Data quality optimization method, equipment and medium

Abstract

The invention relates to a data quality optimization method and device, and a medium. The method comprises the following steps: determining a data quality optimization object; constructing a data quality optimization type library of the steel supply chain, wherein the data quality optimization type library comprises a data quality rule library, a data asset grading library and a business influence condition library; scoring table data quality of the optimization object based on a data quality rule base; determining the importance of the data assets of the optimization object based on the data asset classification library, and obtaining an asset importance coefficient; determining a service influence degree possibly generated by the data quality problem of the optimization object based on a service influence condition library, and obtaining a service influence coefficient; determining a table data health degree score based on the table data quality score, the asset importance coefficient and the business influence coefficient; and optimizing the data quality optimization object based on the table data health degree score. Compared with the prior art, the method has the advantages of high quality detection efficiency, comprehensive detection dimensions and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a data quality optimization method, device and medium. Background Art

[0002] The steel industry Internet is a platform that provides the main participants in the steel industry chain with ecological service capabilities from interaction, matching, trading to delivery throughout the entire process. It has greatly changed the way various factors are produced and value is created, and has played an important role in the current resource allocation and economic organization model. Through the steel industry Internet platform, the "five flows in one" of information flow, business flow, logistics flow, capital flow, and carbon flow are realized, and the interconnection and interoperability of data in the steel industry chain is realized. Based on the construction of the middle platform capacity, rich and diverse steel supply chain data resources are accumulated, and multiple business entities and business process data resources are generated in the process, covering important business entities such as merchants, warehouses, contracts, invoices, and member registration, transactions, after-sales, transportation, warehousing, processing, and capital collection and payment. However, the current steel supply chain is long, with many participants, and the process connects with the data of multiple steel mills, warehousing and processing enterprises, and logistics carriers. Due to the diverse data sources and the lack of unified data specifications, a large number of steel mills have different data storage forms in their own systems, which leads to data accuracy, completeness, consistency, and standardization. The data quality problems such as data quality problems after aggregation lead to data flow card points on the platform, inaccurate report statistics cannot make accurate decisions, and cannot be efficiently interconnected and shared applications.

[0003] Based on the above situation, existing market data quality tools cannot identify data quality problems caused by the docking of a large amount of heterogeneous data in the supply chain. Especially for the steel supply chain, there are no targeted data quality verification rules. It is necessary to combine business analysis and customization, with high development costs and high requirements for data quality verification personnel. At the same time, existing technologies cannot effectively integrate these data, establish a data quality evaluation system and scoring baseline, and form a PDCA cycle. Therefore, it is necessary to develop a data quality optimization method suitable for the steel supply chain industry to promote the circulation of data elements and meet the market quality and standardized circulation demands of data elements. Summary of the invention

[0004] The purpose of the present invention is to provide a data quality optimization method, device and medium.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] According to a first aspect of the present invention, a data quality optimization method is provided, comprising the following steps:

[0007] Determine the data quality optimization targets;

[0008] Construct a data quality optimization type library for the steel supply chain, wherein the data quality optimization type library includes a data quality rule library, a data asset classification library, and a business impact situation library;

[0009] Score the table data quality of the optimization object based on the data quality rule base;

[0010] Determine the importance of the data assets of the optimization object based on the data asset classification library and obtain the asset importance coefficient;

[0011] Based on the business impact database, determine the possible business impact of the data quality problem of the optimization object and obtain the business impact coefficient;

[0012] Determine the table data health score based on the table data quality score, asset importance coefficient, and business impact coefficient;

[0013] Optimize data quality optimization objects based on table data health scores.

[0014] The rules in the data quality rule base include:

[0015] Water transport node accuracy rule: obtain the tracking time of the key nodes of water transport corresponding to the tracking type of the logistics process tracking table, compare the tracking time sequence according to the execution order of the key nodes, and judge whether the data is accurate. The key nodes of water transport include loading, leaving the port, arriving at the port, and unloading; calculate the time difference from loading to unloading completion and compare it with the preset cycle threshold to judge whether the water transport cycle data is accurate;

[0016] Steel standard category name rules: Check the consistency between the category-related data obtained from external steel mills and the category information in the category type table and category comparison table in the core master data to determine the consistency between the category name, category code, and category.

[0017] Credit amount verification rules for core enterprises in the supply chain: Check the credit amount on the credit policy application form corresponding to the trader for consistency with the credit amount in the credit management table of the operation center, and determine whether the credit amount allocated to each subsidiary in operation is consistent with the credit amount reviewed by the credit policy.

[0018] In the data quality rule base, the quality types include completeness, accuracy, timeliness, consistency, standardization, queryability and richness, among which,

[0019] Integrity includes integrity detection and relevance detection. The integrity detection is used to detect non-existent or missing record data, and the relevance detection is used to detect record data where the foreign key has no corresponding primary key.

[0020] As for accuracy, it includes uniqueness detection, validity detection, non-duplicate record detection, authenticity detection and precision detection, wherein the uniqueness detection is used to detect record data whose primary key does not meet the uniqueness constraint, the validity detection is used to detect record data whose domain and value do not meet the defined valid range, the non-duplicate record detection is used to detect record data with duplicate records, the authenticity detection is used to detect record data that is inconsistent with objects in the real world, and the precision detection is used to detect record data whose data accuracy does not meet the business accuracy requirements;

[0021] For timeliness, including timeliness detection, the timeliness detection is used to detect recorded data that does not meet the time requirements of business applications;

[0022] For consistency, including consistency detection, the consistency detection is used to detect the record data that is inconsistent within the system or with other systems;

[0023] For standardization, it includes comprehensibility detection and standardization detection, wherein the comprehensibility detection is used to detect record data that is not easy to understand, and the standardization detection is used to detect record data that does not comply with data standards, business rules, authoritative reference data or has incomplete metadata;

[0024] The queryability includes availability detection, which is used to detect record data that is unavailable or does not meet business usage;

[0025] Richness includes richness detection, where the richness detection is used to detect the number of fields required by the business that are missing from the table.

[0026] The calculation method of the table data quality score is:

[0027] Table data quality score = 1-number of objects with quality problems in the table / total number of objects checked in the table.

[0028] The method for determining the asset importance coefficient is:

[0029] The data used directly or indirectly for the development and analysis of core business is given the highest level of importance, corresponding to the first asset importance coefficient;

[0030] Data used directly or indirectly for the operation of general data products and data applications are given a medium level of importance, corresponding to the second asset importance coefficient;

[0031] The lowest level of importance is assigned to data that is not directly or indirectly used, corresponding to the third asset importance coefficient, where the first asset importance coefficient > the second asset importance coefficient > the third asset importance coefficient.

[0032] The method for determining the business impact coefficient is:

[0033] The business problems caused by data quality issues are classified in order of business impact from high to low: business system failure, data platform failure, data operation and maintenance problem, impact on data development progress, making data analysis impossible, and no business impact yet. The business impact coefficient is assigned according to the business impact, where the higher the business impact, the greater the business impact coefficient value.

[0034] The calculation method of the table data health score is:

[0035] Table data health score = table data quality score * (1-asset importance coefficient * business impact coefficient);

[0036] The business impact coefficient is the highest business impact coefficient corresponding to the data in the table.

[0037] The method for optimizing the data quality optimization object based on the table data health score is:

[0038] Based on the table data health score, data with quality problems are identified, and whether they are internal data or external data are distinguished. For internal data, data source governance is adopted to optimize the data when it is formed to ensure the quality of data generated by the system; for external data, data quality is optimized during the docking process to ensure the quality of data implemented in the system.

[0039] According to a second aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0040] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the program implements the method described above when executed by a processor.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention standardizes and quantifies the data quality assessment process by setting various influence coefficients, such as business influence coefficient, data importance coefficient, etc., realizes automatic scoring of system data quality, improves data quality inspection efficiency, and facilitates business operations.

[0043] 2. The quality rule library established by the present invention performs quality detection on data from multiple angles, covers a comprehensive range of angles, and detects data quality more accurately. The final evaluation result is highly reliable, based on which the source of data problems can be effectively identified and the data can be optimized in a targeted manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 A flowchart for optimizing data quality in one embodiment;

[0046] Figure 3 A functional architecture diagram of data quality inspection in one embodiment;

[0047] Figure 4 The following is a technical flow chart of a data quality inspection tool in one embodiment. DETAILED DESCRIPTION

[0048] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0049] Example 1

[0050] This embodiment provides a data quality optimization method, such as Figure 1 As shown, the following steps are included:

[0051] S1, determine the data quality optimization object.

[0052] In the steel supply chain scenario, there are a large number of dispersed entities in the upstream and downstream, and data quality problems such as inconsistent standards and inaccurate data exist among different entities. Data quality problems are mainly exposed in the following situations:

[0053] (1) Data problems lead to system connection failures;

[0054] (2) Core business data quality (such as master data such as category, origin, warehouse, standard address, etc.);

[0055] (3) Abnormal company-level dashboard data (such as GMV early adjustment meeting and IPO dashboard).

[0056] S2, build a data quality optimization type library for the steel supply chain, classify and refine data quality issues, specifically including a data quality rule library, a data asset classification library, and a business impact situation library.

[0057] (1) Data quality rule base: refers to the basic classification of data problems, including validity, accuracy, etc.

[0058] Combined with the deconstruction of past business problems into data quality rules, this embodiment summarizes 7 major categories (completeness, accuracy, timeliness, consistency, standardization, queryability and richness) and 13 subcategories (completeness detection, relevance detection, uniqueness detection, validity detection, non-duplicate record detection, authenticity detection, accuracy detection, timeliness detection, consistency detection, comprehensibility detection, standardization detection, availability detection, and richness detection) rules, as shown in Table 1. At the same time, combined with the daily operation and maintenance of the data center and the release of enterprise data standards, the scope of data quality rules is continuously improved, and more than 100 quality rules with characteristics of the steel supply chain industry are formed, such as the credit amount verification rules for core supply chain enterprises, the accuracy rules for water transport nodes, the name rules for standard steel categories, and the origin rules.

[0059] Water transport node accuracy rules: Take the tracking type corresponding to the logistics process tracking table for the tracking time of key nodes such as loading, departure, arrival, and unloading of water transport, and compare them. If the relevant rules are not met, it indicates that there are quality problems in the water transport node data. For example: 1. Time sequence: The tracking time of the corresponding node should be loading first, then unloading, then arrival, and finally unloading. 2. Comparison of the time from loading to unloading (average transportation cycle of water transport * 0.1).

[0060] Standard steel category name rules: Compare the category-related data from external steel mills with the category information in the variety type table and variety comparison table in the core master data to perform consistency testing, such as whether the same variety name corresponds to different variety codes, variety categories, etc.

[0061] Credit amount verification rules for core enterprises in the supply chain: compare the credit amount on the credit policy application form corresponding to the trader with the credit amount in the credit management table of the operation center to ensure that the credit amount allocated to each subsidiary in operation is consistent with the credit amount reviewed by the credit policy, and perform consistency testing.

[0062] The calculation method of the table data quality score corresponding to the relevant rules shown in Table 1 is:

[0063] Table data quality score = 1-number of objects with quality problems in the table / total number of objects checked in the table.

[0064] For each rule, a corresponding score can be calculated. The score belongs to the range [0,1] and is presented in the form of percentage.

[0065] Table 1

[0066]

[0067]

[0068] (2) Data asset classification library: refers to the classification and classification of data

[0069] The importance of data assets is graded and given different asset importance coefficients, as shown in Table 2.

[0070] Table 2

[0071]

[0072] (3) Business impact database: refers to the impact of data quality issues, including impact on business systems, data analysis, etc.

[0073] Different business impact coefficients are given to the possible business impact of data quality issues, as shown in Table 3.

[0074] Table 3

[0075]

[0076]

[0077] S3: Score the table data quality of the optimization object based on the data quality rule base.

[0078] S4, determining the importance of the data assets of the optimization object based on the data asset classification library, and obtaining the asset importance coefficient.

[0079] S5, determining the possible business impact of the data quality problem of the optimization object based on the business impact situation database, and obtaining the business impact coefficient.

[0080] S6: Determine the table data health score based on the table data quality score, asset importance coefficient, and business impact coefficient:

[0081] Table data health score = table data quality score * (1-asset importance coefficient * business impact coefficient);

[0082] The business impact coefficient is the highest business impact coefficient corresponding to the data in the table.

[0083] Through the above steps S3-S6, manually triggered or periodically triggered data quality detection is performed, a data quality dashboard is generated, and submitted to the system user or system manager, so that the data quality situation can be easily monitored.

[0084] By setting a data quality inspection cycle, conducting a comprehensive physical examination of data quality on a system-by-system basis and providing quantitative scores, it is helpful to promptly discover data quality issues, locate the causes of the problems, and reduce the impact of data quality issues on the business.

[0085] S7: Optimize the data quality optimization object based on the table data health score.

[0086] There are different data quality optimization methods for different data quality problems. By accumulating solutions to data quality problems, problems can be handled more efficiently. In this embodiment, for internal data, the data source governance method is adopted to optimize the data when it is formed to ensure the data quality generated by the system; for external data, the data quality is optimized during the docking process to ensure the data quality of the system.

[0087] For example, the quality problem of water transport data: through the fishbone diagram analysis, the source system of data generation is proposed to carry out data optimization. Using the ship location data and the electronic fence of the dock anchorage berth, the logistics equipment perception status (anchored-berthed-in transit-delivered) is established in the ship batch management table, and the physical status of the ship is recorded. Combined with the business documents received by the platform and the water transport trajectory, the current logistics node of the ship is automatically determined, and the time data is recorded. The time difference formed by comparing the equipment perception status and the water transport node status is formed, and the business personnel are reminded to click the logistics tracking status in time.

[0088] Variety quality issues: After receiving variety information from external interfaces and telegrams, the platform will first implement the received information and then compare it with the variety information in the core master data. If the comparison does not match, the business personnel will first confirm with the upstream traders whether the variety information is correct. If there is no problem, an application for adding a new variety or variety comparison will be initiated. After internal review and confirmation by the company, the corresponding variety information will be added.

[0089] One application scenario of this method is Figure 2 As shown in the figure, by establishing a standard rule base, quality inspection management and quality problem management, on the basis of applying Six Sigma quality management theory and data management capability maturity assessment model (DCMM), the overall plan for optimizing data quality in the steel supply chain is planned. According to the data physical examination score, a "Data Physical Examination Health Report" is generated, and the root cause analysis method (fishbone diagram) is used to fully analyze from multiple angles of people, machines, materials, methods, environment, and measurement to form a data quality problem analysis report, so as to continuously improve the standardization, consistency, and richness of data quality. In addition, a data quality case library can be accumulated to provide a reference for data problem solutions. By publishing a data quality analysis report, the causes of the problem are analyzed in detail from the aspects of business and technology, and the solution, responsibility center and rectification period are clarified to ensure the implementation of data quality management. By continuously summarizing and analyzing data quality problems, data quality solutions are accumulated to provide guidance for future data quality management.

[0090] Example 2

[0091] Based on the data quality optimization method described in Example 1, this embodiment provides a data inspection tool, whose functional architecture is as follows: Figure 3 shown.

[0092] By utilizing big data processing and analysis technologies, we independently develop data health check tools, which can automatically check and score data quality rules based on the above-mentioned data quality optimization methods, and realize real-time monitoring of data quality and trend analysis.

[0093] The data health check tool includes the data layer, storage layer, functional layer, and application layer. The functional layer includes quality rule management, verification task scheduling, data quality problem management, etc. The quality rule configuration quantifies the business impact coefficient and asset classification coefficient of data quality problems, and the data quality problem management realizes the online management of the problem list. Verification task management is divided into scheduled tasks and manual execution, which tracks the improvement of data quality on a quarterly basis. It can also be executed manually to track data quality. The application layer forms a data quality dashboard based on the problem data and system health check result data formed by the functional layer. The data quality dashboard is divided into the main dashboard and the sub-dashboard. The main dashboard displays the quality health score of each system on a quarterly basis, which is convenient for analyzing the quality health trend of each system. The sub-dashboard compares and displays data quality scores, number of problems, number of abnormal data records, abnormality rate and other information from the dimensions of quality rules and impact.

[0094] like Figure 4 The technical implementation process in an embodiment is shown, which includes the following implementation points:

[0095] 1. Data Quality Basic Configuration Library

[0096] Maintain the basic configuration data required in the data quality detection system (data quality rules, business impact types, data asset classification).

[0097] 2. Data Quality Issues Checklist

[0098] 1) Maintain the collected data quality issues in the quality issue list table, and write data quality rule SQL to support modification of problem data in the maintenance table.

[0099] 2) Rule SQL field explanation: Formatted inspection rule SQL, unified output: problem list number, percentage, latest creation date, latest document number.

[0100] 3) Rule detail SQL field explanation: Formatted detail inspection rule SQL, output: problem list number, document number, document creation date, spare fields 1-4, detail data is sorted by creation date and the latest 10 data are taken (limit 10);

[0101] 3. Data Quality Inspection Scheduling

[0102] 1) Scheduled tasks: quarterly execution

[0103] Scheduled task configuration: Start once a quarter, query the problem list table: data on the identified status (solved / to be solved / temporarily deferred / to be tested), obtain rule SQL and rule detail SQL data, push rule SQL and rule detail SQL data to the platform for execution, and implement the returned result data.

[0104] 2) Manual execution

[0105] Query the problem list table: data with identified status (solved / to be solved / temporarily postponed / to be tested), manually click on quality inspection, push the rule SQL and rule detail SQL data to the platform for execution, and implement the returned result data.

[0106] 4. Data Quality Test Results

[0107] The results returned by data quality scheduling are implemented in the data quality detection main table and data quality detection detailed table, and the corresponding data quality scores are calculated synchronously.

[0108] Example 3

[0109] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0110] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0111] The processing unit performs the various methods and processes described above, such as methods S1 to S7. For example, in some embodiments, methods S1 to S7 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of methods S1 to S7 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S7 in any other appropriate manner (e.g., by means of firmware).

[0112] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0113] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0114] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0115] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning, or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A data quality optimization method, characterized in that: The following steps are involved: Determine the data quality optimization targets; Construct a data quality optimization type library for the steel supply chain, wherein the data quality optimization type library includes a data quality rule library, a data asset classification library, and a business impact situation library; Score the table data quality of the optimization object based on the data quality rule base; Determine the importance of the data assets of the optimization object based on the data asset classification library and obtain the asset importance coefficient; Based on the business impact database, determine the possible business impact of the data quality problem of the optimization object and obtain the business impact coefficient; Determine the table data health score based on the table data quality score, asset importance coefficient, and business impact coefficient; Optimize data quality optimization objects based on table data health scores.

2. A data quality optimization method according to claim 1, characterized in that: The rules in the data quality rule base include: Water transport node accuracy rule: obtain the tracking time of the key nodes of water transport corresponding to the tracking type of the logistics process tracking table, compare the tracking time sequence according to the execution order of the key nodes, and judge whether the data is accurate. The key nodes of water transport include loading, leaving the port, arriving at the port, and unloading; calculate the time difference from loading to unloading completion and compare it with the preset cycle threshold to judge whether the water transport cycle data is accurate; Steel standard category name rules: Check the consistency between the category-related data obtained from external steel mills and the category information in the category type table and category comparison table in the core master data to determine the consistency between the category name, category code, and category. Credit amount verification rules for core enterprises in the supply chain: Check the credit amount on the credit policy application form corresponding to the trader for consistency with the credit amount in the credit management table of the operation center, and determine whether the credit amount allocated to each subsidiary in operation is consistent with the credit amount reviewed by the credit policy.

3. A data quality optimization method according to claim 1, characterized in that: In the data quality rule base, the quality types include completeness, accuracy, timeliness, consistency, standardization, queryability and richness, among which, Integrity includes integrity detection and relevance detection. The integrity detection is used to detect non-existent or missing record data, and the relevance detection is used to detect record data where the foreign key has no corresponding primary key. As for accuracy, it includes uniqueness detection, validity detection, non-duplicate record detection, authenticity detection and precision detection, wherein the uniqueness detection is used to detect record data whose primary key does not meet the uniqueness constraint, the validity detection is used to detect record data whose domain and value do not meet the defined valid range, the non-duplicate record detection is used to detect record data with duplicate records, the authenticity detection is used to detect record data that is inconsistent with objects in the real world, and the precision detection is used to detect record data whose data accuracy does not meet the business accuracy requirements; For timeliness, including timeliness detection, the timeliness detection is used to detect recorded data that does not meet the time requirements of business applications; For consistency, including consistency detection, the consistency detection is used to detect the record data that is inconsistent within the system or with other systems; For standardization, it includes comprehensibility detection and standardization detection, wherein the comprehensibility detection is used to detect record data that is not easy to understand, and the standardization detection is used to detect record data that does not comply with data standards, business rules, authoritative reference data or has incomplete metadata; The queryability includes availability detection, which is used to detect record data that is unavailable or does not meet business usage; Richness includes richness detection, where the richness detection is used to detect the number of fields required by the business that are missing from the table.

4. A data quality optimization method according to claim 1, characterized in that: The calculation method of the table data quality score is: Table data quality score = 1-number of objects with quality problems in the table / total number of objects checked in the table.

5. A data quality optimization method according to claim 1, characterized in that: The method for determining the asset importance coefficient is: The data used directly or indirectly for the development and analysis of core business is given the highest level of importance, corresponding to the first asset importance coefficient; Data used directly or indirectly for the operation of general data products and data applications are given a medium level of importance, corresponding to the second asset importance coefficient; The lowest level of importance is assigned to data that is not directly or indirectly used, corresponding to the third asset importance coefficient, where the first asset importance coefficient > the second asset importance coefficient > the third asset importance coefficient.

6. A data quality optimization method according to claim 1, characterized in that: The method for determining the business impact coefficient is: The business problems caused by data quality issues are classified in order of business impact from high to low: business system failure, data platform failure, data operation and maintenance problem, impact on data development progress, making data analysis impossible, and no business impact yet. The business impact coefficient is assigned according to the business impact, where the higher the business impact, the greater the business impact coefficient value.

7. A data quality optimization method according to claim 1, characterized in that: The calculation method of the table data health score is: Table data health score = table data quality score * (1-asset importance coefficient * business impact coefficient); The business impact coefficient is the highest business impact coefficient corresponding to the data in the table.

8. A data quality optimization method according to claim 1, characterized in that: The method for optimizing the data quality optimization object based on the table data health score is: Based on the table data health score, data with quality problems are identified, and whether they are internal data or external data are distinguished. For internal data, data source governance is adopted to optimize the data when it is formed to ensure the quality of data generated by the system; for external data, data quality is optimized during the docking process to ensure the quality of data implemented in the system.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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