Data element full life cycle management method and system based on data governance

Abstract

The invention relates to the technical field of data governance, in particular to a data element full-life-cycle management method and system based on data governance, and the method comprises the following steps: making a standard rule, identifying a data element, extracting the data element, determining a source data element, managing the data element, and using the data element. The method has the beneficial effects that the setting of the unified standard of the data element is a final affirmation reference for identifying, disjunction, source fixing, management and use of full life cycle operation, and is a unified reference basis for affirmation work of the reference data element; from identification, extraction and analysis, classification management, quality management, adjustment and updating of the reference data elements to service use, full-life-cycle governance is comprehensively carried out on the reference data elements.

Description

Technical Field

[0001] This invention relates to the field of data governance technology, specifically to a method and system for managing the entire lifecycle of data elements based on data governance. Background Technology

[0002] In the field of data governance, there is a lack of a complete and standardized method for managing the entire lifecycle of data elements. This includes the absence of unified and clear processes and standards for the formulation, identification, extraction, source determination, management, and use of data element standard rules, which leads to chaotic data element management and affects the effective utilization and sharing of data. Based on this, a data element lifecycle management method based on data governance is proposed. Summary of the Invention

[0003] The purpose of this invention is to provide a data element lifecycle management method and system based on data governance to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a data element lifecycle management method based on data governance, comprising the following steps: Standard rule development: Develop rules for the attributes of data elements and the description of those attributes, covering naming rules, definition rules, classification rules, data type setting rules, value range setting rules, and principles for identifying data source units, providing reference standards for subsequent steps; Identify data elements: Formulate data element initiation tasks, clarify specific task requirements, participating units and roles in data element collection, planned completion time, start time, and completion objectives, and generate a preliminary list of data elements and review and confirm it according to data element standards; Data element extraction: Extracting, compiling, and quality analyzing data elements to confirm the relevant attribute descriptions that make each data element unique. Defining the source data element: Defining the data source unit of the data element, which is divided into two cases: single source unit and multiple source units negotiated to determine the source; Managing data elements: This includes quality management and dynamic maintenance and update management of data elements; Data Elements Usage: After the baseline data elements are approved and successfully published, they are available for relevant data element users to query, apply for, and use, providing two methods: user-initiated query and system interface service.

[0005] Preferably, the data element identification steps specifically include: formulating relevant content for the baseline data element collection task and issuing the data element collection task, including participating units, participating roles, planned completion time, start time, and completion objectives; carrying out data element identification, collection, and organization work, organizing the number of data elements, data element names, and source departments; and reviewing the completion status of the identification task, including the names of units with tasks to be completed, the list of data element identification tasks to be completed, and the names of units that have completed the task.

[0006] Preferably, the data element extraction step specifically includes: after the data element identification task is completed, extracting and compiling the attributes of the data elements that have passed the identification review, providing specific examples, and explaining the compilation guidelines and compilation examples for the definition, name, characteristics, relationships, data types, value ranges, and source units of the data element attributes; compiling the definition, name, characteristics, relationships, data types, value ranges, and source units of the data element attributes according to the data element compilation examples; submitting and summarizing the compiled data elements, organizing business experts to conduct review and examination; developing a quality analysis model, generating models for ambiguity, polysemy, and duplication, and conducting quality analysis on the writing quality, ambiguity, and polysemy of the data elements based on the models, compiling a list of data element writing attributes that fail to pass the quality analysis and returning it to the compiler, and incorporating the compiled data elements that pass the quality analysis into the benchmark data element table library.

[0007] Preferably, in the step of determining the source of data elements, determining the source of a single data source unit specifically includes: issuing the task of identifying data elements and the data element compiler including the data source unit content of the data element when writing the data element attributes, mainly including the data source unit name and the data source unit organization code; when the data source unit name and the data source unit organization code are the same, it means that the data element has been successfully determined. The process of determining the source of multiple data elements through negotiation includes: when issuing a task, if there are multiple source units for a data element, listing and organizing negotiations with the relevant source units to clarify the source unit of the data element; when there is an unclear source unit for a data element, determining the unique source unit by adjusting the data element and its attribute description, and forming a list of multi-source data elements to be confirmed; re-organizing the list of multi-source data elements, organizing negotiations to clarify the unique source unit, including the source unit name and organization code, and initiating the source unit claim; once only one source unit successfully claims the negotiated data element, it means that the source of that data element has been successfully determined.

[0008] Preferably, in the data element management step, dynamic update management specifically includes: the data source unit submits an application for changes to its data elements, along with the data element name before the change, the data element name after the change, the data element attributes before the change, the data element attributes after the change, and the change behavior; the data source unit reviews the data element change application; after the review is approved, the data source unit re-maintains the data elements and specific attribute descriptions after the change application and submits them for review; based on the quality analysis model, an ambiguous model, a polysemous model, and a duplicate model are generated to conduct quality analysis and review of the data element's writing quality, ambiguity, and polysemy; after the review is approved, the data element is successfully dynamically updated.

[0009] A data element lifecycle management system based on data governance includes the following modules: Standard rule formulation module: used to formulate the attributes and description rules of data elements, covering data element naming rules, definition rules, classification rules, data type setting rules, value range setting rules, and data source unit identification principles, providing reference standards for subsequent data element identification and extraction; Data element identification module: responsible for formulating data element initiation tasks, including specific task requirements, participating units in data element collection, participating roles, planned completion time, start time, and completion goals, and generating a preliminary list of data elements based on the task, and reviewing and confirming the list according to data element standards; Data element extraction module: Extracts, compiles, and performs quality analysis on data elements, and confirms the relevant attribute descriptions that make a data element unique. Source data element module: Defines the data source unit of the data element, and divides it into two processing methods based on whether it is a single data source unit or a negotiated data source unit; Data element management module: performs quality management and dynamic maintenance and update management of data elements; The Data Element Module allows users to query, apply for, and use approved and successfully published benchmark data elements. It provides two usage methods: user-initiated query and system interface service. The module also provides unified design, publication, and daily maintenance and management of the benchmark data element query status and interface service status.

[0010] Preferably, the data element identification module specifically includes: Task formulation and release unit: Used to formulate relevant content for baseline data element collection tasks and release data element collection tasks. The task content includes participating units, participating roles, planned completion time, start time, and completion objectives. Identification, Collection, and Organization Unit: Conducts data element identification, collection, and organization work, and organizes the number of data elements, data element names, and source departments. Task review and viewing unit: Review and view the completion status of identification tasks, including the name of the unit to be completed, the list of data element identification tasks to be completed, and the name of the unit that has completed the task.

[0011] Preferably, the data extraction module specifically includes: Attribute Extraction and Compilation Guidance Unit: After the data element identification task is completed, the attributes of the data elements that have passed the identification and review are extracted and compiled. Specific examples are provided to illustrate the compilation guidelines and examples for the definition, name, characteristics, relationship, data type, value range, and source unit of data element attributes. Initial Attribute Compilation Unit: Following the data element compilation example, the definition, name, characteristics, relationship, data type, value range, and source unit of the data element attributes are compiled initially as required. Submission to review unit: Submit the completed data elements for compilation. After summarizing the extracted compilation content of each data element, organize business experts to carry out review and examination work. Quality Analysis and Review Unit: Develops quality analysis models, generates models for ambiguity, polysemy, and duplication, and performs quality analysis on the writing quality, ambiguity, and polysemy of data elements based on these models. It also creates a list of data element writing attributes that fail the quality analysis and returns it to the compiler. Data elements that pass the quality analysis are incorporated into the baseline data element table library.

[0012] Preferably, in the source-defined data element module, the single data source unit specifically includes: The data source information includes the following units: the task of issuing data element identification tasks and the data element compilers include the data source unit content of the data element when writing data element attributes, mainly including the data source unit name and the data source unit organization code information; Single data source confirmation unit: When the data source unit name and data source unit organization code are both the same, the data element is confirmed to have been successfully identified as a source. Most source units negotiated source determination specifically includes: Multi-source sorting and negotiation unit: When issuing tasks, check if there are multiple source units for data elements, sort out these data elements in a list, and organize negotiations with relevant source units to clarify the source units of the data elements; Adjust and determine the unit to be confirmed: If there are data elements whose source units are unclear due to negotiation, determine the unique source unit by adjusting the data elements and data element attribute descriptions, and form a list of multi-source data elements to be confirmed; Reorganize the claiming units: Reorganize the list of multi-source data elements, organize consultations, identify the unique data source unit, including the name of the data source unit and the organization code of the data source unit, and initiate the claiming of data source units; Multi-source source identification confirmation unit: When only one data source unit successfully claims the data element after negotiation, the source identification of that data element is confirmed to be successful.

[0013] Preferably, in the data element management module, dynamic update management specifically includes: Change application unit: The data source unit submits an application for changes to the data elements of its unit, and submits the data element name before the change, the data element name after the change, the data element attributes before the change, the data element attributes after the change, and the change behavior in the application. Application Review Unit: Reviews data element change applications from data source units; Maintenance and submission for review: After the review is approved, the data source unit re-maintains the data elements and specific attribute descriptions after the change application and submits them for review. Update Quality Audit Unit: Based on the quality analysis model, generate ambiguous models, polysemous models, and duplicate models to conduct quality analysis and audit on the writing quality, ambiguity, and polysemy of data elements. After the audit is passed, the data element is confirmed to have been dynamically updated successfully.

[0014] Compared with the prior art, the beneficial effects of the present invention are: The data element lifecycle management method and system proposed in this invention, based on data governance, establishes a unified standard for data elements, serving as the final benchmark for the entire lifecycle of identification, extraction, source determination, management, and use. This standard provides a unified reference for benchmark data element identification. It comprehensively governs benchmark data elements throughout their entire lifecycle, from identification, extraction and analysis, classification management, quality management, adjustment and updates to usage services. Furthermore, it establishes a standardized, multi-party consultative, and scientifically recognized data element standard benchmark, standardizing the multi-source verification mechanism for data resources and providing a standard reference for data resource sharing services. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the present invention clear and complete, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only some, not all, embodiments of the present invention, and are merely illustrative of the embodiments of the present invention. They are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: This invention provides a technical solution: a data element lifecycle management method based on data governance, comprising the following steps: (1) Standard and rule formulation The formulation of standard rules requires the establishment of attributes for data elements and rules for describing those attributes. Specifically, this includes naming rules for data elements, definition rules, classification rules, data type setting rules, value range setting rules, and principles for identifying data source units. This provides a reference standard for subsequent identification and extraction of data elements.

[0018] (2) Identify data elements Identifying data elements primarily involves defining the data element initiation task, including specific task requirements, participating units and roles in data element collection, planned completion time, start time, and completion objectives. Based on the task, a preliminary list of data elements is generated, and this list is then reviewed and confirmed according to data element standards. Specifically, this includes the following steps: a. Develop relevant information for baseline data element collection tasks and release data element collection tasks, including participating units, roles, planned completion time, start time, and completion objectives; b. Conduct data element identification, collection, and organization, including the number of data elements, their names, and the departments from which they originate; c. Review and check the completion status of the identification tasks, which mainly includes the names of the units with tasks to be completed, the list of data element identification tasks to be completed, and the names of the units that have completed the tasks. (3) Extracting data elements Data element extraction is the main process of extracting, compiling, and analyzing the quality of data elements. It is an essential step in describing the attributes of data elements, a process of describing the relevant attributes to confirm that a data element is unique, and an important step in clarifying the source unit of the data element later.

[0019] a. After the data element identification task is completed, the attributes of the data elements that have passed the identification review are extracted and compiled. First, a specific example of data element extraction and compilation is given. The compilation guidelines and compilation examples for the data element attributes are explained respectively, including their definition, name, characteristics, relationship, data type, value range, and source unit. b. Following the data element compilation example, initially compile the definition, name, characteristics, relationship, data type, value range, source unit, and other contents of the data element attributes in sequence as required; c. Submit the completed data elements for compilation. After summarizing the extracted compilation content of each data element, organize business experts to conduct review and examination. d. Develop a quality analysis model, generate models for ambiguity, polysemy, and duplication, and evaluate the writing quality of data elements based on the model, including whether they are ambiguous, ambiguous, or have undergone quality analysis. List the attributes of data elements that fail the quality analysis and return them to the compiler. Data elements that pass the quality analysis are included in the baseline data element table library. This concludes the expert-guided data extraction process. (4) Source data elements Defining data sources involves clearly defining the data source unit for a data element. This designation assigns sole responsibility for the data element's subsequent maintenance, quality management, and dynamic updates. Based on whether it involves a single data source unit, data source definition is categorized into two cases: single-source-unit definition and multi-source-unit negotiated definition. The specific steps include the following: ① Single data source unit fixed source a. When issuing tasks to identify data elements and when the data element compilers write data element attributes, they should include the data source unit content of the data element, which mainly includes information such as the data source unit name and the data source unit organization code; b. If the source unit name and the source unit organization code are both the same, it means that the data element has been successfully sourced. ② Source determination through consultation among multiple source units a. When issuing tasks, check if there are multiple source units for the data elements, organize a list of these data elements, and organize consultations with the relevant source units to clarify the source units of the data elements; b. If there are data element source units that are unclear through negotiation, a unique data source unit can be determined by adjusting the data element and data element attribute description, and a list of multi-source data elements to be confirmed can be formed. c. Re-examine the multi-source data element list, organize consultations, identify its unique data source unit, including the data source unit name and data source unit organization code, and initiate the data source unit claiming process; d. Once only one data source unit successfully claims and agrees to the data element after negotiation, it means that the data element has been successfully sourced. (5) Management data elements Managing data elements mainly refers to the quality management and dynamic maintenance and update management of data elements.

[0020] The quality management of data elements mainly involves the management of data element quality standards. This includes the unified management of the definition rules, naming principles, classification management principles, and related business standards and specifications for benchmark data elements, as well as the unified management of the compilation specifications and standards for benchmark data elements.

[0021] The steps involved in dynamic update management are as follows: a. The data source unit submits an application for changes to its data elements, and submits the application along with the data element name before the change, the data element name after the change, the data element attributes before the change, the data element attributes after the change, and the change behavior (addition, deletion, modification), etc. b. Review data element change requests from data source units; c. After the review is approved, the data source unit re-maintains the data elements and specific attribute descriptions after the change application and submits them for review; d. Based on the quality analysis model, generate models for ambiguity, polysemy, and duplication, etc., to assess the writing quality of data elements, whether they are ambiguous, polysemous, and whether quality analysis review is conducted. Once the review is passed, the data element is successfully updated dynamically. (6) Use data elements The use of benchmark data elements mainly includes querying, applying for, and using benchmark data elements. Once approved and successfully published, benchmark data elements are available for querying, applying for, and using by relevant data element users. Specific usage methods include user-initiated queries and system interface services. The system interface services are uniformly defined and published, and the query status of benchmark data elements and the status of the interface services are maintained and managed daily.

[0022] Example 2, based on Example 1, proposes a data element lifecycle management system based on data governance, including the following modules: Standard rule formulation module: used to formulate the attributes and description rules of data elements, covering data element naming rules, definition rules, classification rules, data type setting rules, value range setting rules, and data source unit identification principles, providing reference standards for subsequent data element identification and extraction; The Data Element Identification Module is responsible for formulating data element initiation tasks, including specific task requirements, participating units, roles, planned completion time, start time, and completion objectives. It also generates a preliminary list of data elements based on the task and reviews and confirms the list according to data element standards. Specifically, it includes: a Task Formulation and Release Unit, used to formulate relevant content for baseline data element collection tasks and release these tasks, including participating units, roles, planned completion time, start time, and completion objectives; an Identification, Collection, and Organization Unit, which conducts data element identification, collection, and organization, including the number of data elements, their names, and the departments from which they originate; and a Task Review and Viewing Unit, which reviews and views the completion status of identification tasks, including the names of units awaiting completion, a list of pending data element identification tasks, and the names of units that have completed the tasks.

[0023] The data element extraction module extracts, compiles, and performs quality analysis on data elements, confirming the relevant attribute descriptions that make a data element unique; specifically, it includes: Attribute Extraction and Compilation Guidance Unit: After the data element identification task is completed, the attributes of the data elements that have passed the identification and review are extracted and compiled. Specific examples are provided to illustrate the compilation guidelines and examples for the definition, name, characteristics, relationship, data type, value range, and source unit of data element attributes. Initial Attribute Compilation Unit: Following the data element compilation example, the definition, name, characteristics, relationship, data type, value range, and source unit of the data element attributes are initially compiled according to the requirements. Submission and Review Unit: The compiled data elements are submitted and summarized. After summarizing the extracted compilation content of each data element, business experts are organized to conduct review and examination. Quality Analysis and Audit Unit: A quality analysis model is developed, generating models for ambiguity, polysemy, and duplication. Based on the model, the writing quality of the data elements is analyzed for ambiguity and polysemy. A list of data element writing attributes that fail the quality analysis is returned to the compiler. Data elements that pass the quality analysis are included in the benchmark data element table library.

[0024] The data source identification module clarifies the data source unit of a data element. Based on whether it's a single data source unit, it's divided into two processing methods: single-source-unit identification and multi-source-unit negotiated identification. Single-source-unit identification specifically includes: a data source information inclusion unit: This unit issues the data element identification task and ensures that the data element compiler includes the data source unit information when writing data element attributes, mainly including the data source unit name and organization code information; a single-source confirmation unit: When the data source unit name and organization code are the same, it confirms that the data element identification is successful. Multi-source-unit negotiated identification specifically includes: a multi-source sorting and negotiation unit: When issuing the task, it checks if the data element... There are multiple source units. These data elements are listed and sorted, and relevant source units are organized to clarify the source unit of the data elements. Adjustment and confirmation of units to be confirmed: If there are data elements whose source units are unclear, the unique source unit is determined by adjusting the data elements and data element attribute descriptions, and a list of multi-source data elements to be confirmed is formed. Re-sorting and claiming units: The list of multi-source data elements is re-sorted, and negotiations are organized to clarify its unique source unit, including the source unit name and source unit organization code, and the claiming of the source unit is initiated. Multi-source source confirmation unit: When only one source unit successfully claims the data element after the negotiation is approved, the source of the data element is confirmed to be successfully determined.

[0025] The data element management module performs quality management and dynamic maintenance and update management of data elements; dynamic update management specifically includes: Change Request Unit: The data source unit submits a request for changes to its data elements, including the original data element name, the new data element name, the original attributes, the new attributes, and the change behavior. Application Review Unit: Reviews the data source unit's data element change request. Maintenance and Submission Review Unit: After approval, the data source unit re-maintains the changed data elements and their specific attribute descriptions and submits them for review. Update Quality Review Unit: Based on the quality analysis model, generates ambiguous, polysemous, and duplicate models to analyze and review the writing quality, ambiguity, and polysemy of the data elements. Upon approval, the data element is confirmed to have been successfully updated dynamically.

[0026] The Data Element Module allows users to query, apply for, and use approved and successfully published benchmark data elements. It provides two usage methods: user-initiated query and system interface service. The module also provides unified design, publication, and daily maintenance and management of the benchmark data element query status and interface service status.

[0027] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for data element lifecycle management based on data governance, characterized in that: The method comprises the following steps: Standard rule making: making rules for the attributes and attribute descriptions of data elements, covering data element name naming rules, definition rules, classification rules, data type setting rules, value range setting rules, and data source unit identification principles, to provide reference standards for subsequent steps; Identifying data elements: making data element initiation tasks, specifying specific task requirements, data element collection participating units, participating roles, planned completion time, start time, and completion goals, and issuing a preliminary data element list and confirming it according to data element standards; Extracting data elements: extracting, compiling, and quality analyzing data elements, and confirming the relevant attribute descriptions of unique data elements; Defining source data elements: specifying the data source units of data elements, including single data source unit definition and multi-data source unit negotiation; Managing data elements: including data element quality management and dynamic maintenance and update management; Using data elements: after the benchmark data elements pass the audit and are successfully published, the relevant data element users can query, apply, and use them, providing two ways of user-initiated query and system interface service. 2.The data element full life cycle management method based on data governance according to claim 1, characterized in that: The step of identifying data elements specifically comprises: making relevant contents of benchmark data element collection tasks and publishing data element collection tasks, including data element collection participating units, participating roles, planned completion time, start time, and completion goals; developing data element identification, collection, and arrangement work, including data element quantity, data element name, and source department; and auditing and checking the identification task completion, including task to be completed unit name, to-be-completed data element identification task list, and completed unit name.

3. The data element whole life cycle management method based on data governance according to claim 2, characterized in that: The step of extracting data elements specifically comprises: after the data element identification task is completed, extracting and compiling the data element attributes that have passed the identification audit, giving specific examples, and explaining the compilation guidelines and examples of data element attribute definition, name, characteristics, relationship, data type, value range, and source unit; compiling the definition, name, characteristics, relationship, data type, value range, and source unit contents of the data element attributes according to the data element compilation examples; submitting and summarizing the compiled data elements, organizing business experts to carry out review and examination work; making quality analysis models, including whether the model is ambiguous, whether the model is polysemous, and whether the model is repeated, and analyzing the quality, ambiguity, and polysemy of the data elements according to the models; returning the data element writing attributes of the data elements that do not pass the quality analysis to the compiling party in the form of a list, and including the data element writing attributes that pass the quality analysis into the benchmark data element table library.

4. The data element whole life cycle management method based on data governance according to claim 3, characterized in that: In the step of defining source data elements, single data source unit definition specifically comprises: issuing identification data element tasks and including data source unit contents, mainly including data source unit name and data source unit organization code, in the data element attribute writing by the data element compiling party; and when the data source unit name and data source unit organization code are one, it represents that the data element definition is successful. The source unit negotiation includes: checking data elements when issuing tasks, listing and sorting the data elements, and organizing the negotiation of the related source units to determine the data element source unit; when there is a data element source unit that needs to be negotiated, determining the unique source unit by adjusting the data element and data element attribute description, and forming a list of multi-source data elements to be confirmed; re-sorting the multi-source data element list, organizing the negotiation, and determining the unique source unit, including the source unit name and organization code, and initiating the source unit claim; only when the data element is successfully negotiated and claimed by one source unit, the data element is successfully determined.

5. The data element whole life cycle management method based on data governance according to claim 4, characterized in that: The dynamic update management in the management data element step includes: the source unit applies for data element changes, and applies for the data element name before the change, the data element name after the change, the data element attribute before the change, the data element attribute after the change, and the change behavior; auditing the data element change application of the source unit; after the audit is passed, the source unit re-maintains the data element and the specific attribute description after the change, and submits for audit; according to the quality analysis model, whether the model is ambiguous, whether the model is polysemous, whether the model is repeated, and whether the model is repeated, the quality analysis audit is carried out on the writing quality, ambiguity, and polysemy of the data element, and after the audit is passed, the dynamic update of the data element is successful.

6. A data element whole life cycle management system based on data governance, applying the method of claim 5, characterized in that: The following modules are included: A standard rule making module is used to make rules for data element attributes and attribute descriptions, including data element name naming rules, definition rules, classification rules, data type setting rules, value range setting rules, and source unit identification principles, which provide reference standards for subsequent data element identification and extraction; An identification data element module is responsible for formulating data element initiation tasks, including specific task requirements, data element collection participating units, participating roles, planned completion time, start time, and completion target, and issuing a preliminary list of data elements according to the task, and auditing and confirming the list according to the data element standard; An extraction data element module extracts, compiles, and quality analyzes data elements to confirm the relevant attribute description of the unique data element; A source data element module determines the source unit of the data element, and divides it into single source unit determination and multi-source unit negotiation determination according to whether it is a single source unit; A management data element module is used for quality management and dynamic maintenance and update management of data elements; A use data element module is used for querying, applying, and using the benchmark data elements that have passed the audit and been successfully published, providing two use modes of user-initiated query and system interface service, and uniformly formulating, publishing, and daily maintaining the benchmark data element query state and interface service state.

7. The data element whole life cycle management system based on data governance according to claim 6, characterized in that: The identification data element module specifically includes: A task formulation and release unit is used to formulate benchmark data element collection task related content and release data element collection tasks, and the task content includes data element collection participating units, participating roles, planned completion time, start time, and completion target; The identification collection and arrangement unit: carries out data element identification, collection and arrangement, and arranges the number of data elements, data element names and source department contents; The task review and viewing unit: reviews and views the identification task completion, including the task to be completed unit name, the data element identification task list to be completed, and the completed unit name. 8.The data element whole life cycle management system based on data governance according to claim 7, characterized in that: The data element module specifically includes: The attribute extraction and preparation guidance unit: after the data element identification task is completed, the attribute of the data element that has passed the identification review is extracted and prepared, and specific examples are given to explain the definition, name, characteristics, relationship, data type, value range and source unit of the data element attribute, and the preparation guidance principle and preparation example are given; The attribute initial preparation unit: according to the data element preparation example, the definition, name, characteristics, relationship, data type, value range and source unit content of the data element attribute are sequentially prepared according to the requirements; The submission and review unit: the prepared data element is submitted and summarized, and after summarizing the extraction and preparation content of each data element, business experts are organized to carry out review and review work; The quality analysis and review unit: a quality analysis model is developed to generate whether the model is ambiguous, whether the model is polysemous, whether the model is repeated, and the quality of the data element writing, whether it is ambiguous, whether it is polysemous, and the data element writing attribute that does not pass is returned to the preparation party, and the data element preparation that passes the quality analysis is included in the benchmark data element table library. 9.The data element whole life cycle management system based on data governance according to claim 8, characterized in that: In the source data element module, the single data source unit source specifically includes: The data source information includes unit: the identification data element task is issued, and the data element preparation party includes the data element source unit content when writing the data element attribute, mainly including the data source unit name and the data source unit organization code information; The single data source confirmation unit: when the data source unit name and the data source unit organization code are one, the data element source is confirmed to be successful; The multi-source unit negotiation source specifically includes: The multi-source carding negotiation unit: when there are multiple source units of the data element, the data element list is carded, and the related source units are organized to clarify the data element source unit; The adjustment and determination to be confirmed unit: if there are data element source units that are not clear after negotiation, the unique data source unit is determined by adjusting the data element and the data element attribute description, and a multi-source data element list to be confirmed is formed; The re-carding and claiming unit: the multi-source data element list is re-carded, the unique data source unit is clarified, the data source unit name and the data source unit organization code are included, and the data source unit claiming is initiated; The multi-source source confirmation unit: when only one data source unit claims the data element after negotiation is successful, the data element source is confirmed to be successful.

10. The data element whole life cycle management system based on data governance according to claim 9, characterized in that: In the management data element module, the dynamic update management specifically includes: The change application unit: the data element of the source unit is changed and applied, and the data element name before the application, the data element name after the change, the data element attribute before the change, the data element attribute after the change and the change behavior are applied together; The application review unit: reviews the data element change application of the source unit; The maintenance submission auditing unit: after the auditing, the data element and the specific attribute description of the number source unit after the change application are maintained and submitted for auditing; The update quality auditing unit: according to the quality analysis model, whether the ambiguity model, whether the polysemy model, whether the repetition model, the writing quality of the data element, whether the ambiguity, whether the polysemy are analyzed and audited, after the auditing, the dynamic update of the data element is confirmed to be successful.

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