CN116245580A - Data asset value acquisition method, apparatus, device, medium and program product - Google Patents
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Abstract
The present application relates to a data asset value acquisition module method, apparatus, computer device, storage medium and computer program product. The method comprises the following steps: acquiring a data asset to be evaluated and a value evaluation index; calculating a target index value of the data asset to be evaluated in parallel based on each value evaluation index; acquiring the weight corresponding to each value evaluation index; and obtaining the value of the data asset based on the target index value and the weight. The method can improve the processing efficiency and quantify the value of the data asset.
Description
Technical Field
The present application relates to the field of computer technology, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for acquiring a value of a data asset.
Background
With the development of computer technology, data is becoming the basis of life and work of people, and many products and services exist in digital form. When data is shared, exchanged, and reused, a reasonable assessment of the value of the data is required.
Currently, the common evaluation methods of the value of the data asset comprise three basic methods of cost method, income method and market method and derivative methods thereof, but the methods are difficult to quantify or mainly evaluate subjectively.
Therefore, there is an urgent need for a way to efficiently evaluate the value of data assets.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a data asset value acquisition method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve processing efficiency and quantify the value of a data asset.
In a first aspect, the present application provides a method for obtaining value of a data asset, the method comprising:
acquiring a data asset to be evaluated and a value evaluation index;
calculating a target index value of the data asset to be evaluated in parallel based on each value evaluation index;
acquiring the weight corresponding to each value evaluation index;
and obtaining the value of the data asset based on the target index value and the weight.
In one embodiment, the calculating, based on each of the value evaluation indexes, a target index value of the data asset to be evaluated in parallel includes:
acquiring metadata of the data asset to be evaluated through a first thread, and calculating a data standard index value based on the metadata;
Acquiring a data auditing rule through a second thread, and auditing the data asset to be evaluated based on the data auditing rule to obtain a data quality index value;
determining, by a third line Cheng Huoqu, a blood-lineage correlation table for the data asset under evaluation, a blood-lineage index value for the data asset under evaluation based on the blood-lineage correlation table;
determining, via a fourth line Cheng Huoqu, a field and a data table of the data asset under evaluation, an integrity indicator value for the data asset under evaluation based on the field and the data table; the first thread, the second thread, the third thread and the fourth thread are parallel threads.
In one embodiment, the obtaining metadata of the data asset to be evaluated, and calculating a data standard index value based on the metadata includes:
acquiring a mark file of the data asset to be evaluated, and acquiring metadata from the mark file;
and calculating a data standard index value based on the data attribute of the metadata.
In one embodiment, the acquiring the data auditing rule, and auditing the data asset to be evaluated based on the data auditing rule to obtain the data quality index value includes:
Acquiring data auditing rules obtained from each channel;
judging whether the data asset to be evaluated triggers the data auditing rule or not;
when the data to be evaluated triggers the data auditing rule, adjusting a target index value to obtain a data quality index value based on the number and importance of the triggered data auditing rule.
In one embodiment, the obtaining the blood-edge association table of the data asset to be evaluated, determining the blood-edge index value of the data asset to be evaluated based on the blood-edge association table, includes:
acquiring each data table associated with the data asset to be evaluated and a system corresponding to each data table;
analyzing codes of the systems, and determining association relations of the data tables to obtain blood-margin association tables;
and determining the blood margin index value of the data asset to be evaluated based on the reference times of the data asset to be evaluated in the blood margin association table.
In one embodiment, the obtaining the field and the data table of the data asset to be evaluated, determining the integrity index value of the data asset to be evaluated based on the field and the data table, includes:
acquiring a field and a data table of the data asset to be evaluated;
Comparing the fields of the data asset to be evaluated and the data attribute of the data table with standard data attribute;
and obtaining the integrity index value of the data asset to be evaluated based on the comparison result.
In a second aspect, the present application further provides a data asset value acquisition device, the device comprising:
the data acquisition module is used for acquiring the data asset to be evaluated and the value evaluation index;
an index value calculation module for calculating a target index value of the data asset to be evaluated in parallel based on each value evaluation index;
the weight acquisition module is used for acquiring the weight corresponding to each value evaluation index;
and the data asset value acquisition module is used for acquiring the data asset value based on the target index value and the weight.
In a third aspect, the present application also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method of any one of the embodiments described above when the computer program is executed by the processor.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the embodiments described above.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of any of the embodiments described above.
The data asset value acquisition method, the data asset value acquisition device, the computer equipment, the storage medium and the computer program product quantitatively calculate the target index value of the data asset to be evaluated through the fixed value evaluation index, and determine the weight corresponding to each value evaluation index based on the expert matrix; and obtaining the value of the data asset based on the target index value and the weight, so that the quantitative calculation of the value of the data asset is realized, the calculation is performed in parallel in the calculation process, and the processing efficiency is improved.
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FIG. 1 is an application environment diagram of a data asset value acquisition method in one embodiment;
FIG. 2 is a flow diagram of a method of data asset value acquisition in one embodiment;
FIG. 3 is a flow diagram of the steps of multithreading in one embodiment;
FIG. 4 is a block diagram of a data asset value acquisition device in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The data asset value obtaining method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the server 102 communicates with the database 104 via a network. The data storage system may store data that the server 102 needs to process, such as data obtained from the database 104. The data storage system may be integrated on the server 102 or may be located on a cloud or other network server.
Specifically, the server 102 acquires the data asset to be evaluated and the value evaluation index, and calculates the target index value of the data asset to be evaluated in parallel based on each value evaluation index; acquiring the weight corresponding to each value evaluation index; and obtaining the value of the data asset based on the target index value and the weight, wherein each value evaluation index comprises indexes in terms of data quality, standardization degree, data use condition, data storage condition and the like, so that the automatic acquisition and statistics of indexes such as data quality accuracy rate, consistency rate, integrity rate, standardization rate, use heat, storage capacity, storage utilization rate and the like are realized through the construction of modules such as data standard, quality management, metadata management and the like, the weight of each index is scored and confirmed by an expert matrix method, and finally the score of each index is transversely summed to obtain the final score of the value of the data asset. Thereby evaluating in a quantitative manner the quality of a team or department for data asset maintenance, value utilization.
According to the data asset value acquisition method, the target index value of the data asset to be evaluated is quantitatively calculated through the fixed value evaluation index, and the weight corresponding to each value evaluation index is determined based on the expert matrix; and obtaining the value of the data asset based on the target index value and the weight, so that the quantitative calculation of the value of the data asset is realized, the calculation is performed in parallel in the calculation process, and the processing efficiency is improved.
The server 102 may be implemented as a stand-alone server or a server cluster including a plurality of servers. Database 104 may be implemented as a stand-alone database or as a cluster of databases comprising a plurality of databases
In one embodiment, as shown in fig. 2, a method for obtaining a value of a data asset is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
s202: and acquiring the data asset to be evaluated and a value evaluation index.
In particular, the data asset to be evaluated is the data that is required to be evaluated for value of the data asset, which may be all of the data in one database or the data in one or more tables in one database. Alternatively, the data assets to be evaluated may be corresponding to processing systems, each processing system corresponding to a corresponding type of data in a processing database, where the same type of data is a set of data assets to be evaluated, optionally the same type of data in the database is stored in a data table.
The value evaluation index is constructed from a plurality of aspects based on the angle of data construction, and comprises at least one of a data standard index, a data quality index, a blood margin index and an integrity index, wherein the data standard index is constructed from the aspect of data standardization, the data quality index is constructed from the aspect of data accuracy, the data blood margin index is constructed from the aspect of data use condition, integrity and the like, the data integrity index is constructed from the aspects of data English name, chinese name, data type, field length and the like, and each value evaluation index realizes the complete characterization of the data quality accuracy, consistency rate, integrity rate, standardization rate, use heat, storage capacity and storage utilization rate.
In one alternative embodiment, different data assets to be evaluated may have different value evaluation indexes, for example, when an evaluation task of the data assets to be evaluated is constructed, the different value evaluation indexes may be selected according to needs, so as to implement personalized evaluation of the different data assets to be evaluated.
S204: and calculating a target index value of the data asset to be evaluated in parallel based on each value evaluation index.
Specifically, the server constructs different parallel threads according to the number of the value evaluation indexes, so that a target index value corresponding to one value evaluation index of the data asset to be evaluated is calculated in each parallel thread, and the evaluation efficiency of the data asset to be evaluated is improved.
Optionally, in practical application, the server may be provided with different modules, including, for example, a data standard management module, a metadata management module, a data quality management module, and a data value management module, so that the different modules may perform data processing separately to improve accuracy of data processing. It should be noted that, since each parallel thread has commonality in processing data when calculating the target index value, the server extracts the commonality calculation aspect of each value evaluation index to calculate by a separate thread, and calculates the individuation calculation aspect of the value evaluation index by the parallel thread, thereby improving the processing efficiency. For example, the metadata of the data asset to be evaluated can be acquired through a separate thread, then the target index values of the data standard index and the integrity index are calculated, the metadata acquired through the separate thread are respectively acquired through parallel threads, and then the target index values are calculated respectively.
S206: and obtaining the weight corresponding to each value evaluation index.
Specifically, the weights are obtained by scoring and confirming the weights of the indexes by an expert matrix method. In other embodiments, the weights of the respective value evaluation indexes may be calculated by a neural network or the like, which is not particularly limited herein. The weights may also be adjusted based on the needs of the user to ensure the accuracy of the calculated data asset value.
S208: and obtaining the value of the data asset based on the target index value and the weight.
Specifically, the scores of each index are summed laterally to obtain a final score for the value of the data asset, e.g., a weighted sum of the value of the data asset equal to each target index value and the weight. For convenience of explanation, the data standard index, the data quality index, the blood-margin index and the integrity index are taken as examples for explanation, wherein the data standard index is A, the data quality index is B, the blood-margin index is C, the integrity index is D, and the weights corresponding to the data standard index, the data quality index, the blood-margin index and the integrity index are a, B, C and D respectively. Data asset value = a x a+b x b+c x c+d. In addition, it should be noted that each value evaluation index may further include a score index, so that the target index value of each value evaluation index is a weighted sum of each score index, where the weighted manner is similar to the weighted manner of the value of the data asset, and will not be described herein.
According to the data asset value acquisition method, the target index value of the data asset to be evaluated is quantitatively calculated through the fixed value evaluation index, and the weight corresponding to each value evaluation index is determined based on the expert matrix; and obtaining the value of the data asset based on the target index value and the weight, so that the quantitative calculation of the value of the data asset is realized, the calculation is performed in parallel in the calculation process, and the processing efficiency is improved.
In one embodiment, the calculating, based on each of the value evaluation indexes, a target index value of the data asset to be evaluated in parallel includes: acquiring metadata of the data asset to be evaluated through a first thread, and calculating a data standard index value based on the metadata; acquiring a data auditing rule through a second thread, and auditing the data asset to be evaluated based on the data auditing rule to obtain a data quality index value; determining, by a third line Cheng Huoqu, a blood-lineage correlation table for the data asset under evaluation, a blood-lineage index value for the data asset under evaluation based on the blood-lineage correlation table; determining, via a fourth line Cheng Huoqu, a field and a data table of the data asset under evaluation, an integrity indicator value for the data asset under evaluation based on the field and the data table; the first thread, the second thread, the third thread and the fourth thread are parallel threads.
Specifically, as shown in fig. 3, in this embodiment, different value evaluation indexes are processed by different threads, so that target index values of data assets to be evaluated are calculated in parallel, thus saving calculation time and improving processing efficiency.
Wherein the server tries to analyze the data value based on the data standard management module, the metadata management module, the data quality management module and the data value management module from the data construction perspective.
The data standard management module comprehensively manages the data standard by formulating a unified data specification, classification codes, data interaction formats, data terms, file formats and a unified service index system and landing the data standard. The data standard management is standard management of each data item, and a data structure standard management interface comprises standard checking, flow management, history searching function, version comparison function, standard alignment function and standard penetration function, so that comprehensive management of the data standard is realized. Wherein each data standard is subsequently used for calculation of a data standard indicator for the data asset to be evaluated. And the respective data criteria may be preset by the user, so that a privately-owned configuration may be realized.
The metadata management module mainly comprises metadata acquisition requirements, metadata change management, metadata application management (blood-bearing relationship and influence analysis) and the like. The visual management of the system is realized, metadata combing among the systems is completed, metadata content is standardized, metadata standards are unified, blood margin analysis, influence analysis and metadata version control are enhanced. The metadata management module is used for calculating the standard indexes of the follow-up data, and the like, and can be regarded as the common calculation aspect of each value evaluation index, and each parallel thread can acquire corresponding metadata information from the metadata management module, so that the calculation efficiency is improved.
The data quality management interface mainly comprises data quality flow management, data quality monitoring and data quality problem analysis functions. The data quality problems in the data application process are systematically and flowsheet managed, and a data quality problem report and a data quality billboard are produced. The data quality problem monitoring management observes abnormal fluctuation conditions of data according to laid monitoring rules and is used for early warning the data quality problem so as to solve the problem in time and avoid data application accidents. The data quality management interface is mainly an output module and is used for outputting the value of the data asset, so that the user can conveniently check the value, namely, after the value of the data asset is calculated, the value of the data asset is sent to the data quality management interface, and the output of the value of the data asset is realized.
The data value management module is used for making a data asset catalog, unifying a data asset map, combing stock data, combing the association relation between a service scene and data resources according to a theme in combination with the service, strengthening the relation among the service, the system and the data, knowing the service conditions such as the coldness and the warmth of the data and reducing the application threshold of the data. And (3) formulating an assessment and measurement standard of the data value, developing and landing a quantitative model of the data value, and classifying the data.
In this embodiment, the server constructs different modules based on the value evaluation index, so that each module works independently, so as to implement calculation of the value evaluation index, and improve processing efficiency.
In one embodiment, the obtaining metadata of the data asset to be evaluated, and calculating a data standard index value based on the metadata includes: acquiring a mark file of the data asset to be evaluated, and acquiring metadata from the mark file; and calculating a data standard index value based on the data attribute of the metadata.
Specifically, the calculation of the target index value corresponding to the data standard index may include: whether the English name, chinese name, field type, field length and the like of the metadata meet the data standard or not is judged.
Metadata information collection is divided into two cases, one of which is that a server automatically collects a mark file of a metadata system, metadata information is obtained from the mark file, and the other is that the metadata system automatically pushes the metadata information to the server. The server automatically performs label matching operation on the acquired metadata information and the data standard, scores the data standard according to the matching degree of the data standard, and can respectively obtain the English name, chinese name, field type, field length of the metadata and the matching degree of the data standard, and then synthesizes the matching degrees to obtain the data standard index value.
Specifically, the matching degree between the english name, chinese name, field type, field length and the data standard of the metadata may be that the similarity between the english name, chinese name, field type, field length and the data standard of the metadata, for example, the similarity between the character, the character position, etc. is calculated, and then the target similarity is selected as the data standard index value, where the target similarity may be the minimum similarity, and in other embodiments, the target similarity may be other, which is not described herein.
In one embodiment, the acquiring the data auditing rule, and auditing the data asset to be evaluated based on the data auditing rule to obtain the data quality index value includes: acquiring data auditing rules obtained from each channel; judging whether the data asset to be evaluated triggers the data auditing rule or not; when the data to be evaluated triggers the data auditing rule, adjusting a target index value to obtain a data quality index value based on the number and importance of the triggered data auditing rule.
The data auditing rules may include monitoring class and registration class auditing rules, with the data quality indicator value being discounted by the number of data quality problems for the monitoring class and registration class. And the monitoring class, the service lays out data quality checking rules in the server, and the server automatically monitors whether the content of the data asset to be evaluated accords with the monitoring rules or not and deducts the touch condition. And (3) registering, wherein the business monitors the content of the data asset to be evaluated from other channels, and inputs the check result into the server.
The different data auditing rules can be processed through different threads, and finally, the unified threads are used for comprehensive processing to obtain a data quality index value, for example, the original value of the index value of the data quality is 100 minutes, and then, the data asset to be evaluated is audited based on the data auditing rules to obtain the corresponding deduction, and the data quality index value is obtained after the deduction is completed.
In one embodiment, the obtaining the blood-edge association table of the data asset to be evaluated, determining the blood-edge index value of the data asset to be evaluated based on the blood-edge association table, includes: acquiring each data table associated with the data asset to be evaluated and a system corresponding to each data table; analyzing codes of the systems, and determining association relations of the data tables to obtain blood-margin association tables; and determining the blood margin index value of the data asset to be evaluated based on the reference times of the data asset to be evaluated in the blood margin association table.
The blood margin index value is calculated by the number of times of the blood margin association table, and the server automatically collects codes of related systems and performs visual display after automatic analysis. The blood margin analysis and the influence analysis of all tables can be checked through the server, and the scores are carried out according to the reference times.
In practical application, each data table associated with the data asset to be evaluated is determined, and a system for generating or updating data in the data table is determined, so that the reference relation of each system can be obtained by analyzing the codes of the systems, and the blood-edge association tables of each data table can be obtained because the systems and the data tables have one-to-one correspondence, so that the reference times of the data asset to be evaluated can be determined based on the blood-edge association tables, and further the blood-edge index value of the data asset to be evaluated is obtained, specifically, when the reference times are more, the blood-edge index value is higher, in other words, the data asset to be evaluated is used for multiple times, the utilization rate is higher, and the value is also high.
In one embodiment, the obtaining the field and the data table of the data asset to be evaluated, determining the integrity index value of the data asset to be evaluated based on the field and the data table, includes: acquiring a field and a data table of the data asset to be evaluated; comparing the fields of the data asset to be evaluated and the data attribute of the data table with standard data attribute; and obtaining the integrity index value of the data asset to be evaluated based on the comparison result.
Specifically, metadata integrity is scored by whether the Chinese names of the fields and tables are complete. The part of data is automatically pushed to a server by a data system, the server collects related information and compares the related information with the collected metadata information, and the complete condition of fields and table names is checked.
For example, comparing the collected metadata with the standard metadata field and table chinese name, if the collected metadata field and table chinese name do not include the standard metadata field and table chinese name, determining the incompleteness.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a data asset value acquisition device for realizing the data asset value acquisition method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitations in the embodiments of one or more data asset value obtaining apparatuses provided below may be referred to the limitations of the data asset value obtaining method hereinabove, and will not be described herein.
In one embodiment, as shown in FIG. 4, there is provided a data asset value acquisition device comprising: a data acquisition module 401, an index value calculation module 402, a weight acquisition module 403, and a data asset value acquisition module 404, wherein:
a data acquisition module 401, configured to acquire a data asset to be evaluated and a value evaluation index;
an index value calculation module 402, configured to calculate, in parallel, a target index value of the data asset to be evaluated based on each of the value evaluation indexes;
a weight obtaining module 403, configured to obtain weights corresponding to the value evaluation indexes;
a data asset value acquisition module 404, configured to obtain a data asset value based on the target index value and the weight.
In one embodiment, the index value calculation module 402 is further configured to obtain metadata of the data asset to be evaluated through a first thread, and calculate a data standard index value based on the metadata; acquiring a data auditing rule through a second thread, and auditing the data asset to be evaluated based on the data auditing rule to obtain a data quality index value; determining, by a third line Cheng Huoqu, a blood-lineage correlation table for the data asset under evaluation, a blood-lineage index value for the data asset under evaluation based on the blood-lineage correlation table; determining, via a fourth line Cheng Huoqu, a field and a data table of the data asset under evaluation, an integrity indicator value for the data asset under evaluation based on the field and the data table; the first thread, the second thread, the third thread and the fourth thread are parallel threads.
In one embodiment, the index value calculation module 402 is further configured to obtain a flag file of the data asset to be evaluated, and obtain metadata from the flag file; and calculating a data standard index value based on the data attribute of the metadata.
In one embodiment, the index value calculation module 402 is further configured to obtain data auditing rules obtained from each channel; judging whether the data asset to be evaluated triggers the data auditing rule or not; when the data to be evaluated triggers the data auditing rule, adjusting a target index value to obtain a data quality index value based on the number and importance of the triggered data auditing rule.
In one embodiment, the index value calculation module 402 is further configured to obtain each data table associated with the data asset to be evaluated, and a system corresponding to each data table; analyzing codes of the systems, and determining association relations of the data tables to obtain blood-margin association tables; and determining the blood margin index value of the data asset to be evaluated based on the reference times of the data asset to be evaluated in the blood margin association table.
In one embodiment, the index value calculation module 402 is further configured to obtain a field of the data asset to be evaluated and a data table; comparing the fields of the data asset to be evaluated and the data attribute of the data table with standard data attribute; and obtaining the integrity index value of the data asset to be evaluated based on the comparison result.
The various modules in the data asset value acquisition device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store data assets to be evaluated and a value assessment index. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data asset value acquisition method.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of: acquiring a data asset to be evaluated and a value evaluation index; calculating a target index value of the data asset to be evaluated in parallel based on each value evaluation index; acquiring the weight corresponding to each value evaluation index; and obtaining the value of the data asset based on the target index value and the weight.
In one embodiment, the computing, in parallel, of a target indicator value for the data asset under evaluation based on each of the value assessment indicators implemented when the processor executes the computer program, comprises: acquiring metadata of the data asset to be evaluated through a first thread, and calculating a data standard index value based on the metadata; acquiring a data auditing rule through a second thread, and auditing the data asset to be evaluated based on the data auditing rule to obtain a data quality index value; determining, by a third line Cheng Huoqu, a blood-lineage correlation table for the data asset under evaluation, a blood-lineage index value for the data asset under evaluation based on the blood-lineage correlation table; determining, via a fourth line Cheng Huoqu, a field and a data table of the data asset under evaluation, an integrity indicator value for the data asset under evaluation based on the field and the data table; the first thread, the second thread, the third thread and the fourth thread are parallel threads.
In one embodiment, the obtaining metadata of the data asset to be evaluated, which is implemented when the processor executes the computer program, and calculating a data standard index value based on the metadata, includes: acquiring a mark file of the data asset to be evaluated, and acquiring metadata from the mark file; and calculating a data standard index value based on the data attribute of the metadata.
In one embodiment, the acquiring data auditing rule implemented by the processor when executing the computer program, and auditing the data asset to be evaluated based on the data auditing rule, to obtain a data quality index value, includes: acquiring data auditing rules obtained from each channel; judging whether the data asset to be evaluated triggers the data auditing rule or not; when the data to be evaluated triggers the data auditing rule, adjusting a target index value to obtain a data quality index value based on the number and importance of the triggered data auditing rule.
In one embodiment, the obtaining the blood-edge correlation table of the data asset under evaluation, implemented when the processor executes the computer program, determining the blood-edge index value of the data asset under evaluation based on the blood-edge correlation table, comprises: acquiring each data table associated with the data asset to be evaluated and a system corresponding to each data table; analyzing codes of the systems, and determining association relations of the data tables to obtain blood-margin association tables; and determining the blood margin index value of the data asset to be evaluated based on the reference times of the data asset to be evaluated in the blood margin association table.
In one embodiment, the obtaining the field and the data table of the data asset to be evaluated, which are implemented when the processor executes the computer program, determining the integrity index value of the data asset to be evaluated based on the field and the data table, includes: acquiring a field and a data table of the data asset to be evaluated; comparing the fields of the data asset to be evaluated and the data attribute of the data table with standard data attribute; and obtaining the integrity index value of the data asset to be evaluated based on the comparison result.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring a data asset to be evaluated and a value evaluation index; calculating a target index value of the data asset to be evaluated in parallel based on each value evaluation index; acquiring the weight corresponding to each value evaluation index; and obtaining the value of the data asset based on the target index value and the weight.
In one embodiment, said computing, based on each of said value assessment indicators, a target indicator value for said data asset under assessment, as implemented by a computer program when executed by a processor, comprises: acquiring metadata of the data asset to be evaluated through a first thread, and calculating a data standard index value based on the metadata; acquiring a data auditing rule through a second thread, and auditing the data asset to be evaluated based on the data auditing rule to obtain a data quality index value; determining, by a third line Cheng Huoqu, a blood-lineage correlation table for the data asset under evaluation, a blood-lineage index value for the data asset under evaluation based on the blood-lineage correlation table; determining, via a fourth line Cheng Huoqu, a field and a data table of the data asset under evaluation, an integrity indicator value for the data asset under evaluation based on the field and the data table; the first thread, the second thread, the third thread and the fourth thread are parallel threads.
In one embodiment, the obtaining metadata of the data asset under evaluation and calculating a data criteria index value based on the metadata, as implemented by a computer program when executed by a processor, comprises: acquiring a mark file of the data asset to be evaluated, and acquiring metadata from the mark file; and calculating a data standard index value based on the data attribute of the metadata.
In one embodiment, the acquiring data auditing rule implemented when the computer program is executed by the processor, and auditing the data asset to be evaluated based on the data auditing rule, to obtain a data quality index value, includes: acquiring data auditing rules obtained from each channel; judging whether the data asset to be evaluated triggers the data auditing rule or not; when the data to be evaluated triggers the data auditing rule, adjusting a target index value to obtain a data quality index value based on the number and importance of the triggered data auditing rule.
In one embodiment, the obtaining a blood-edge correlation table of the data asset under evaluation, which is implemented when the computer program is executed by the processor, determining a blood-edge index value of the data asset under evaluation based on the blood-edge correlation table, comprises: acquiring each data table associated with the data asset to be evaluated and a system corresponding to each data table; analyzing codes of the systems, and determining association relations of the data tables to obtain blood-margin association tables; and determining the blood margin index value of the data asset to be evaluated based on the reference times of the data asset to be evaluated in the blood margin association table.
In one embodiment, the obtaining the field and the data table of the data asset under evaluation, which is implemented when the computer program is executed by the processor, determining the integrity index value of the data asset under evaluation based on the field and the data table, comprises: acquiring a field and a data table of the data asset to be evaluated; comparing the fields of the data asset to be evaluated and the data attribute of the data table with standard data attribute; and obtaining the integrity index value of the data asset to be evaluated based on the comparison result.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of: acquiring a data asset to be evaluated and a value evaluation index; calculating a target index value of the data asset to be evaluated in parallel based on each value evaluation index; acquiring the weight corresponding to each value evaluation index; and obtaining the value of the data asset based on the target index value and the weight.
In one embodiment, said computing, based on each of said value assessment indicators, a target indicator value for said data asset under assessment, as implemented by a computer program when executed by a processor, comprises: acquiring metadata of the data asset to be evaluated through a first thread, and calculating a data standard index value based on the metadata; acquiring a data auditing rule through a second thread, and auditing the data asset to be evaluated based on the data auditing rule to obtain a data quality index value; determining, by a third line Cheng Huoqu, a blood-lineage correlation table for the data asset under evaluation, a blood-lineage index value for the data asset under evaluation based on the blood-lineage correlation table; determining, via a fourth line Cheng Huoqu, a field and a data table of the data asset under evaluation, an integrity indicator value for the data asset under evaluation based on the field and the data table; the first thread, the second thread, the third thread and the fourth thread are parallel threads.
In one embodiment, the obtaining metadata of the data asset under evaluation and calculating a data criteria index value based on the metadata, as implemented by a computer program when executed by a processor, comprises: acquiring a mark file of the data asset to be evaluated, and acquiring metadata from the mark file; and calculating a data standard index value based on the data attribute of the metadata.
In one embodiment, the acquiring data auditing rule implemented when the computer program is executed by the processor, and auditing the data asset to be evaluated based on the data auditing rule, to obtain a data quality index value, includes: acquiring data auditing rules obtained from each channel; judging whether the data asset to be evaluated triggers the data auditing rule or not; when the data to be evaluated triggers the data auditing rule, adjusting a target index value to obtain a data quality index value based on the number and importance of the triggered data auditing rule.
In one embodiment, the obtaining a blood-edge correlation table of the data asset under evaluation, which is implemented when the computer program is executed by the processor, determining a blood-edge index value of the data asset under evaluation based on the blood-edge correlation table, comprises: acquiring each data table associated with the data asset to be evaluated and a system corresponding to each data table; analyzing codes of the systems, and determining association relations of the data tables to obtain blood-margin association tables; and determining the blood margin index value of the data asset to be evaluated based on the reference times of the data asset to be evaluated in the blood margin association table.
In one embodiment, the obtaining the field and the data table of the data asset under evaluation, which is implemented when the computer program is executed by the processor, determining the integrity index value of the data asset under evaluation based on the field and the data table, comprises: acquiring a field and a data table of the data asset to be evaluated; comparing the fields of the data asset to be evaluated and the data attribute of the data table with standard data attribute; and obtaining the integrity index value of the data asset to be evaluated based on the comparison result.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of data asset value acquisition, the method comprising:
acquiring a data asset to be evaluated and a value evaluation index;
calculating a target index value of the data asset to be evaluated in parallel based on each value evaluation index;
acquiring the weight corresponding to each value evaluation index;
and obtaining the value of the data asset based on the target index value and the weight.
2. The method of claim 1, wherein the computing, in parallel, a target indicator value for the data asset under evaluation based on each of the value assessment indicators comprises:
acquiring metadata of the data asset to be evaluated through a first thread, and calculating a data standard index value based on the metadata;
acquiring a data auditing rule through a second thread, and auditing the data asset to be evaluated based on the data auditing rule to obtain a data quality index value;
determining, by a third line Cheng Huoqu, a blood-lineage correlation table for the data asset under evaluation, a blood-lineage index value for the data asset under evaluation based on the blood-lineage correlation table;
determining, via a fourth line Cheng Huoqu, a field and a data table of the data asset under evaluation, an integrity indicator value for the data asset under evaluation based on the field and the data table; the first thread, the second thread, the third thread and the fourth thread are parallel threads.
3. The method of claim 2, wherein the obtaining metadata of the data asset under evaluation and calculating a data criteria index value based on the metadata comprises:
Acquiring a mark file of the data asset to be evaluated, and acquiring metadata from the mark file;
and calculating a data standard index value based on the data attribute of the metadata.
4. The method of claim 2, wherein the obtaining the data auditing rule, and auditing the data asset to be evaluated based on the data auditing rule, obtains a data quality indicator value, comprises:
acquiring data auditing rules obtained from each channel;
judging whether the data asset to be evaluated triggers the data auditing rule or not;
when the data to be evaluated triggers the data auditing rule, adjusting a target index value to obtain a data quality index value based on the number and importance of the triggered data auditing rule.
5. The method of claim 2, wherein the obtaining a blood-edge correlation table of the data asset under evaluation, determining a blood-edge index value of the data asset under evaluation based on the blood-edge correlation table, comprises:
acquiring each data table associated with the data asset to be evaluated and a system corresponding to each data table;
analyzing codes of the systems, and determining association relations of the data tables to obtain blood-margin association tables;
And determining the blood margin index value of the data asset to be evaluated based on the reference times of the data asset to be evaluated in the blood margin association table.
6. The method of claim 2, wherein the obtaining the field and the data table of the data asset under evaluation, determining an integrity indicator value for the data asset under evaluation based on the field and the data table, comprises:
acquiring a field and a data table of the data asset to be evaluated;
comparing the fields of the data asset to be evaluated and the data attribute of the data table with standard data attribute;
and obtaining the integrity index value of the data asset to be evaluated based on the comparison result.
7. A data asset value acquisition device, the device comprising:
the data acquisition module is used for acquiring the data asset to be evaluated and the value evaluation index;
an index value calculation module for calculating a target index value of the data asset to be evaluated in parallel based on each value evaluation index;
the weight acquisition module is used for acquiring the weight corresponding to each value evaluation index;
and the data asset value acquisition module is used for acquiring the data asset value based on the target index value and the weight.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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