Maturity assessment method, device, system, computer device and storage medium

Abstract

The application relates to a maturity evaluation method, device, system, computer equipment and storage medium. The method comprises the following steps: performing data governance processing on each to-be-governed data according to at least one to-be-evaluated data governance strategy to obtain a plurality of first governed data; performing data governance processing on each to-be-governed data according to a corresponding control data governance strategy of each to-be-evaluated data governance strategy to obtain a plurality of second governed data; and performing maturity evaluation on each to-be-evaluated data governance strategy according to each first governed data and each second governed data, and determining a maturity evaluation result of each to-be-evaluated data governance strategy. The above method can use the corresponding control data governance strategy of each to-be-evaluated data governance strategy as a reference strategy to perform maturity evaluation on each to-be-evaluated data governance strategy, so that the accuracy of the finally obtained maturity evaluation result of each to-be-evaluated data governance strategy is higher.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a maturity assessment method, apparatus, system, computer equipment, and storage medium. Background Art

[0002] With the development of big data technology, more and more business data is generated in various fields. During the data application and data management stages, data governance of business data is often required. Therefore, it is particularly important to evaluate the maturity of data governance strategies.

[0003] In related technologies, a qualitative analysis is performed on the maturity of a data governance policy to be evaluated to determine a maturity evaluation result of the data governance policy.

[0004] However, in the process of maturity assessment of data governance strategies in related technologies, there is a problem of inaccurate assessment results. Summary of the Invention

[0005] Based on this, it is necessary to provide a maturity assessment method, device, system, computer equipment and storage medium to address the above technical problems, which can improve the accuracy of assessment results in the process of maturity assessment of data governance strategies.

[0006] In a first aspect, an embodiment of the present application provides a maturity assessment method, the method comprising:

[0007] Performing data governance processing on each data to be governed according to at least one data governance policy to be evaluated to obtain a plurality of first governed data; and performing data governance processing on each data to be governed according to a reference data governance policy corresponding to each data governance policy to be evaluated to obtain a plurality of second governed data; the reference data governance policy is a pre-constructed data governance policy that is similar to or complementary to the corresponding data governance policy to be evaluated;

[0008] Based on each first-governed data and each second-governed data, a maturity assessment is performed on each data governance strategy to be assessed, and a maturity assessment result of each data governance strategy to be assessed is determined.

[0009] In one embodiment, a maturity assessment is performed on each data governance policy to be assessed based on each first-governed data and each second-governed data, and a maturity assessment result of each data governance policy to be assessed is determined, including:

[0010] Determine, based on the actual data type of each to-be-governed data, a processing strategy corresponding to the error rate between each first-governed data and each second-governed data;

[0011] Determining an error rate between each first treated data and each second treated data according to each processing strategy;

[0012] The maturity evaluation result of each data governance strategy to be evaluated is determined according to the error rate between each first post-governance data and each second post-governance data.

[0013] In one of the embodiments, the maturity evaluation result of each data governance strategy to be evaluated is determined according to the error rate between each first post-governance data and each second post-governance data, including:

[0014] For any first post-governance data, if the error rate between the first post-governance data and the corresponding second post-governance data is greater than a preset threshold, it is determined that the maturity evaluation result of the data governance strategy to be evaluated corresponding to the first post-governance data is unreliable;

[0015] If the error rate between the first post-governance data and the corresponding second post-governance data is less than or equal to the preset threshold, it is determined that the maturity evaluation result of the data governance strategy to be evaluated is reliable.

[0016] In one of the embodiments, the construction process of each data governance strategy to be evaluated includes:

[0017] According to the governance requirement information corresponding to different standard data types, different data governance indicators, and different data governance targets, a mapping relationship is constructed;

[0018] According to the actual data types of each data to be governed, the data governance targets corresponding to the standard data types matched with each actual data type in the mapping relationship are searched to serve as a plurality of first data governance targets;

[0019] According to at least one first data governance indicator corresponding to each actual data type and each first data governance target, each data governance strategy to be evaluated is constructed.

[0020] In one of the embodiments, the construction process of each control data governance strategy includes:

[0021] According to the preset indicator set and the mapping relationship, at least one second data governance indicator corresponding to each first data governance indicator is determined;

[0022] According to each first data governance indicator, each second data governance indicator, and at least one second data governance target, each control data governance strategy is constructed.

[0023] In one of the embodiments, the second data governance indicator includes a first sub-indicator; according to the preset indicator set and the mapping relationship, at least one second data governance indicator corresponding to each first data governance indicator includes:

[0024] A plurality of reference data governance targets are obtained;

[0025] Searching for data governance indicators corresponding to data governance objectives that match each reference data governance objective in the mapping relationship as multiple candidate data governance indicators;

[0026] At least one candidate data governance indicator in the indicator set is selected from the candidate data governance indicators as each first sub-indicator.

[0027] In one embodiment, a plurality of reference data governance objectives are obtained, including:

[0028] Performing similarity processing on the multiple original data governance objectives and each first data governance objective to determine a similarity value between each original data governance objective and each first data governance objective;

[0029] Multiple reference data governance objectives with similarity values ​​greater than a preset similarity threshold are selected from each original data governance objective.

[0030] In one embodiment, the second data governance indicator includes a second sub-indicator; determining at least one second data governance indicator corresponding to each first data governance indicator based on a preset indicator set and mapping relationship includes:

[0031] Acquire multiple third-party data governance objectives;

[0032] Searching for data governance indicators corresponding to data governance objectives that match each third data governance objective in the mapping relationship as multiple candidate data governance indicators;

[0033] At least one candidate data governance indicator in the indicator set is selected from the candidate data governance indicators as each second sub-indicator.

[0034] In one embodiment, obtaining a plurality of third data governance objectives includes:

[0035] Obtain multiple original data governance objectives; and divide historical data governance issues into multiple problem sets; each original data governance objective covers at least one problem set;

[0036] For any original data governance objective, if there is no overlap between at least one problem set covered by the original data governance objective and at least one problem set covered by each first data governance objective, the original data governance objective is determined as the third data governance objective.

[0037] In a second aspect, an embodiment of the present application provides a maturity assessment device, the device comprising:

[0038] a processing module configured to perform data governance processing on each data to be governed according to at least one data governance policy to be evaluated, to obtain a plurality of first governed data; and to perform data governance processing on each data to be governed according to a reference data governance policy corresponding to each data governance policy to be evaluated, to obtain a plurality of second governed data; the reference data governance policy being a pre-constructed data governance policy that is similar to or complementary to the corresponding data governance policy to be evaluated;

[0039] The maturity assessment module is used to perform maturity assessment on each data governance strategy to be assessed based on each first-governed data and each second-governed data, and determine the maturity assessment result of each data governance strategy to be assessed.

[0040] In a third aspect, an embodiment of the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method of any embodiment of the first aspect are implemented.

[0041] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method of any embodiment of the first aspect above are implemented.

[0042] In a fifth aspect, an embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method of any embodiment of the first aspect above.

[0043] The maturity assessment method, apparatus, system, computer equipment and storage medium provided in the embodiments of the present application include: performing data governance processing on each data to be governed according to at least one data governance policy to be assessed to obtain multiple first governed data; and performing data governance processing on each data to be governed according to a reference data governance policy corresponding to each data governance policy to be assessed to obtain multiple second governed data, and performing maturity assessment on each data governance policy to be assessed based on each first governed data and each second governed data to determine a maturity assessment result of each data governance policy to be assessed, wherein the reference data governance policy is a pre-constructed data governance policy that is similar to or complementary to the corresponding data governance policy to be assessed. The above method does not require qualitative analysis of each data governance strategy to be evaluated. The control data governance strategy corresponding to each data governance strategy to be evaluated can be used as a reference strategy to conduct maturity assessment of each data governance strategy to be evaluated, so that the maturity assessment results of each data governance strategy to be evaluated obtained in the end are more accurate. On this basis, accurate reference information can be provided for the subsequent reliable use of the data governance strategy to be evaluated. At the same time, the above method does not require the participation of complex algorithms, thereby reducing the amount of data required to be processed in the data governance strategy maturity assessment process, thereby reducing the complexity of the maturity assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 An application environment diagram of a maturity assessment method in one embodiment;

[0045] Figure 2 1 is a flow chart of a maturity assessment method according to an embodiment;

[0046] Figure 3 is a flow chart of a maturity assessment method according to another embodiment;

[0047] Figure 4 is a flow chart of a maturity assessment method according to another embodiment;

[0048] Figure 5 is a flow chart of a maturity assessment method according to another embodiment;

[0049] Figure 6 is a flow chart of a maturity assessment method according to another embodiment;

[0050] Figure 7 is a flow chart of a maturity assessment method according to another embodiment;

[0051] Figure 8 is a flow chart of a maturity assessment method according to another embodiment;

[0052] Figure 9is a flow chart of a maturity assessment method according to another embodiment;

[0053] Figure 10 is a structural block diagram of a maturity assessment device in one embodiment;

[0054] Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0056] In the field of big data, data governance of business data is often required in the stages of data application and data management. Therefore, it is particularly important to conduct maturity assessment of data governance strategies. In the related art, the maturity of the data governance strategy to be assessed is qualitatively analyzed to determine the maturity assessment result of the data governance strategy. However, in the process of performing maturity assessment of data governance strategies in the related art, there is a problem of inaccurate assessment results. Based on this, an embodiment of the present application provides a maturity assessment method, which can improve the accuracy of the assessment results in the process of performing maturity assessment of data governance strategies.

[0057] The maturity assessment method provided in the embodiment of the present application can be applied to Figure 1 The maturity assessment system shown includes computer equipment and data management equipment within each enterprise. Optionally, the computer equipment can be a server, personal computer, laptop computer, tablet computer, smart phone, smart bracelet or other electronic device with data processing function; the data management equipment can be a computer equipment, server, database system or other device with data storage function; wherein, this embodiment does not limit the specific form of the computer equipment and the data management equipment. In the embodiment of the present application, the data management equipment can manage customer relationship, financial, market research, social and other types of data generated within the corresponding enterprise. Optionally, there can be a communication connection between the computer equipment and the data management equipment, and the communication method can be Wi-Fi, mobile network or Bluetooth connection, etc. wherein, Figure 1 The maturity assessment system is illustrated by taking a personal computer as the computer device and a database system as the data management device. The following embodiment describes the specific process of the maturity assessment method by taking a computer device as the execution subject of the maturity assessment method.

[0058] like Figure 2FIG. 1 is a flow chart of a maturity assessment method provided in an embodiment of the present application, which can be implemented by the following steps:

[0059] S100: Perform data governance processing on each data to be governed according to at least one data governance policy to be evaluated, obtaining a plurality of first governed data; and perform data governance processing on each data to be governed according to a reference data governance policy corresponding to each data governance policy to be evaluated, obtaining a plurality of second governed data. The reference data governance policy is a pre-established data governance policy that is similar to or complementary to the corresponding data governance policy to be evaluated.

[0060] Specifically, the computer device can obtain at least one data governance policy to be evaluated and a corresponding control data governance policy for each data governance policy to be evaluated, and invoke a data collection tool to collect each data to be governed from the data management device to improve the collection efficiency and accuracy of each data to be governed. Optionally, the aforementioned data to be governed can be various types of data in any field, such as communications, the internet, healthcare, computers, artificial intelligence, etc. The data management device can centrally manage different types of data to be evaluated to facilitate subsequent centralized and comprehensive collection of data to be governed, thereby improving the collection efficiency of data to be governed.

[0061] Furthermore, for any data governance policy to be evaluated, data governance processing can be performed on each data to be governed according to the data governance policy to be evaluated, and for any comparison data governance policy, data governance processing can be performed on each data to be governed according to the comparison data governance policy.

[0062] Each data governance policy to be evaluated may include at least one first data governance indicator, and correspondingly, the reference data governance policy corresponding to the data governance policy to be evaluated may include each first data governance indicator in the data governance policy to be evaluated and at least one other data governance indicator. In embodiments of the present application, each other data governance indicator is similar to each first data governance indicator, or complements each other to form a comprehensive data governance indicator set.

[0063] S200: Perform maturity assessment on each data governance strategy to be assessed based on each first-governed data and each second-governed data, and determine a maturity assessment result of each data governance strategy to be assessed.

[0064] One implementation method may be to pre-train an algorithm model, and then input each first-governed data and each second-governed data into the algorithm model. The algorithm model performs a maturity assessment on each data governance strategy to be assessed and then outputs the maturity assessment results of each data governance strategy to be assessed.

[0065] Another implementation method may be to perform comparison processing, analysis processing, matching processing, and / or arithmetic processing on each first and second data to complete the maturity assessment of each data governance policy to be assessed, and determine the maturity assessment result of each data governance policy to be assessed based on the processing results. Optionally, the arithmetic processing may include at least one of addition, subtraction, multiplication, division, exponential operation, logarithmic operation, etc.

[0066] The technical solution in the embodiment of the present application performs data governance processing on each data to be governed according to at least one data governance strategy to be evaluated to obtain multiple first governed data; and performs data governance processing on each data to be governed according to the reference data governance strategy corresponding to each data governance strategy to be evaluated to obtain multiple second governed data, and performs maturity assessment on each data governance strategy to be evaluated based on each first governed data and each second governed data, to determine the maturity assessment result of each data governance strategy to be evaluated, wherein the reference data governance strategy is a pre-constructed data governance strategy that is similar to or complementary to the corresponding data governance strategy to be evaluated; the above method does not require qualitative analysis of each data governance strategy to be evaluated, and can use the reference data governance strategy corresponding to each data governance strategy to be evaluated as a reference strategy to perform maturity assessment on each data governance strategy to be evaluated, so that the maturity assessment result of each data governance strategy to be evaluated finally obtained is more accurate, and on this basis, accurate reference information can be provided for the subsequent reliable use of the data governance strategy to be evaluated; at the same time, the above method does not require the participation of complex algorithms, thereby reducing the amount of data required to be processed in the data governance strategy maturity assessment process, thereby reducing the complexity of the maturity assessment.

[0067] The following describes the process of constructing each data governance strategy to be evaluated. In one embodiment, before executing the steps in S100 above, Figure 3 As shown, the above method may further include the following steps:

[0068] S300. Construct a mapping relationship based on the governance requirement information corresponding to different standard data types, different data governance indicators, and different data governance goals.

[0069] Specifically, different standard data types can be analyzed and matched to obtain governance requirement information corresponding to the different standard data types. Optionally, the different standard data types may include structured data, semi-structured data, and unstructured data. Optionally, the semi-structured data may be JSON, XML, YAML, or other data; and the unstructured data may be text, images, audio, video, or other data.

[0070] It should be noted here that the governance requirement information corresponding to structured data may include ideal values ​​corresponding to data consistency, data integrity and data accuracy; the governance requirement information corresponding to semi-structured data may include ideal values ​​corresponding to data flexibility, data applicability and data scalability; the governance requirement information corresponding to unstructured data may include ideal values ​​corresponding to data validity and data availability.

[0071] In actual applications, different standard data types correspond to different governance requirement information. Specifically, the computer device can construct the corresponding relationship between the governance requirement information corresponding to different standard data types, different data governance indicators, and different data governance objectives according to a preset mapping method, and generate the mapping relationship between the governance requirement information, data governance indicators, and data governance objectives.

[0072] In addition, the computer device can also pre-train an algorithm model, and then input the governance requirement information corresponding to different standard data types, different data governance indicators and different data governance goals into the algorithm model. The algorithm model outputs the mapping relationship between the governance requirement information corresponding to different standard data types, different data governance indicators and different data governance goals.

[0073] Among them, data governance goals correspond one-to-one to corresponding data governance indicators, and data governance goals can be understood as target values ​​of corresponding data governance indicators.

[0074] S400. According to the actual data types of the data to be managed, search in the mapping relationship for data governance targets corresponding to the standard data types that match the actual data types as multiple first data governance targets.

[0075] Specifically, the computer device can search for data governance targets corresponding to standard data types that match each actual data type in the mapping relationship as multiple first data governance targets based on the actual data type of each to-be-governed data.

[0076] In the embodiments of the present application, the actual data type of the data to be governed can be structured data, semi-structured data, or unstructured data. The data to be governed can be classified, and the characteristics of each data to be governed can be analyzed in combination with industry standards and business needs to determine the actual data type of each data to be governed.

[0077] S500: Construct each data governance strategy to be evaluated based on at least one first data governance indicator and each first data governance goal corresponding to each actual data type.

[0078] In actual application, the computer device can pre-train a strategy construction model, and then input the at least one first data governance indicator corresponding to each actual data type and each first data governance target into the strategy construction model, and the strategy construction model outputs each to-be-evaluated data governance strategy. Optionally, the strategy construction model can be implemented by at least one of a convolutional neural network model, a fully connected neural network model, a recurrent neural network model, a long short-term memory neural network model, and a residual neural network model.

[0079] Meanwhile, the computer device can also call a strategy construction tool or construct each to-be-evaluated data governance strategy according to the at least one first data governance indicator corresponding to each actual data type and each first data governance target in a preset strategy construction manner.

[0080] It should be noted that each first data governance indicator is a data governance indicator in a preset indicator set, and the indicator set includes a plurality of data governance indicators. In the embodiments of the present application, the correspondence between the actual data type and the first data governance indicator is described as follows: if the actual data type is structured data, the corresponding first data governance indicator can include data integrity, data consistency and data accuracy; if the actual data type is semi-structured data, the corresponding first data governance indicator can include data flexibility, data applicability and data scalability; and if the actual data type is unstructured data, the corresponding first data governance indicator can include data validity and data availability.

[0081] For example, if each first data governance indicator is A, B and C, A represents data integrity, B represents data consistency, and C represents data accuracy, the computer device can arrange and combine A, B and C to obtain a plurality of sets of data governance indicators, and then construct a plurality of to-be-evaluated data governance strategies according to each set of data governance indicators and each first data governance target. Each set of data governance indicators can be a combination of A, B, C, AB, AC, BC or ABC.

[0082] It should be noted here that the first data governance objective corresponding to a single data governance indicator A can be to ensure that all governed data are not lost or damaged during the data governance process; the first data governance objective corresponding to a single data governance indicator B can be to ensure that all governed data are synchronized and coordinated across multiple systems during the data governance process; the first data governance objective corresponding to a single data governance indicator C can be to ensure that all governed data are error-free or anomaly-free during the data governance process; the first data governance objective corresponding to a single data governance indicator AB can be to ensure that all governed data are not lost and remain consistent across multiple systems during the data governance process; the first data governance objective corresponding to a single data governance indicator AC can be to ensure that all governed data are not lost and must maintain high accuracy during the data governance process, which is applicable to scenarios requiring no data loss and high accuracy; the first data governance objective corresponding to a single data governance indicator BC can be to ensure that all governed data are synchronized and highly accurate across multiple systems during the data governance process, which is applicable to scenarios requiring data synchronization across multiple systems and requiring high accuracy; the first data governance objective corresponding to a single data governance indicator ABC can be to ensure that all governed data reach the optimal state in all dimensions during the data governance process, which is applicable to business scenarios with extremely high data quality requirements.

[0083] The technical solution in the embodiment of the present application constructs a mapping relationship based on the governance requirement information, different data governance indicators and different data governance goals corresponding to different standard data types, and searches for data governance goals corresponding to standard data types that match each actual data type in the mapping relationship as multiple first data governance goals according to the actual data type of each data to be governed, and constructs each data governance strategy to be evaluated based on at least one first data governance indicator and each first data governance goal corresponding to each actual data type; the above method can quickly determine the corresponding data governance goal through the pre-constructed mapping relationship to construct the data governance strategy, thereby improving the construction speed and efficiency of the data governance strategy; at the same time, the above method can flexibly match multiple data governance features to construct the data governance strategy, thereby improving the flexibility of constructing the data governance strategy, and can construct a variety of different data governance strategies, so that the corresponding data can be efficiently and comprehensively governed based on these data governance strategies in subsequent applications.

[0084] The following describes the construction process of each control data governance strategy. In one embodiment, before executing the steps in S100 above, Figure 4 As shown, the above method may further include the following steps:

[0085] S600: Determine at least one second data governance indicator corresponding to each first data governance indicator according to a preset indicator set and mapping relationship.

[0086] In actual applications, computer equipment can perform search processing, matching processing, screening processing and / or transformation processing according to a preset indicator set and mapping relationship, and determine at least one second data governance indicator corresponding to each first data governance indicator based on the processing operation results.

[0087] S700: Constructing respective comparative data governance policies based on respective first data governance indicators, respective second data governance indicators, and at least one second data governance objective.

[0088] Specifically, for any data governance policy to be evaluated, the computer device can construct a control data governance policy corresponding to the data governance policy to be evaluated based on each first data governance indicator, at least one second data governance indicator and the corresponding second data governance goal in the data governance policy to be evaluated.

[0089] The technical solution in the embodiment of the present application determines at least one second data governance indicator corresponding to each first data governance indicator based on a preset indicator set and mapping relationship, and constructs each comparison data governance strategy based on each first data governance indicator, each second data governance indicator and at least one second data governance goal; the above method can construct a comparison data governance strategy corresponding to each data governance strategy to be evaluated, which can serve as a reference strategy for the subsequent maturity evaluation of each data governance strategy to be evaluated, thereby facilitating the accurate evaluation of the maturity of each data governance strategy to be evaluated.

[0090] In one embodiment, the second data governance indicator includes a first sub-indicator; Figure 5 As shown, the step of determining at least one second data governance indicator corresponding to each first data governance indicator according to the preset indicator set and mapping relationship in S600 can be implemented in the following manner:

[0091] S610. Obtain multiple reference data governance objectives.

[0092] Specifically, the computer device can arbitrarily obtain multiple data governance goals as multiple reference data governance goals.

[0093] In one embodiment, if Figure 6 As shown, the step of obtaining multiple reference data governance targets in S610 may include:

[0094] S611: Perform similarity processing based on multiple original data governance objectives and each first data governance objective to determine a similarity value between each original data governance objective and each first data governance objective.

[0095] In practical applications, the computer device can calculate the characteristic vector of each original data governance target and the characteristic vector of each first data governance target, and use a similarity method to calculate the similarity between the characteristic vector of each original data governance target and the characteristic vector of each first data governance target to complete the similarity processing and obtain the similarity value between each original data governance target and each first data governance target, so as to provide assistance for the subsequent accurate acquisition of multiple reference data governance targets closely related to each original data governance target. Optionally, the above-mentioned similarity method can be a Euclidean distance method, a Manhattan distance method, etc., but in the embodiment of the present application, the above-mentioned similarity method can be a cosine similarity method.

[0096] It should be noted here that the characteristic vector of the data governance goal may include multi-dimensional characteristics related to the data governance goal, such as the nature of the data governance goal, applicable scenarios and governance effects.

[0097] S612: Filter multiple reference data governance objectives whose similarity values ​​are greater than a preset similarity threshold from the original data governance objectives.

[0098] Specifically, the computer device can compare the similarity values ​​and similarity thresholds between each original data governance target and each first data governance target, and based on the comparison results, select multiple reference data governance targets from each original data governance target whose similarity values ​​are greater than a preset similarity threshold, that is, select multiple reference data governance targets with high similarity to each original data governance target. Optionally, the above-mentioned similarity threshold can be user-defined or determined based on historical experience, which is not limited in this embodiment of the present application.

[0099] S620: Search the mapping relationships for data governance indicators corresponding to data governance objectives that match the reference data governance objectives, as multiple candidate data governance indicators.

[0100] Furthermore, data governance indicators corresponding to data governance objectives that match each reference data governance objective can be searched in the mapping relationship as multiple candidate data governance indicators. It should be noted that some of the multiple candidate data governance indicators can be valid data governance indicators, while some can be invalid data governance indicators.

[0101] S630: Filter at least one candidate data governance indicator in the indicator set from the candidate data governance indicators as each first sub-indicator.

[0102] Specifically, the computer device can screen at least one candidate data governance indicator in the indicator set from each candidate data governance indicator as each first sub-indicator. It should be noted that the at least one candidate data governance indicator screened is the same as part of the data governance indicators in the indicator set.

[0103] It should be noted that each first sub-indicator is similar to each first data governance indicator, so that in the data governance strategy construction process, each first sub-indicator can be combined with each first data governance indicator to effectively supplement or enhance each first data governance indicator, thereby improving the effectiveness, reliability or accuracy of the constructed corresponding data governance strategy, and improving the governance effect of the constructed data governance strategy.

[0104] The technical solution in the embodiment of the application obtains a plurality of reference data governance targets, finds data governance indicators corresponding to data governance targets matched with each reference data governance target in the mapping relationship as a plurality of candidate data governance indicators, and screens at least one candidate data governance indicator in the indicator set from each candidate data governance indicator as each first sub-indicator. The above method can screen a plurality of corresponding effective data governance indicators according to a plurality of reference data governance targets to prepare for subsequent construction of a contrast data governance strategy, and the processing process is relatively simple and does not need complex algorithms to participate, thereby being able to speed up the speed of obtaining the first sub-indicator and improve the efficiency of obtaining the first sub-indicator.

[0105] In an embodiment, the second data governance indicator includes a second sub-indicator; as Figure 7 As shown in the above S600, the step of determining at least one second data governance indicator corresponding to each first data governance indicator according to the preset indicator set and the mapping relationship can be implemented by the following way:

[0106] S640, obtaining a plurality of third data governance targets.

[0107] Specifically, the computer device can obtain a plurality of data governance targets as a plurality of third data governance targets at will. The plurality of third data governance targets are different from the plurality of reference data governance targets.

[0108] In an embodiment, as Figure 8 As shown in the above S640, the step of obtaining a plurality of third data governance targets can include:

[0109] S641, obtaining a plurality of original data governance targets; and dividing historical data governance problems to obtain a plurality of problem sets; each original data governance target covers at least one problem set.

[0110] The historical data governance issues may include multiple data governance issues existing in multiple traditional data governance strategies collected during a historical period. Specifically, the computer device may classify each of the historical data governance issues to obtain multiple problem sets of different types. Furthermore, each problem set may cover specific governance requirement information.

[0111] S642. For any original data governance objective, if there is no overlap between at least one problem set covered by the original data governance objective and at least one problem set covered by each first data governance objective, the original data governance objective is determined as the third data governance objective.

[0112] The aforementioned third data governance objectives may be complementary to the first data governance objectives. Optionally, corresponding second sub-indicators may be determined based on the third data governance objectives, and corresponding comparison data governance strategies may be constructed based on the second sub-indicators. This allows the constructed comparison data governance strategies to comprehensively address various potential data governance issues, effectively address omissions and deficiencies that may exist in traditional data governance strategies, and make the constructed comparison data governance strategies more systematic and comprehensive.

[0113] In actual applications, for any original data governance goal, the computer device can call a data analysis tool to evaluate the original data governance goal and each first data governance goal to determine whether there is overlap between at least one problem set covered by the original data governance goal and at least one problem set covered by each first data governance goal. If so, it is determined that there is overlap between the at least one problem set covered by the original data governance goal and at least one problem set covered by each first data governance goal. Otherwise, it is determined that there is no overlap between the at least one problem set covered by the original data governance goal and at least one problem set covered by each first data governance goal. Evaluating the original data governance goal and each first data governance goal refers to the process of comparing or contrasting the original data governance goal with each first data governance goal.

[0114] Furthermore, when it is determined that there is no overlap between at least one problem set covered by the original data governance goal and at least one problem set covered by each first data governance goal, the original data governance goal can be determined as a third data governance goal, so that the third data governance goal can effectively fill the gaps in the first data governance goal, making the determined data governance strategy more rich and comprehensive, thereby avoiding duplicate governance of the determined data governance strategy. In the embodiment of the present application, the introduction of complementary data governance goals not only helps to systematically solve data governance problems, but also forms a good synergistic effect between different data governance strategies, so that in actual operation, the data governance strategy constructed based on the third data governance goal can achieve higher governance efficiency and better governance effect.

[0115] S650: Search the mapping relationship for data governance indicators corresponding to data governance objectives that match each third data governance objective, as multiple candidate data governance indicators.

[0116] Furthermore, data governance indicators corresponding to data governance objectives that match each third data governance objective can be searched in the mapping relationship to serve as multiple candidate data governance indicators. Since the multiple reference data governance objectives and the multiple third data governance objectives are different, the multiple candidate data governance indicators here are naturally different from the multiple candidate data governance indicators in S620 above.

[0117] S660: Filter at least one candidate data governance indicator in the indicator set from the candidate data governance indicators as each second sub-indicator.

[0118] It should be noted that the implementation of step S660 is similar to that of step S630, and this embodiment of the present application does not limit this. In addition, the at least one second data governance indicator can be at least one sub-indicator of each of the first sub-indicator and the second sub-indicator.

[0119] For example, if the first data governance indicators are A, B, and C, the first sub-indicators are D, E, and F, and the second sub-indicators are G, H, and I, the computer device may permutate and combine the first data governance indicators with the first sub-indicators and the second sub-indicators to obtain multiple sets of reference data governance indicator sets, and then construct multiple reference data governance policies based on each set of reference data governance indicator sets. Each set of reference data governance indicator sets may include a permutation and combination of A, B, C, and at least one of the first sub-indicators and the second sub-indicators (i.e., D, E, F, G, H, and I).

[0120] The technical solution in the embodiment of the present application obtains multiple third data governance objectives, searches for data governance indicators corresponding to data governance objectives that match each third data governance objective in the mapping relationship as multiple candidate data governance indicators, and selects at least one candidate data governance indicator in the indicator set from each candidate data governance indicator as each second sub-indicator; the above method can select multiple corresponding valid data governance indicators based on multiple third data governance objectives to prepare for the subsequent construction of a control data governance strategy, and the processing process is relatively simple and does not require the participation of complex algorithms, thereby speeding up the acquisition of the second sub-indicators and improving the efficiency of obtaining the second sub-indicators.

[0121] The following describes the process of performing maturity evaluation on each data governance strategy to be evaluated based on each first governance data and each second governance data. Figure 9As shown, the steps in S200 above can be implemented in the following ways:

[0122] S210: Determine a processing strategy corresponding to the error rate between each first-governed data and each second-governed data according to the actual data type of each data to be governed.

[0123] It should be noted here that the above processing strategy can be understood as a specific method or process for calculating the error rate between each first treated data and each second treated data.

[0124] The actual data types of the data to be governed are different, and correspondingly, the processing strategies corresponding to the error rates between the first governed data and the second governed data are different.

[0125] In actual applications, the computer device can search the association table between different data types and different processing strategies for the actual data type of each to-be-governed data, then obtain the processing strategy corresponding to the data type that matches each actual data type in the association table, and determine the processing strategy corresponding to the error rate between each first-governed data and each second-governed data. In the embodiment of the present application, the processing strategies corresponding to different actual data types are described as follows:

[0126] a. If the actual data type is structured data, the error rate between the first processed data and the second processed data can be expressed as:

[0127] (1)

[0128] in, 1 represents the data after the first governance. Indicates the data after the second governance.

[0129] b. If the actual data type is semi-structured data, the error rate between the first processed data and the second processed data can be equal to the structural error rate between the first processed data and the second processed data. , field error rate and The result of the arithmetic operation. Optionally, the arithmetic operation can be implemented by at least one of addition, subtraction, logarithm, exponential, multiplication, etc., but in the embodiment of the present application, the arithmetic operation can be a weighted summation process. The formulas can be expressed as:

[0130] (2)

[0131] (3)

[0132] (4)

[0133] wherein, denotes the hierarchical structure in the first post-governed data, denotes the hierarchical structure in the second post-governed data, denotes the field value in the first post-governed data, denotes the field value in the second post-governed data, denotes the number of missing fields existing between the first post-governed data and the second post-governed data, denotes the total number of fields in the first post-governed data and the second post-governed data. Optionally, it can reflect whether there is a field omission or redundancy in the data governance process of the corresponding data to be governed.

[0134] It should be noted that the first post-governed data and the second post-governed data can be parsed and processed to obtain the hierarchical structure and the field value in the first post-governed data, and the hierarchical structure and the field in the second post-governed data. Among them, if there is a field missing, a different nested level or a change in the key-value pair between the hierarchical structures in the first post-governed data and the second post-governed data, the structural error rate between the first post-governed data and the second post-governed data is large, otherwise, the structural error rate is small; at the same time, if there is a large difference between the field values in the first post-governed data and the second post-governed data, the field error rate between the first post-governed data and the second post-governed data is large, otherwise, the field error rate is small.

[0135] c、(1) If the actual data type is unstructured data and the unstructured data is text data, the error rate between the first post-governed data and the second post-governed data can be equal to the text error rate and the keyword error rate arithmetic operation processing result; wherein the text error rate can be represented by the following formula (5) or (6), and the keyword error rate can be represented by formula (7) as:

[0136] (5)

[0137] (6)

[0138] (7)

[0139] wherein, denotes the length of the first post-governed data, denotes the length of the second post-governed data, denotes the edit distance between the first post-governed data and the second post-governed data, Represents the vocabulary or character set in the first processed data, Represents the vocabulary or character set in the second processed data, Indicates the total number of keywords that were not extracted or incorrectly extracted from the first and second processed data. The total number of correct keywords extracted from the first processed data and the second processed data is represented by . Optionally, the edit distance between the first processed data and the second processed data can be calculated using, but is not limited to, an edit distance method or a Jaccard similarity coefficient.

[0140] (2) If the unstructured data is image data, the error rate between the first processed data and the second processed data can be equal to the image error rate and image feature error rate The result of arithmetic operation; among them, the image error rate This can be achieved using mean square error, image error rate and image feature error rate They can be expressed by formulas (8) and (9) respectively as:

[0141] (8)

[0142] (9)

[0143] in, Indicates the total number of pixels of the first processed data or the second processed data (the total number of pixels of the first processed data is equal to the total number of pixels of the second processed data), Indicates the first governance data The pixel value of pixels, Indicates the second post-governance data The pixel value of pixels; Indicates the number of unmatched feature points between the first processed data and the second processed data, Represents the total number of feature points in the first processed data and the second processed data. Optionally, a feature extraction algorithm (such as edge detection method, object recognition method, etc.) can be used to extract features from the first processed data and the second processed data to obtain feature points of the first processed data and feature points of the second processed data, and then a feature point matching algorithm (such as SIFT method, ORB method, etc.) can be used to calculate the matching degree of the feature points of the first processed data and the feature points of the second processed data to obtain .

[0144] (3) If the unstructured data is audio data, the error rate between the first processed data and the second processed data can be achieved by comparing the waveform differences between the first processed data and the second processed data using the mean square error method; the error rate between the first processed data and the second processed data can be expressed by formula (10) as:

[0145] (10)

[0146] in, Indicates the total duration of the first or second processed data (the total duration of the first processed data is equal to the total duration of the second processed data). represents the audio signal at time j in the first processed data, Represents the audio signal at time j in the second processed data.

[0147] (4) If the unstructured data is video data, the error rate between the first processed data and the second processed data can be achieved by comparing the frame differences between the first processed data and the second processed data using a video frame difference method or a video frame matching method; the error rate between the first processed data and the second processed data can be equal to the video frame error rate and video frame feature error rate The result of arithmetic operation; among them, the video frame error rate This can be achieved using mean square error, video frame error rate and video frame feature error rate They can be expressed by formulas (11) and (12) respectively as:

[0148] (11)

[0149] (12)

[0150] in, Indicates the total number of video frames of the first processed data or the second processed data (the total number of video frames of the first processed data is equal to the total number of video frames of the second processed data), Indicates the first governance data Frame data of video frames, Indicates the second post-governance data Frame data of video frames; Indicates the number of unmatched video frames between the first processed data and the second processed data, Indicates the total number of video frames in the first processed data and the second processed data.

[0151] S220: Determine the error rate between each first processed data and each second processed data according to each processing strategy.

[0152] Specifically, the computer device may calculate the error rate between the first treated data and the second treated data corresponding to each data to be treated according to each processing strategy.

[0153] S230: Determine the maturity evaluation result of each data governance strategy to be evaluated based on the error rate between each first governance data and each second governance data.

[0154] In one embodiment, the computer device can perform at least one of analysis processing, comparison processing, and matching processing on the error rate between each first-governed data and each second-governed data, and obtain the maturity assessment results of each data governance strategy to be assessed based on the processing operation results.

[0155] In another embodiment, the computer device may send the error rate between each first-governed data and each second-governed data to a third-party device, instructing the third-party device to determine the maturity assessment results of each data governance policy to be assessed based on each error rate. Correspondingly, the computer device receives the maturity assessment results of each data governance policy to be assessed sent by the third-party device to obtain the maturity assessment results of each data governance policy to be assessed.

[0156] In one embodiment, the step of determining the maturity assessment result of each data governance policy to be assessed based on the error rate between each first governed data and each second governed data in S230 may include:

[0157] For any first-governed data, if the error rate between the first-governed data and the corresponding second-governed data is greater than the preset threshold, the maturity assessment result of the data governance strategy to be assessed corresponding to the first-governed data is determined to be unreliable; if the error rate between the first-governed data and the corresponding second-governed data is less than or equal to the preset threshold, the maturity assessment result of the data governance strategy to be assessed is determined to be reliable.

[0158] Optionally, the above-mentioned preset threshold value can be determined by the user or determined based on historical experience values, which is not limited in the embodiments of the present application.

[0159] In an embodiment of the present application, for any first governed data, if the error rate between the first governed data and the corresponding second governed data is greater than a preset threshold, it indicates that the difference between the data governance policy to be evaluated corresponding to the first governed data and the corresponding control data governance policy is large, and the maturity assessment result of the data governance policy to be evaluated can be determined to be unreliable. At the same time, if the error rate between the first governed data and the corresponding second governed data is less than or equal to the preset threshold, it indicates that the difference between the data governance policy to be evaluated corresponding to the first governed data and the corresponding control data governance policy is small or no difference, and the maturity assessment result of the data governance policy to be evaluated can be determined to be reliable.

[0160] It should be noted here that if the data governance strategy to be evaluated is unreliable, it indicates that the data governance strategy to be evaluated is immature and cannot be used as a subsequent reliable or effective data governance strategy; if the data governance strategy to be evaluated is reliable, it indicates that the data governance strategy to be evaluated is relatively mature and can be used as a subsequent reliable or effective data governance strategy.

[0161] In addition, the maturity assessment result of the data governance policy to be assessed can represent the maturity level of the data governance policy to be assessed. Specifically, the computer device can determine the maturity assessment level of the data governance policy to be assessed based on the corresponding threshold range of the error rate between the first governed data and the corresponding second governed data. For example, if the error rate between the first governed data and the corresponding second governed data falls within the range of 0%-2%, the maturity assessment level of the data governance policy to be assessed is determined to be high; if the error rate between the first governed data and the corresponding second governed data falls within the range of 2%-5%, the maturity assessment level of the data governance policy to be assessed is determined to be medium; if the error rate between the first governed data and the corresponding second governed data falls within the range of greater than 5%, the maturity assessment level of the data governance policy to be assessed is determined to be low.

[0162] The technical solution in the embodiment of the present application determines the processing strategy corresponding to the error rate between each first governed data and each second governed data according to the actual data type of each data to be governed, determines the error rate between each first governed data and each second governed data according to each processing strategy, and determines the maturity assessment result of each data governance strategy to be assessed according to the error rate between each first governed data and each second governed data; the above method can adopt the corresponding processing strategy to determine the error rate between the first governed data and the second governed data corresponding to different data to be governed according to the actual data type of different data to be governed, so that the accuracy of the determined error rate is higher, and then the maturity assessment result of each data governance strategy to be assessed can be accurately determined according to the obtained error rates, thereby improving the correctness of the determined maturity assessment result; at the same time, the above method can be applicable to various types of data to be governed, and has no limitations on the usage scenarios of the maturity assessment method, thereby improving the wide applicability of the maturity assessment method.

[0163] In one embodiment, the present application also provides a maturity assessment method applied to a computer device, the method comprising the following process:

[0164] (1) Construct mapping relationships based on governance requirement information, different data governance indicators, and different data governance goals corresponding to different standard data types;

[0165] (2) According to the actual data type of each data to be governed, searching in the mapping relationship for data governance targets corresponding to the standard data types that match each actual data type as multiple first data governance targets;

[0166] (3) constructing each data governance strategy to be evaluated based on at least one first data governance indicator and each first data governance goal corresponding to each actual data type;

[0167] (4) Determine at least one second data governance indicator corresponding to each first data governance indicator based on a preset indicator set and mapping relationship; the second data governance indicator includes a first sub-indicator and a second sub-indicator;

[0168] The above step (4) may be implemented in the following manner:

[0169] (41) performing similarity processing on the basis of the plurality of original data governance objectives and each first data governance objective, and determining a similarity value between each original data governance objective and each first data governance objective;

[0170] (42) Selecting multiple reference data governance targets whose similarity values ​​are greater than a preset similarity threshold from each original data governance target;

[0171] (43) Searching for data governance indicators corresponding to data governance objectives that match each reference data governance objective in the mapping relationship as multiple candidate data governance indicators;

[0172] (44) Selecting at least one candidate data governance indicator in the indicator set from each candidate data governance indicator as each first sub-indicator;

[0173] (45) obtaining a plurality of original data governance objectives; and dividing the historical data governance problems into a plurality of problem sets; each original data governance objective covers at least one problem set;

[0174] (46) For any original data governance objective, if there is no overlap between at least one problem set covered by the original data governance objective and at least one problem set covered by each first data governance objective, the original data governance objective shall be determined as the third data governance objective;

[0175] (47) searching for data governance indicators corresponding to data governance objectives that match each third data governance objective in the mapping relationship as multiple candidate data governance indicators;

[0176] (48) selecting at least one candidate data governance indicator in the indicator set from each candidate data governance indicator as each second sub-indicator;

[0177] (5) constructing each comparison data governance strategy based on each first data governance indicator, each second data governance indicator, and at least one second data governance goal;

[0178] (6) performing data governance processing on each data to be governed according to at least one data governance policy to be evaluated, to obtain a plurality of first governed data; and performing data governance processing on each data to be governed according to a reference data governance policy corresponding to each data governance policy to be evaluated, to obtain a plurality of second governed data; the reference data governance policy is a pre-constructed data governance policy that is similar to or complementary to the corresponding data governance policy to be evaluated;

[0179] (7) Determine the processing strategy corresponding to the error rate between each first-treated data and each second-treated data according to the actual data type of each data to be treated;

[0180] (8) determining the error rate between each first treated data and each second treated data according to each processing strategy;

[0181] (9) For any first-governed data, if the error rate between the first-governed data and the corresponding second-governed data is greater than a preset threshold, the maturity assessment result of the data governance policy to be assessed corresponding to the first-governed data is determined to be unreliable;

[0182] (10) If the error rate between the first governed data and the corresponding second governed data is less than or equal to a preset threshold, the maturity assessment result of the data governance strategy to be assessed is determined to be reliable.

[0183] The execution process of (1) to (10) above can be specifically referred to the description of the above embodiment. The implementation principles and technical effects are similar and will not be repeated here.

[0184] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0185] Based on the same inventive concept, embodiments of the present application also provide a maturity assessment device for implementing the aforementioned maturity assessment method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more maturity assessment device embodiments provided below can be found in the above-described limitations of the maturity assessment method and will not be further elaborated here.

[0186] In one embodiment, Figure 10 This is a schematic diagram of the structure of a maturity assessment device in one embodiment of the present application. The maturity assessment device provided in the embodiment of the present application can be applied to computer equipment. Figure 10 As shown, the maturity assessment device of the embodiment of the present application may include: a processing module 11 and a maturity assessment module 12, wherein:

[0187] Processing module 11 is configured to perform data governance processing on each data to be governed according to at least one data governance policy to be evaluated, thereby obtaining a plurality of first governed data; and perform data governance processing on each data to be governed according to a reference data governance policy corresponding to each data governance policy to be evaluated, thereby obtaining a plurality of second governed data; the reference data governance policy is a pre-established data governance policy that is similar to or complementary to the corresponding data governance policy to be evaluated;

[0188] The maturity assessment module 12 is configured to perform maturity assessment on each data governance policy to be assessed based on each first governed data and each second governed data, and determine a maturity assessment result of each data governance policy to be assessed.

[0189] The maturity assessment device provided in the embodiment of the present application can be used to implement the technical solution in the above-mentioned maturity assessment method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0190] In one embodiment, the maturity assessment module 12 includes: a first determination unit, a second determination unit, and a third determination unit, wherein:

[0191] A first determining unit is configured to determine, according to the actual data type of each to-be-groomed data, a processing strategy corresponding to an error rate between each first-groomed data and each second-groomed data;

[0192] A second determining unit is used to determine the error rate between each first treated data and each second treated data according to each processing strategy;

[0193] The third determining unit is configured to determine a maturity evaluation result of each data governance policy to be evaluated according to an error rate between each first governed data and each second governed data.

[0194] The maturity assessment device provided in the embodiment of the present application can be used to implement the technical solution in the above-mentioned maturity assessment method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0195] In one embodiment, the third determining unit is specifically configured to:

[0196] For any first-governed data, if the error rate between the first-governed data and the corresponding second-governed data is greater than a preset threshold, the maturity assessment result of the data governance policy to be assessed corresponding to the first-governed data is determined to be unreliable;

[0197] If the error rate between the first governed data and the corresponding second governed data is less than or equal to a preset threshold, the maturity assessment result of the data governance policy to be assessed is determined to be reliable.

[0198] The maturity assessment device provided in the embodiment of the present application can be used to implement the technical solution in the above-mentioned maturity assessment method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0199] In one embodiment, the maturity assessment device further includes: a mapping relationship building module, a search module, and a first strategy building module, wherein:

[0200] A mapping relationship building module is used to build mapping relationships based on the governance requirement information corresponding to different standard data types, different data governance indicators, and different data governance goals;

[0201] A search module, configured to search, according to the actual data type of each to-be-managed data, in a mapping relationship for data governance targets corresponding to standard data types that match each actual data type as a plurality of first data governance targets;

[0202] The first policy building module is used to build each data governance policy to be evaluated according to at least one first data governance indicator and each first data governance goal corresponding to each actual data type.

[0203] The maturity assessment device provided in the embodiment of the present application can be used to implement the technical solution in the above-mentioned maturity assessment method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0204] In one embodiment, the maturity assessment device further comprises: a determination module and a second strategy construction module, wherein:

[0205] a determination module, configured to determine at least one second data governance indicator corresponding to each first data governance indicator based on a preset indicator set and mapping relationship;

[0206] The second strategy building module is used to build each comparison data governance strategy according to each first data governance indicator, each second data governance indicator and at least one second data governance goal.

[0207] The maturity assessment device provided in the embodiment of the present application can be used to implement the technical solution in the above-mentioned maturity assessment method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0208] In one embodiment, the second data governance indicator includes a first sub-indicator; the determination module includes: a first acquisition unit, a first search unit, and a first screening unit, wherein:

[0209] A first acquisition unit is used to acquire multiple reference data governance targets;

[0210] A first search unit is configured to search, in the mapping relationship, for data governance indicators corresponding to data governance objectives that match each reference data governance objective, as multiple candidate data governance indicators;

[0211] The first screening unit is configured to screen at least one candidate data governance indicator in the indicator set from the candidate data governance indicators as each first sub-indicator.

[0212] The maturity assessment device provided in the embodiment of the present application can be used to implement the technical solution in the above-mentioned maturity assessment method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0213] In one embodiment, the first acquiring unit is specifically configured to:

[0214] Performing similarity processing on the multiple original data governance objectives and each first data governance objective to determine a similarity value between each original data governance objective and each first data governance objective;

[0215] Multiple reference data governance objectives with similarity values ​​greater than a preset similarity threshold are selected from each original data governance objective.

[0216] The maturity assessment device provided in the embodiment of the present application can be used to implement the technical solution in the above-mentioned maturity assessment method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0217] In one embodiment, the second data governance indicator includes a second sub-indicator; the determination module includes: a second acquisition unit, a second search unit, and a second screening unit, wherein:

[0218] A second acquisition unit, configured to acquire a plurality of third data governance objectives;

[0219] A second search unit is configured to search, in the mapping relationship, for data governance indicators corresponding to data governance objectives that match each third data governance objective, as multiple candidate data governance indicators;

[0220] The second screening unit is configured to screen at least one candidate data governance indicator in the indicator set from the candidate data governance indicators as each second sub-indicator.

[0221] The maturity assessment device provided in the embodiment of the present application can be used to implement the technical solution in the above-mentioned maturity assessment method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0222] In one embodiment, the second acquiring unit is specifically configured to:

[0223] Obtain multiple original data governance objectives; and divide historical data governance issues into multiple problem sets; each original data governance objective covers at least one problem set;

[0224] For any original data governance objective, if there is no overlap between at least one problem set covered by the original data governance objective and at least one problem set covered by each first data governance objective, the original data governance objective is determined as the third data governance objective.

[0225] The maturity assessment device provided in the embodiment of the present application can be used to implement the technical solution in the above-mentioned maturity assessment method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0226] The specific definition of the maturity assessment device can refer to the definition of the maturity assessment method above, which will not be repeated here. Each module in the above maturity assessment device can be realized by software, hardware and their combination in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor calls to execute the operation corresponding to each module.

[0227] In one embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 11 The computer device includes a processor, a memory and a network interface connected through a system bus. The processor of the computer device is configured to provide processing capability. 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 running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store each to-be-governed data, each to-be-assessed data governance strategy and each control data governance strategy. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a maturity assessment method.

[0228] Those skilled in the art can understand that Figure 11 The structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0229] In one embodiment, a computer device is also provided, which includes a memory and a processor. The memory stores a computer program, and the processor implements the technical solutions in the above-mentioned maturity assessment method embodiments of the present application when executing the computer program. The implementation principles and technical effects are similar, and will not be repeated here.

[0230] In one embodiment, a computer readable storage medium is also provided, which stores a computer program. The computer program is executed by the processor to implement the technical solutions in the above-mentioned maturity assessment method of the present application. The implementation principles and technical effects are similar, and will not be repeated here.

[0231] In one embodiment, a computer program product is also provided, which includes a computer program. The computer program is executed by the processor to implement the technical solutions in the above-mentioned maturity assessment method of the present application. The implementation principles and technical effects are similar, and will not be repeated here.

[0232] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0233] The technical features of the above embodiments can 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.

[0234] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A maturity assessment method, characterized in that: The method comprises: Performing data governance processing on each data to be governed according to at least one data governance policy to be evaluated to obtain a plurality of first governed data; and performing data governance processing on each data to be governed according to a reference data governance policy corresponding to each of the data governance policies to be evaluated to obtain a plurality of second governed data; the reference data governance policy is a pre-constructed data governance policy that is similar to or complementary to the corresponding data governance policy to be evaluated; Determining, according to the actual data type of each of the to-be-groomed data, a processing strategy corresponding to the error rate between each of the first-groomed data and each of the second-groomed data; determining an error rate between each of the first treated data and each of the second treated data according to each of the processing strategies; For any first-governed data, if the error rate between the first-governed data and the corresponding second-governed data is greater than a preset threshold, the maturity assessment result of the data governance policy to be assessed is determined to be unreliable; if the error rate is less than or equal to the preset threshold, the maturity assessment result is determined to be reliable; The construction process of each control data governance strategy includes: Obtain multiple original data governance objectives; and divide historical data governance issues into multiple problem sets; each original data governance objective covers at least one problem set; According to the actual data type of each of the to-be-governed data, searching in the mapping relationship for data governance targets corresponding to standard data types that match each of the actual data types as multiple first data governance targets; For any original data governance objective, if there is no overlap between at least one problem set covered by the original data governance objective and at least one problem set covered by each of the first data governance objectives, the original data governance objective is determined as the third data governance objective; Determine each second sub-indicator in at least one second data governance indicator based on each third data governance objective, and construct a complementary comparison data governance strategy based on each second sub-indicator.

2. The method according to claim 1, characterized in that Determining the maturity evaluation result of each of the data governance policies to be evaluated based on the error rate between each of the first governed data and each of the second governed data includes: For any first governed data, if the error rate between the first governed data and the corresponding second governed data is greater than a preset threshold, the maturity assessment result of the data governance policy to be assessed corresponding to the first governed data is determined to be unreliable; If the error rate between the first governed data and the corresponding second governed data is less than or equal to the preset threshold, it is determined that the maturity assessment result of the data governance policy to be assessed is reliable.

3. The method according to claim 1 or 2, characterized in that The process of constructing each of the data governance strategies to be evaluated includes: Construct mapping relationships based on governance requirement information, different data governance indicators, and different data governance goals corresponding to different standard data types; Each of the data governance strategies to be evaluated is constructed based on at least one first data governance indicator corresponding to each of the actual data types and each of the first data governance objectives.

4. The method according to claim 3, characterized in that The construction process of each of the control data governance strategies includes: Determining at least one second data governance indicator corresponding to each of the first data governance indicators according to a preset indicator set and the mapping relationship; Constructing each of the comparison data governance policies according to each of the first data governance indicators, each of the second data governance indicators, and at least one second data governance goal.

5. The method according to claim 4, characterized in that The second data governance indicator includes a first sub-indicator; and determining, based on the preset indicator set and the mapping relationship, at least one second data governance indicator corresponding to each of the first data governance indicators includes: Obtain multiple reference data governance objectives; Searching the mapping relationships for data governance indicators corresponding to data governance objectives that match the reference data governance objectives, as multiple candidate data governance indicators; At least one candidate data governance indicator in the indicator set is selected from the candidate data governance indicators as each of the first sub-indicators.

6. The method according to claim 5, characterized in that The acquisition of multiple reference data governance objectives includes: Performing similarity processing on the multiple original data governance objectives and each first data governance objective to determine a similarity value between each original data governance objective and each first data governance objective; The multiple reference data governance objectives having similarity values ​​greater than a preset similarity threshold are screened from the original data governance objectives.

7. The method according to claim 4, characterized in that The determining, based on the preset indicator set and the mapping relationship, at least one second data governance indicator corresponding to each of the first data governance indicators includes: Acquire multiple third-party data governance objectives; Searching the mapping relationships for data governance indicators corresponding to data governance objectives that match each of the third data governance objectives as multiple candidate data governance indicators; At least one candidate data governance indicator in the indicator set is selected from the candidate data governance indicators as each second sub-indicator.

8. A maturity assessment device, characterized in that: The device comprises: a processing module configured to perform data governance processing on each data to be governed according to at least one data governance policy to be evaluated, to obtain a plurality of first governed data; and to perform data governance processing on each data to be governed according to a reference data governance policy corresponding to each of the data governance policies to be evaluated, to obtain a plurality of second governed data; the reference data governance policy is a pre-constructed data governance policy that is similar to or complementary to the corresponding data governance policy to be evaluated; a maturity assessment module, configured to determine, based on the actual data type of each of the data to be governed, a processing strategy corresponding to an error rate between each of the first governed data and each of the second governed data; determine, according to each of the processing strategies, an error rate between each of the first governed data and each of the second governed data; for any first governed data, if the error rate between the first governed data and the corresponding second governed data is greater than a preset threshold, determine that the maturity assessment result of the governance strategy for the data to be assessed is unreliable; if the error rate is less than or equal to the preset threshold, determine that the maturity assessment result is reliable; The construction process of each control data governance strategy includes: Obtain multiple original data governance objectives; and divide historical data governance issues into multiple problem sets; each original data governance objective covers at least one problem set; According to the actual data type of each of the to-be-governed data, searching in the mapping relationship for data governance targets corresponding to standard data types that match each of the actual data types as multiple first data governance targets; For any original data governance objective, if there is no overlap between at least one problem set covered by the original data governance objective and at least one problem set covered by each of the first data governance objectives, the original data governance objective is determined as the third data governance objective; Determine each second sub-indicator in at least one second data governance indicator based on each third data governance objective, and construct a complementary comparison data governance strategy based on each second sub-indicator.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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