Data credible management and control method utilizing data list management

Abstract

The invention discloses a data trusted management and control method utilizing data list management. The method comprises the following steps: S1, determining data to be delivered, creating a worksheet, and starting to make the data; s2, creating a verification sheet for the data to be delivered; s3, delivering the data in the data list, and storing the data in a data space; s4, using and transmitting the data to another data list; and S5, archiving the data. According to the data credible management and control method utilizing data list management, dynamic management and control, real-time evidence storage and result tracing can be carried out on the whole data life period, the data quality is guaranteed, and data credible management and control are achieved.

Description

Technical Field

[0001] The present invention relates to the field of data management, and particularly to a method for data trustworthy control using data inventory management. Background Art

[0002] Data credibility refers to the degree to which data is considered true, accurate, reliable, complete, and secure in a specific context. It involves multiple aspects such as data quality, accuracy, integrity, reliability, security, and privacy protection, and is the basis for data analysis and decision-making. To ensure data credibility, corresponding data management means need to be adopted.

[0003] Existing data management usually uses centralized management means such as uploading / downloading, backup, permission management, version management, etc. The advantages of these data management means are centralization and efficiency in data management, but the disadvantage is that it is impossible to conduct real-time control throughout the entire life cycle of the data itself, namely production, governance, delivery, use (maintenance), and archiving, and it cannot perform real-time evidence storage, so it is impossible to conduct result traceability. Therefore, the existing methods for data trustworthy control obviously lack a very important part of process control. Summary of the Invention

[0004] To solve the above problems, the present invention provides a method for data trustworthy control using data inventory management, which can conduct dynamic control, real-time evidence storage, and result traceability throughout the entire data life cycle, ensuring data quality and realizing data trustworthy control.

[0005] According to one aspect of the present invention, there is provided a method for data trustworthy control using data inventory management, including the following steps:

[0006] S1: Determine the data to be delivered, create a work order in the data inventory, and start producing the data. Among them, in the work order, set the data responsible person, data production / delivery time, delivery standards and requirements for the data, data group category attribution, and set data change records, set modification opinions, set the basis for data classification and management, set the result data to be visible at any time, and set the work order to be visible at any time;

[0007] S2: Create a review order in the data inventory for the data to be delivered. Among them, in the review order, set the review personnel, determine the data review process, start / complete time, and set the data governance time limit, set the review process, set the review data and opinions to be visual, and conduct full-process recording;

[0008] S3: Deliver the data in the data inventory and store it in the data space. Among them, in the delivery, set the delivery record and use the data inventory delivery method;

[0009] S4: Use the data and transfer it to another data list. When using the data, locate the data user, set the visualization of the data usage opinion, and record the attribute values of the dynamic data list.

[0010] S5: Archive the data. When archiving, archive the work order and the review order, and archive the data according to the standard.

[0011] In some embodiments, in step S1, the attribute values in the work order include work order number, work order name, work label, work scope, work type, work status, start / end time, work executor, total work amount, completed amount, and creator. The advantage is that it describes some of the attribute values in the work order.

[0012] In some embodiments, in step S1, the attribute values in the work order further include editable work descriptions, studio designs, mounted data, work attachments, work members, suggestion boxes, list configurations, message configurations, and communication windows. The advantage is that it supplements the description of the editable attribute values in the work order.

[0013] In some embodiments, in step S2, the attribute values in the review order include review order number, review order name, review process, creator, processor, creation time, and update time. The advantage is that it describes some of the attribute values in the review order.

[0014] In some embodiments, in step S2, the attribute values in the review order further include editable submission instructions, submission results, review opinions, attachment lists, and communication windows. The advantage of the supplement is that it describes the editable attribute values in the review order.

[0015] In some embodiments, in step S2, the review process aggregates the review personnel for all processes, and publishes the aggregated review opinions of each review personnel. The advantage is that it describes the content of the review process.

[0016] In some embodiments, in step S3, the data list delivery method includes visualization of the delivery progress and a pure data space. The advantage is that it describes the content of the data list delivery method.

[0017] In some embodiments, in step S4, add the person who will apply this data to the "Work Members" page in the data list, and the data will be transferred to the data list of the data that the person needs to deliver. The advantage is that it clarifies the data user, clarifies the method of data transfer to the data user, and can trace the history of data usage and the attribution of usage opinions.

[0018] In some embodiments, in step S5, the work order and the review order are rechecked to file the data according to the standards. The advantage is that the operation method of filing the data according to the standards is described. Brief Description of the Drawings

[0019] Figure 1 It is a flowchart of a data trust control method using data inventory management according to an embodiment of the present invention;

[0020] Figure 2 For Figure 1 It is a schematic diagram of the content of the work order shown;

[0021] Figure 3 For Figure 1 It is a schematic diagram of the content of the review order shown;

[0022] Figure 4 For Figure 1 It is a schematic diagram of the content of the list delivery shown;

[0023] Figure 5 For Figure 1 It is a schematic diagram of the content of the data usage shown. Detailed Embodiments

[0024] The present invention will be further described in detail below with reference to the drawings.

[0025] The entire life cycle of data mainly includes several processes such as data production, data governance, data delivery, data usage, and data archiving. And a data trust control method using data inventory management provided by the present invention can play a role in ensuring data trust during the entire life cycle of data by setting up a data inventory management method in the above processes.

[0026] Among them, a data inventory refers to various documents formed by decomposing work based on the data of deliverables according to business classifications, mainly including work orders, review orders, etc. For the delivered data, the information in the data inventory will be additional descriptions of the data, called the attribute values of the data, and the attribute values are dynamic attributes that are recorded in real time.

[0027] Such as Figure 1 As shown, the data trust control method mainly includes the following steps S1 to S5, etc.

[0028] S1: Data production.

[0029] In this step, first determine the data to be delivered and create a data inventory for this, called a work order. After receiving the work order, the data responsible person starts to produce the data, and the recording and trust control of the entire life cycle process of the data will also start.

[0030] Among them, in the work order, it is necessary to specify the data responsible person, data production / delivery time, delivery standards and requirements of the data, and the classification attribution of the data group, etc.

[0031] The attribute values in the work order generally include work order number, work order name, work label, work scope, work type, work status, start / end time, work executor, total work volume, completed volume, creator, etc.; it can also include editable types, such as work description, workroom design, mounted data, work attachments, work members, suggestion box, this order configuration, message configuration, communication window, etc.

[0032] Such as Figure 1 and 2 As shown, the work order for data trust control methods and objectives mainly includes:

[0033] 1. Set up a data responsible person: The data responsible person is not only responsible for the quality of data production, but also responsible for data maintenance throughout the life cycle to ensure the correctness, integrity, compliance, and availability of the data.

[0034] 2. Set up data change records: Through the communication window in the work order, record the data change process; the reason and history of data updates can be traced.

[0035] 3. Set up modification opinions: During the data maintenance process, data modification opinions can be timely extracted from the communication window, updated in a timely manner, and the timeliness of the data is guaranteed.

[0036] 4. Set up the basis for data classification and management: All work orders are created for delivering data according to business classification. Therefore, the work order clarifies the classification, delivery standards and other attributes of the data, providing a basis for data classification and centralized management in the data storage space, ensuring that all delivered data is valid and controlled by the data list, and providing a reliable guarantee for the data source.

[0037] 5. Set the resulting data and work order to be visible at any time: The data quality is visible at any time, which is a real-time guarantee for the correctness, integrity, and timeliness of the data.

[0038] S2: Data governance.

[0039] Create a data list that has gone through the review process for the data to be delivered, that is, a review form. In the review form, determine the data review process, review personnel (i.e., data quality control personnel), start / complete time, etc. The entire process of data governance will be recorded in this review form.

[0040] The attribute values in the review form generally include review form number, review form name, review process, creator, processor, creation time, update time, etc.; it can also include editable types, such as submission instructions, submission results, review opinions, attachment list, and communication window, etc.

[0041] As Figure 1 and 3 shown, the control methods and objectives for data credibility in the review form mainly include:

[0042] 1. Establish review personnel: The review personnel are the data quality controllers. They put forward review opinions on the data to be reviewed and publish them on the review form. The data responsible person then extracts the review opinions from the review form in a timely manner to modify and improve the data. Through iteration, the data quality is gradually improved until the data is credible.

[0043] 2. Set data governance time limit: This can ensure the timeliness of data.

[0044] 3. Set review processes: Set the review process flow in the review form. This not only aggregates the review personnel for all processes but also publishes the review opinions of each person. Compared with the traditional method of modifying data based on the review opinions for different processes separately, where there may be opinion conflicts and incomplete modifications, this way of aggregating the review opinions for all processes can more fully ensure the unity and completeness of data modification opinions, ensuring that the data is more accurate and precise and reaches the highest quality level.

[0045] 4. Set the visualization of both review data and opinions: During the review process, the data to be reviewed and the review opinions are visible in real time. Relevant personnel can communicate fully based on this to avoid misunderstandings, mistakes, and omissions as much as possible and ensure data quality.

[0046] 5. Set full-process records: The full-process data governance process records of review opinions and communication windows can be used to trace the responsibilities related to data quality and serve as quality confirmation vouchers.

[0047] S3: Data delivery.

[0048] The data that has gone through the data governance process can be delivered as credible data in the data list, stored in the data space. Based on the structured and clear data list, the delivery progress of the data can be grasped in real time, and it can be checked to ensure that the content and format of the delivered data comply with the delivery standards and that the delivered data has the attributes of credibility such as correctness, integrity, and compliance.

[0049] As Figure 1 and 4 shown, the control methods and objectives for data credibility in data delivery mainly include:

[0050] 1. Set delivery records: In the communication window of the data list, the process of data quality iteration and deliverable vouchers are recorded.

[0051] 2. Use the data list-based delivery method, including visualization of delivery progress and a pure data space;

[0052] Delivery progress visualization is based on a structured data list, creating a set of data lists for stage delivery data to centrally query the delivery progress. Especially for the delivery of a large amount of data, on the progress visualization page, it is convenient to locate the work orders of data that have not been delivered in a timely manner and find the data responsible person for follow-up. By using the data list-based delivery method, it is possible to easily control the delivery progress of a large amount of data and ensure the timeliness of reliable data.

[0053] The pure data space is in the data list, where the data version can be selected as the delivery version. Therefore, it can be determined that each data list contains delivered data. This data list-based delivery method not only purifies the data space but also ensures that the data has been iterated and confirmed to meet the delivery data standards. Moreover, the entire history of each data can be traced and queried, ensuring the credibility of the data space.

[0054] S4: Data usage.

[0055] When data is transferred during use, it is passed from one data list to another. Especially in complex business scenarios, data transfer occurs between multiple data lists. Based on the visual data list, the logic of data transfer is very clear and rigorous.

[0056] Such as Figure 1 and 5 As shown, the methods and objectives for reliable control of data during data usage mainly include:

[0057] 1. Locate the data users: Add the personnel who will apply this data to the "Work Members" page in the data list and grant them the right to use the data, which can ensure the usage rights and security of the data. The data will be transferred to the data list of the data they need to deliver. Based on reliable upstream data, it is the guarantee for downstream data to produce reliable data.

[0058] 2. Set the visualization of data usage opinions: In the communication window of the data list, users publish their opinions on data usage, and the data responsible person extracts the usage opinions to maintain the data, thereby further enhancing the credibility of the data.

[0059] 3. Record the attribute values of the dynamic data list: Since the attribute values of the data list are dynamically recorded, recording its attribute values, that is, recording all the change records of the data, helps to trace the transmission path and status of the data during data transfer, and also facilitates retrieval, query, traceability, and subsequent data analysis and processing.

[0060] S5: Data archiving.

[0061] When conducting data archiving, the data list can ensure that valuable information is retained for a long time.

[0062] The methods for trusted control of data during data archiving include:

[0063] 1. Archive the data list: Data lists such as work orders and review sheets record the changes in the entire life cycle of the delivered data. Information such as the data responsible person, review responsible person, data delivery voucher, and data maintenance voucher during the data transfer process are all very valuable. These are the vouchers for data trustworthiness, and the complete structure of the data list is a digital representation of the entire business panorama, ensuring that the delivered data in the entire business can be traced in all scenarios.

[0064] 2. Archive the data according to standards: Although the data list has clearly defined the data classification and delivery standards, and corresponding effective guarantees have been provided for the data in the above-mentioned various links, in the data archiving link, relevant reviews can still be carried out on each data list such as work orders and review sheets once again, further ensuring that the data is archived according to these standards.

[0065] A method for trusted control of data using data list management of the present invention mainly has the following

[0066] Beneficial effects:

[0067] 1. Starting from the production of data, the data responsible person, the data transfer direction, and the data timeliness can be clearly determined;

[0068] 2. In the data governance link, using the data list for tracking and control can ensure the correctness of the data to a greater extent;

[0069] 3. In the data delivery stage, through the visualization of the delivery progress and the pure data space, the delivery progress control of a large amount of data can be easily achieved, while ensuring the credibility and timeliness of the data;

[0070] 4. In the data usage stage, through the transfer path between "sheets", it is ensured that the downstream data can use the correct upstream data to generate new trusted data, and the transfer logic is rigorous, the path is clearly visible and controllable. In the data maintenance stage, all data in the data list are recorded and the results can be traced back;

[0071] 5. In the data archiving stage, the history of the data can be recorded through the data list and archived together for easy search and query.

[0072] The above are only some embodiments of the present invention. For those of ordinary skill in the art, without departing from the inventive concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. A data trust management method using data list management, characterized by: The following steps are included S1: Determine the data to be delivered, create a work order in the data list, and start making data. In the work order, set up the data responsible person, data production / delivery time, data delivery standards and requirements, data group category, and set up data change records, set up modification opinions, set up the basis for data classification and management, set up the achievement data to be visible at any time, and set up the work order to be visible at any time; S2: Create a review sheet in the data list for the data to be delivered, in which the review personnel are set up, the data review process and start / completion time are determined, and the data governance time limit, review process, review data and opinions are visualized and recorded throughout the process; S3: delivering the data in the data list and storing it in the data space, wherein during the delivery, setting the delivery record and using the data list delivery method; S4: using and transferring the data to another data list, wherein, when using, locating the data user, setting the visualization of the data usage opinion, and recording the attribute values ​​of the dynamic data list; S5: Archive the data, wherein when archiving, the work order and the review order are archived, and the data is archived according to the standard.

2. According to claim 1, a data trust management method using data list management is characterized by: In step S1, the attribute values ​​in the work order include the work order number, work order name, work label, work scope, work type, work status, start / end time, work executor, total work amount, completed amount, and creator.

3. According to claim 2, a data trust management method using data list management is characterized by: In step S1, the attribute values ​​in the work order also include editable work description, studio design, mounting data, work attachments, work members, suggestion box, order configuration, message configuration, and communication window.

4. According to claim 1, a data trust management method using data list management is characterized by: In step S2, the attribute values ​​in the review form include the review form number, review form name, review process, creator, processor, creation time, and update time.

5. According to claim 4, a data trust management method using data list management is characterized by: In step S2, the attribute values ​​in the review form also include editable review instructions, review results, review opinions, attachment list and communication window.

6. According to claim 1, a data trust management method using data list management is characterized by: In step S2, the reviewers of all processes are gathered in the review process, and the review opinions of each reviewer are collected and published.

7. According to claim 1, a data trust management method using data list management is characterized by: In step S3, the data inventory delivery method includes delivery progress visualization and a clean data space.

8. According to claim 1, a data trust management method using data list management is characterized by: In step S4, add the person to whom the data is to be applied on the "Work Member" page in the data list, and the data will be transferred to the data list of the data that the person needs to deliver.

9. The method for data trust management and control using data inventory management according to claim 1, characterized in that: In step S5, the work order and the review order are reviewed to archive the data according to the standard.

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