Data quality score evaluation method and device, equipment, medium and product
Calculating the weights of data quality dimensions and rules through hierarchical analysis method, solving the problems of differences in the importance of data rules and weight evaluation in the existing technology, and achieving the accuracy and flexibility of data quality score evaluation.
Patent Information
- Application Number
- CN202411425955.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-12
- Publication Date
- 2025-01-10
AI Technical Summary
The existing data quality score calculation methods cannot flexibly show the differences in importance between data rules, and the weights are difficult to accurately evaluate when new rules are added, resulting in the importance between data quality dimensions being ignored.
The weights of data quality dimensions and rules are determined by defining the importance matrix between data quality dimensions and rules, calculating the weights of each dimension and rules, and multiplying them by the corresponding score in the evaluation of data quality scores to achieve Accurate calculation of data quality scores.
Improves the accuracy and reliability of data quality score evaluation, supports flexible new rules addition and weight adjustments, ensuring consistency of calculation results.
Smart Images
Figure CN119293029A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data quality management, and in particular to a data quality score evaluation method, device, equipment, medium and product. Background Art
[0002] With the development of science and technology and the expansion of business scale, the amount of data generated in the process of business operations has increased dramatically, and a unified data management platform is increasingly needed to manage and optimize large amounts of data in order to extract, process and analyze data, namely data mining. Data mining aims to obtain valuable information from data and provide services for enterprise development decisions. The premise of reliable information obtained by data mining is that the data is reliable. High-quality data is the basis for the successful application of data. Therefore, data quality management is an important part of the data management platform to ensure the value of data.
[0003] The six core dimensions of data quality are accuracy, completeness, consistency, immediacy, uniqueness and validity. The management of data quality rules, evaluation and other management should be carried out around these core dimensions. The specific functions of data quality management include the configuration of quality rules, the creation and regular operation of quality tasks and the overall monitoring of data quality. Among them, the data quality score is the most intuitive indicator to show the overall data quality in the current data platform. It can be used to measure the overall data quality of the current data platform and monitor the fluctuation of platform data quality. However, the definition of data quality score calculation method varies. Common calculation methods include: directly taking the average of the scores of each rule; deducting N points for each problem that does not meet the quality rules; dividing the influence of the rules into high, medium, low and none, and weighting the scores according to the values of high (3), medium (2), low (1) and none (0); directly filling in the rule weight. Some of these data quality score calculation methods include the scores of individual rules in the data quality score according to a fixed ratio, which cannot flexibly show the difference in importance between data rules. Some ignore the connection between data quality rules and data quality dimensions, which leads to the neglect of the difference in importance between data quality dimensions. In addition, directly filling in the weights of the rules will also result in all weights needing to be readjusted when new rules are added later. As the number of rules increases, it will be difficult to accurately assess the importance of quality rules. Therefore, the existing technology has shortcomings and needs further improvement. Summary of the invention
[0004] The purpose of this application is to provide a data quality score evaluation method, device, equipment, medium and product, which can be used to evaluate data quality.
[0005] To achieve the above objectives, this application provides the following solutions:
[0006] In a first aspect, the present application provides a data quality score evaluation method, including: defining data quality dimensions of an enterprise data management platform; configuring at least one data quality rule and a score calculation method for each data quality rule under each data quality dimension; determining a judgment matrix of the importance between different data quality dimensions, and calculating the weight of each data quality dimension using a hierarchical analysis method; under each data quality dimension, determining a judgment matrix of the importance between different data quality rules, and calculating the weight of each data quality rule using a hierarchical analysis method; under each data quality dimension, calculating the score of each data quality rule based on the score calculation method of each data quality rule; under each data quality dimension, multiplying the score of each data quality rule by the sum of the weights of each data quality rule to obtain the score of each data quality dimension; multiplying the score of each data quality dimension by the sum of the weights of each data quality dimension to determine the overall data quality score of the enterprise data management platform.
[0007] In a second aspect, the present application provides a data quality score evaluation device, including: a dimension definition module, used to define the data quality dimensions of an enterprise data management platform; a rule definition module, used to configure at least one data quality rule and a score calculation method for each data quality rule under each data quality dimension; a dimension weight calculation module, used to determine the judgment matrix of the importance between different data quality dimensions, and use the hierarchical analysis method to calculate the weight of each data quality dimension; a rule weight calculation module, used to determine the judgment matrix of the importance between different data quality rules under each data quality dimension, and use the hierarchical analysis method to calculate the weight of each data quality rule; a rule score calculation module, used to calculate the score of each data quality rule according to the score calculation method of each data quality rule under each data quality dimension; a dimension score calculation module, used to multiply the score of each data quality rule by the sum of the weights of each data quality rule under each data quality dimension, as the score of each data quality dimension; a data quality score calculation module, used to multiply the score of each data quality dimension by the sum of the weights of each data quality dimension, to determine the data quality score of the enterprise data management platform as a whole.
[0008] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the data quality score assessment methods described above.
[0009] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described data quality score assessment methods.
[0010] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the data quality score assessment methods described above.
[0011] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0012] The present application provides a data quality score evaluation method, device, equipment, medium and product, which incorporates the weights of two levels, data quality dimension and data quality rule, and considers the connection between data quality rule and data quality dimension, so that the quality score calculation method is more in line with the definition process of data quality rule; the weights of two levels, data quality dimension and data quality rule, are determined by using hierarchical analysis method, which can flexibly show the difference in importance between data quality rules compared with the existing method of incorporating the score of a single rule into the data quality score according to a fixed ratio, and the hierarchical analysis method relies on calculating the consistency ratio to test the consistency of the results, which improves the reliability of the results and makes the data quality score evaluation process more accurate.
[0013] When adding new data quality rules, this application only needs to give the pairwise comparison values of the new data quality rules and other data quality rules. Through consistency verification, the weights of all data quality rules under the corresponding data quality dimension can be updated, which can more flexibly support the weight adjustment process brought about by the addition of new data quality rules. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0015] Figure 1 A flowchart of a data quality score evaluation method provided in one embodiment of the present application;
[0016] Figure 2 A brief flowchart of a data quality score evaluation method provided in another embodiment of the present application;
[0017] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0019] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0020] When using data quality scores to evaluate the data quality level in an enterprise data management platform, in order to overcome the problem that the weight division method of data quality rules is single and the difference in importance between data quality dimensions is ignored, in an exemplary embodiment, Figure 1 As shown, a data quality score evaluation method is provided, comprising the following steps 101 to 107. Among them:
[0021] Step 101: Define the data quality dimensions of the enterprise data management platform.
[0022] Step 102: Configure at least one data quality rule and a score calculation method for each data quality rule under each data quality dimension.
[0023] Step 103: Determine the judgment matrix of the importance between different data quality dimensions, and use the hierarchical analysis method to calculate the weight of each data quality dimension.
[0024] Step 104: Under each data quality dimension, determine the judgment matrix of the importance between different data quality rules, and use the hierarchical analysis method to calculate the weight of each data quality rule.
[0025] Step 105: Under each data quality dimension, according to the score calculation method of each data quality rule, calculate the score of each data quality rule.
[0026] Step 106: Under each data quality dimension, multiply the score of each data quality rule by the sum of the weights of the respective data quality rules to obtain the score of each data quality dimension.
[0027] Step 107: Multiply the score of each data quality dimension by the sum of the weights of the respective data quality dimensions to determine the overall data quality score of the enterprise data management platform.
[0028] Implement the above steps 101 to 107, incorporate the factors of the two levels of data quality dimensions and data quality rules into the data quality score evaluation method, and support users to first define the data quality dimensions, and then configure their own data quality rules under different data quality dimensions. At the data quality dimension level, the user fills in the judgment matrix of the importance between different dimensions, and uses the hierarchical analysis method to calculate the weights of different dimensions based on the judgment matrix; for each dimension, the hierarchical analysis method is also used to calculate the weights of each rule in the dimension. When calculating the data quality score of the data platform, first calculate the sum of the scores of each data quality rule multiplied by the rule weight in each dimension to obtain the scores of each dimension; then multiply the scores of each dimension by the weights corresponding to the dimensions to obtain the overall data quality score of the data platform.
[0029] In another exemplary embodiment of the present application, the data quality dimensions in the above step 101 are first defined to include: accuracy, completeness, consistency, immediacy, uniqueness and validity. Then, the relevant conceptual descriptions of these data quality dimensions are given so that corresponding data quality rules can be created under the correct dimensions later. For example: accuracy: whether the data correctly represents the "real" entity; completeness: whether all data is included; consistency: the degree of consistency between data; immediacy: whether the data is kept up to date; uniqueness: whether entities in the data set appear repeatedly; validity: the data value is consistent with the defined value domain.
[0030] In another exemplary embodiment of the present application, the definition of data quality rules is carried out around the specific data content, and the data quality rules of each database or data table under the same data quality dimension are not necessarily the same. Taking the user data table as an example, the data quality rules defined in the above step 102 can be: the data quality rule under accuracy is that the user login time is not earlier than the system online time; the data quality rule under integrity is that the id field cannot be empty; the data quality rule under consistency is that the name, age and year of birth of customers with the same id in different tables are consistent; the data quality rule under immediacy is whether the data is updated every day; the data quality rule under uniqueness is that the id field is unique; the data quality rule under validity is that the email field must contain the "@" symbol and a valid domain name, and the age field must be a positive integer between 0-120.
[0031] In another exemplary embodiment of the present application, after the above step 102, the method may further include: configuring rule operation parameters. For example, the rule operation parameters include: the rule operation parameter configured for the data quality rule that the id field cannot be empty is the number of data with an empty id field, and the rule operation parameter configured for the data quality rule that the age and birth year are consistent is the number of data with an age field that is not equal to the age calculated by subtracting the birth year from this year.
[0032] In another exemplary embodiment of the present application, the score calculation method of the data quality rule is:
[0033]
[0034] In the formula, F i is the score of data quality rule i, m is the number of data that does not meet the rule, and M is the total amount of data.
[0035] The following example illustrates the correspondence between data quality rules, data quality rules, and score calculation methods:
[0036] Data quality rule 1: The id field cannot be empty; rule running parameters of data quality rule 1: the number of data with an empty id field; score calculation method of data quality rule 1: score of data quality rule 1 = (1-number of data with an empty id field / total amount of data)*100%.
[0037] Data quality rule 2: Age and year of birth are consistent; Rule running parameters of data quality rule 2: The number of data whose age field is not equal to the age calculated by subtracting the year of birth from this year; Score calculation method of data quality rule 2: Score of data quality rule 2 = (1-the number of data whose age field is not equal to the age calculated by subtracting the year of birth from this year / total amount of data)*100%.
[0038] In another exemplary embodiment of the present application, the above step 104 of calculating the weight of each data quality rule can be replaced by the following steps 201 to 207:
[0039] Step 201: Use a scale of 1-9 to measure the importance between any two data quality rules to form a judgment matrix.
[0040] Step 202: Normalize each column in the judgment matrix to obtain a normalized judgment matrix.
[0041] Step 203: Add rows in the normalized judgment matrix to obtain the sum vector of all data quality rules.
[0042] Step 204: Normalize the sum vectors of all data quality rules to obtain weight vectors of all data quality rules.
[0043] Step 205: Perform consistency check on the normalized judgment matrix.
[0044] Step 206: If the consistency check passes, the weight vectors of all data quality rules are output to obtain the weight of each data quality rule.
[0045] Step 207: If the consistency check fails, return to step "using a scale of 1-9 to measure the importance between any two data quality rules to form a judgment matrix".
[0046] Compare the importance of different dimensions and different rules, and construct a judgment matrix as shown in Table 1 (taking dimensions as an example). In the judgment matrix, importance is generally measured by a scale of 1-9, where 1 means that the former and the latter are equally important, and 9 means that the former is more important than the latter. If the latter is more important than the former, fill in the reciprocal of 1-9. As shown in Table 1, accuracy is slightly more important than completeness, so fill in 3 in the first row and second column of the matrix. The judgment matrix only needs to fill in the value in the upper right corner, and the data in the lower left corner is the corresponding reciprocal, that is, a ji =1 / a ij . After the judgment matrix is filled in, normalize each column first, then add them row by row to get the sum vector, and normalize the sum vector to get the weight vector of each element. Calculate the characteristic roots and perform a consistency check on the judgment matrix. If the consistency check passes, the weight of each element is determined; if the consistency check fails, it is necessary to check the judgment matrix, modify the conflicting values, and recalculate the weights and perform a consistency check. In the specific implementation, a fixed script is written according to the calculation logic of the weight calculation and consistency check to directly obtain the weight value and consistency check result after the judgment matrix is filled in.
[0047] Table 1 Judgment Matrix
[0048] accuracy Completeness consistency Timeliness Uniqueness Effectiveness accuracy 1 3 1 5 5 2 Completeness 1 / 3 1 1 / 3 2 2 1 / 2 consistency 1 3 1 5 5 2 Timeliness 1 / 5 1 / 2 1 / 5 1 1 1 / 3 Uniqueness 1 / 5 1 / 2 1 / 5 1 1 1 / 3 Effectiveness 1 / 2 2 1 / 2 3 3 1
[0049] The implementation process of calculating the score of each data quality rule is the same as steps 201 to 207, and will not be repeated here.
[0050] The overall data quality score of the enterprise data management platform is used to measure the quality of all data in the data platform and monitor fluctuations in data quality.
[0051] The brief process of the data quality score evaluation method of this application is as follows Figure 2 As shown. Start → Define data quality dimensions → Define data quality rules and rule scoring formulas → Fill in the importance judgment matrix of rules and dimensions, and use the hierarchical analysis method to calculate the corresponding weights → Multiply the rule score by the corresponding weight and sum to get the corresponding dimension score → Multiply the dimension score by the corresponding weight and sum to get the total platform data quality score.
[0052] The beneficial effects of the present application are: first, the weights of the two levels of data quality dimensions and data quality rules are incorporated into the data quality score evaluation method, so that the calculation method of the quality score is more in line with the definition process of data quality rules. Second, the hierarchical analysis method is used to calculate the weights of data quality dimensions and rules. Users only need to define the dimensions and rules of data quality, and then compare the importance of the factors in the two levels of dimensions and rules to obtain the weights. The hierarchical analysis method is applied to the weight determination of the two levels of data quality dimensions and data quality rules, allowing users to quantify the weights of each factor by comparing the importance of different factors, making the weight calculation process simpler. At the same time, the hierarchical analysis method relies on calculating the consistency ratio to test the consistency of the results, which improves the reliability and accuracy of the results. When adding new rules, only the pairwise comparison values of the new rules and other rules need to be given, and the weights of all rules under the corresponding dimensions can be updated through consistency testing. In summary, the present application incorporates the two-layer factors of data quality dimensions and rules into the data quality score evaluation reference, and uses the hierarchical analysis method to quantify the indicator weights, so that the data quality score evaluation process is more objective and reliable, and can also more flexibly support the weight adjustment process brought about by the addition of new rules.
[0053] Based on the same inventive concept, the embodiment of the present application also provides a data quality score evaluation device for implementing the data quality score evaluation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more data quality score evaluation device embodiments provided below can refer to the limitations of the data quality score evaluation method above, and will not be repeated here.
[0054] In an exemplary embodiment, a data quality score evaluation device is provided, including: a dimension definition module, a rule definition module, a dimension weight calculation module, a rule weight calculation module, a rule score calculation module, a dimension score calculation module and a data quality score calculation module.
[0055] A dimension definition module is used to define the data quality dimensions of the enterprise data management platform; a rule definition module is used to configure at least one data quality rule and a score calculation method for each data quality rule under each data quality dimension; a dimension weight calculation module is used to determine the judgment matrix of the importance between different data quality dimensions and calculate the weight of each data quality dimension using the hierarchical analysis method; a rule weight calculation module is used to determine the judgment matrix of the importance between different data quality rules under each data quality dimension and calculate the weight of each data quality rule using the hierarchical analysis method; a rule score calculation module is used to calculate the score of each data quality rule according to the score calculation method of each data quality rule under each data quality dimension; a dimension score calculation module is used to multiply the score of each data quality rule by the sum of the weights of each data quality rule under each data quality dimension to obtain the score of each data quality dimension; a data quality score calculation module is used to multiply the score of each data quality dimension by the sum of the weights of each data quality dimension to determine the overall data quality score of the enterprise data management platform.
[0056] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the data quality score of the enterprise data management platform as a whole. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a data quality score evaluation method is implemented.
[0057] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.
[0058] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0059] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0060] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.
[0061] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0062] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.
[0063] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0064] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A data quality score evaluation method, characterized in that: include: Define the data quality dimensions of the enterprise data management platform; Configure at least one data quality rule and the score calculation method for each data quality rule under each data quality dimension; Determine the judgment matrix of the importance between different data quality dimensions and use the analytic hierarchy process to calculate the weight of each data quality dimension; Under each data quality dimension, determine the judgment matrix of the importance of different data quality rules, and use the hierarchical analysis method to calculate the weight of each data quality rule; Under each data quality dimension, the score of each data quality rule is calculated according to the score calculation method of each data quality rule; Under each data quality dimension, the score of each data quality rule is multiplied by the sum of the weights of the respective data quality rules to obtain the score of each data quality dimension; The score of each data quality dimension is multiplied by the sum of the weights of each data quality dimension to determine the overall data quality score of the enterprise data management platform.
2. The data quality score evaluation method according to claim 1, characterized in that: The data quality dimensions include: accuracy, completeness, consistency, timeliness, uniqueness and validity.
3. The data quality score evaluation method according to claim 2, characterized in that: The data quality rule under accuracy is that the user login time should not be earlier than the system online time; The data quality rule under integrity is that the id field cannot be empty; The data quality rule under consistency is that the name, age and birth year of customers with the same ID in different tables are consistent; The data quality rule under immediacy is whether the data is updated every day; The data quality rule under uniqueness is that the id field is unique; The data quality rules under validity are that the email field must contain the "@" symbol and a valid domain name, and the age field must be a positive integer between 0 and 120.
4. The data quality score evaluation method according to claim 1, characterized in that: Configure at least one data quality rule and the score calculation method for each data quality rule under each data quality dimension, and then include: Configure the rule operation parameters; the rule operation parameters include: the number of data with an empty id field and the number of data with an age field that is not equal to the age calculated by subtracting the year of birth from this year.
5. The data quality score evaluation method according to claim 1, characterized in that: The score for a data quality rule is calculated as follows: In the formula, F i is the score of data quality rule i, m is the number of data that does not meet the rule, and M is the total amount of data.
6. The data quality score evaluation method according to claim 1, characterized in that: Under each data quality dimension, determine the judgment matrix of the importance of different data quality rules, and use the hierarchical analysis method to calculate the weight of each data quality rule, including: Use a scale of 1-9 to measure the importance between any two data quality rules to form a judgment matrix; Normalize each column in the judgment matrix to obtain a normalized judgment matrix; The sum vector of all data quality rules is obtained by adding rows in the normalized judgment matrix; Normalize the sum vectors of all data quality rules to obtain the weight vectors of all data quality rules; Perform consistency check on the normalized judgment matrix; If the consistency check passes, the weight vectors of all data quality rules are output to obtain the weight of each data quality rule; If the consistency check fails, return to step "Use a scale of 1-9 to measure the importance between any two data quality rules to form a judgment matrix".
7. A data quality score evaluation device, characterized in that: The data quality score evaluation includes: Dimension definition module, used to define the data quality dimensions of the enterprise data management platform; A rule definition module, used to configure at least one data quality rule and a score calculation method for each data quality rule under each data quality dimension; Dimension weight calculation module, which is used to determine the judgment matrix of the importance between different data quality dimensions and calculate the weight of each data quality dimension using the hierarchical analysis method; The rule weight calculation module is used to determine the judgment matrix of the importance of different data quality rules under each data quality dimension, and calculate the weight of each data quality rule using the hierarchical analysis method; A rule score calculation module is used to calculate the score of each data quality rule under each data quality dimension according to the score calculation method of each data quality rule; A dimension score calculation module, used for multiplying the score of each data quality rule by the sum of the weights of the respective data quality rules under each data quality dimension, as the score of each data quality dimension; The data quality score calculation module is used to multiply the score of each data quality dimension by the sum of the weights of the respective data quality dimensions to determine the overall data quality score of the enterprise data management platform.
8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data quality score assessment method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data quality score evaluation method according to any one of claims 1 to 6 is implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the data quality score evaluation method according to any one of claims 1 to 6 is implemented.