The goal of building a data standard system is to achieve the integrity, validity, consistency, standardization, openness and sharing management of internal data within the enterprise through the formulation and release of unified data standards, combined with institutional constraints, system control and other means, and provide Data governance lays a solid foundation and provides standardized and effective basis for data asset management activities.
Data standard planning mainly refers to the establishment of a data standard classification framework by enterprises and the formulation of implementation routes for data standard management. The data standards planning process mainly includes the following six stages:
(1) Data standards research
The data standards research work is mainly carried out from three aspects: enterprise business operation and management level, national and industry-related data standard regulations level, and information and business system data status. The research content includes the existing data business meaning, data standard classification, data elements Definitions, data item attribute rules and relevant international standards, national standards, local standards and industry data standards, etc.;
(2)Business and data analysis
Mainly based on the data standard survey results and the principles of data standard system construction, preliminary research will be conducted on the overall classification framework and definition of data standards, as well as the support for business;
(3) Research and refer to industry best practices
Collect and study data standard system construction cases, and study and learn from the practical experience of enterprises and units in the same industry in planning data standard systems in this industry;
(4) Define the data standard system framework and classification
Based on the data standard survey results and industry best practices, and based on the analysis of the company's existing business and data status, define the company's own data standard system framework and classification;
(5) Develop a data standard implementation roadmap
Based on the defined data standard system framework and classification, combined with the enterprise's own priorities in the construction of business systems and information systems, formulate a phased and step-by-step implementation roadmap for data standards;
(6) Approval and release of data standards framework and plans
The decision-making layer of data standard management reviews the data standard system framework and planning implementation roadmap, approves and releases it.
Data standards management tools should include: standard classification management, standard addition, deletion, modification, standard import and export, standard review, standard release, standard version management, standard implementation mapping, standard implementation evaluation, standard monitoring and other functions. At the same time, in order to better ensure the implementation of data standards, it is best to use it in conjunction with metadata management tools.