domains: data asset management, standard data management, metadata management, data quality management, data management, and operation and maintenance of data lifecycle management.
• data standards: Data Governance organization in promoting and guidance, follow the consensus to develop data standards, using standardized processes to control the entire process of implementing data standardization.
• Metadata: centralized management model for metadata management, enterprise metadata logic centralized, that is, the metadata management module as the company metadata uniform distribution source, centralized management of metadata, providing metadata centrally create, maintain and query.
• Data Quality: Data from the plan, acquiring, storing, sharing, maintenance, application, dying and other data for each stage of the life cycle may lead to quality problems, identification, measurement, monitoring, early warning and a series of management activities, and by improving the organization and improve management so that data quality was further improved.
• Data assets: planning, control, provides a set of business functions, data and data assets, including the development of plans, policies, programs, projects, processes, methods and procedures for implementation and monitoring of relevant data, so as to control, protect, enhance data assets the value of.
• Data security: through planning, developing, implementing data security policies and security policy measures to provide for the effective enterprise data authentication, authorization, access, and audit.
• operation and maintenance of data: data assets, including operation and maintenance, operation and maintenance of data quality, operation and maintenance tools can help to improve the overall efficiency of operation and maintenance of enterprise data.
6 Conclusion
In the data asset value is highly recognized and exploitation of today, not only need data governance as a management function in the implementation of the enterprise, it should become a corporate culture. Enterprise data management at all levels must continue to communicate the importance of the contribution of business education and promotion of the value of data assets and data management functions. To enhance the awareness of data users of data governance and data governance recognition of the extent of the benefit is based on continuous improvement of enterprise data management mechanism, fully tap the business value of data to enhance their core competitiveness.