In short under the capability of data development we focus on: Where is the data stored? How to do migration and backup? Integrate and improve Standardization robustness and scalability of data modeling Abstraction and organization from business flow to data flow Data ranges and entities Implementation of methodologies such as data hierarchical modeling and dimensional modeling at the code level Quality control of data development CodeReview mechanism The above and so on. Data management What is the connotation of data management? If any readers are interested and want to learn more the author recommends a book DAMA Data Management Knowledge System Guide.
This book not only systematically summarizes and analyzes all key points in the field of data management but is also a textbook for the authoritative certification in the data field the Data Governance Engi Job Seekers Phone Numbers List neer CDGA certificate. The author briefly introduces the key connotations of the data management category. ① Data standards Connotation: It refers to the normative constraints that ensure the consistency and accuracy of internal and external use and exchange of data. Application scenarios: applied to data development and data quality management. rules based on data standards focus on business areas and quality issues and continuously monitor application status. Business terminology management is the basic work of data standard management.
Data asset management Connotation: A set of activities that plan control and provide data assets. Two key links: Data resourceization: Transforming original data into data resources so that the data has a certain potential value is a necessary prerequisite for data assetization. With the goal of improving data quality and ensuring data security it includes the following activities and functions: data model management data standard management data quality management master data management data security management metadata management data development management etc. Data capitalization : Transform data resources into data assets so that the potential value of data resources can be fully released.
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