T/BDIA 9002-2024 Data asset quality classification standard preparation requirements
T/BDIA 9002-2024 Data asset quality classification standard preparation requirements
Basic Information
Scope
Scope: Table of Contents Preface. II Introduction. III 1 Scope.1 2 Normative Reference Documents.1 3 Terms and Definitions.1 4 Naming Rules for Data Asset Quality Grading Standards.1 5 Requirements for Preparing Normative References and Term Definitions for Data Asset Quality Grading Standards.2. 6 Requirements for Preparing Core Chapters of Data Asset Quality Grading Standards.2. 7 Requirements for Preparing Normative Appendices for Data Asset Quality Grading Standards.2. Appendix A (Normative Appendix) Data Asset Quality Grading Standard Template.2. References.3. Main Technical Content: With the introduction of the concept of "data assets," the technical framework based on "data resources" in the past will face corresponding reconstruction. This standard focuses on the business use value of data from the perspective of "data assets," distinguishing it from the technical development and management needs focused on "data resources." It proposes a data asset quality attribute system based on the authenticity of data records for trusted use, and the richness of business expression of data fields and compliance with public norms as the main content. We hope that the data asset quality standards prepared based on this quality attribute framework can more directly stimulate the generation of data element use needs. At the same time, considering that from the perspective of data use, it often leads to the need for degree-based quality expressions such as good or bad, superior or inferior. Based on the current development stage of the data element market, this standard selects "completeness" as a supporting attribute for quality degree expression, which is mainly combined with objective objects such as data fields, data tables, and data records. Based on the number and refinement of the objects combined with it, a basic, professional, and leading data asset quality grading framework is proposed: - "Basic Level": Specification expression of comple