Data asset management method, control device and readable storage medium

Abstract

The present application relates to the technical field of information technology, and particularly provides a data asset management method, a control device and a readable storage medium, aiming at solving the problem of how to effectively manage data assets to improve the consistency, accuracy and management efficiency of data management. To this end, the present application can parse the data information to be managed into the form of triples, store the triples in the knowledge graph, thereby generating a data asset panoramic map of the data information, and performing data evaluation on the data asset panoramic map based on a preset rule. Through the above configuration mode, the present application can comprehensively evaluate the data information based on the data asset panoramic map, which is more conducive to discovering quality problems existing in the data, achieving effective management of the data information, and effectively improving the consistency, accuracy and management efficiency of data management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information technology, and specifically provides a data asset management method, a control device and a readable storage medium. BACKGROUND

[0002] Metadata management and data quality management are the focus and difficulty of data asset management. Metadata management includes business metadata management, technical metadata management, operation metadata management and data bloodline management. The current business data flow, technical metadata, business metadata management, operation metadata management and data bloodline management are all independent, and the proportion of manual maintenance of business metadata is very high, which seriously affects the accuracy, consistency and data management efficiency of data. At the same time, it also hinders the discovery of data quality problems, and it is difficult to find deep-seated data quality problems such as data consistency problems.

[0003] Correspondingly, there is a need in the art for a new data asset management scheme to solve the above problems. SUMMARY

[0004] In order to overcome the above defects, the present application is proposed to provide a solution or at least partially solve the problem of how to effectively manage data assets to improve the consistency, accuracy and management efficiency of data management.

[0005] In a first aspect, the present application provides a data asset management method, comprising:

[0006] acquiring data information to be managed;

[0007] parsing the data information into a form of triple;

[0008] storing the triple in a knowledge graph to generate a data asset panoramic map of the data information;

[0009] According to a preset rule, the data asset panoramic map is evaluated to realize the management of the data information

[0010] In one technical solution of the above data asset management method, the data information includes business data flow and / or data bloodline and / or business metadata and / or technical metadata and / or operation metadata.

[0011] In one technical solution of the above data asset management method, the preset rule includes a consistency rule, and the step of "evaluating the data asset panoramic map according to the preset rule" includes:

[0012] According to the consistency rule, for each node of the data asset panoramic map, a directed acyclic graph is generated according to the data bloodline;

[0013] obtain the final encoding of the node according to the directed acyclic graph;

[0014] obtain the final inconsistency problem of the data information according to the final encoding of all nodes as a data evaluation result.

[0015] In one of the technical solutions of the data asset management method, the step of "obtaining the final encoding of the node according to the directed acyclic graph" comprises:

[0016] applying a preset traversal method to traverse the directed acyclic graph to generate a pre-encoding of the node;

[0017] applying an MD5 algorithm to obtain the final encoding of the node according to the pre-encoding.

[0018] In one of the technical solutions of the data asset management method, the step of "obtaining the final inconsistency problem of the data information according to the final encoding of all nodes" comprises:

[0019] comparing the node name and the final encoding of each node with the node name and the final encoding of other nodes respectively;

[0020] when the node names of two nodes are inconsistent but the final encodings are consistent, determining as a suspected inconsistency problem;

[0021] applying a preset machine learning classification model to classify and filter the nodes corresponding to the suspected inconsistency problem to obtain the final inconsistency problem;

[0022] The machine learning classification model is constructed according to the node features of the nodes; the node features at least include the link update frequency and / or usage frequency and / or data source corresponding to the nodes.

[0023] In one of the technical solutions of the data asset management method, the preset rule comprises an accuracy rule, and the step of "performing data evaluation on the data asset panoramic map according to the preset rule" comprises:

[0024] determining the consistency between the business data flow topology and the corresponding data bloodline topology in the data asset panoramic map according to the accuracy rule;

[0025] obtaining the number of inconsistent edges between the business data flow topology and the corresponding data bloodline topology as a data evaluation result according to the determination result.

[0026] In one of the technical solutions of the data asset management method, the step of "storing the triplets in the knowledge graph to generate the data asset panoramic map of the data information" comprises:

[0027] store the triplets in a knowledge graph, and construct a dependency relationship tree of the data information according to the business data flow and the data bloodline;

[0028] According to the business metadata and the dependency relationship tree, the subsequent nodes of the dependency relationship tree inherit the business metadata from the predecessor nodes to generate a data asset panoramic map of the data information.

[0029] The node attributes of the data asset panoramic map are stored in the knowledge graph or a relational database, and the node attributes are obtained according to the triplets of the technical metadata and / or the operation metadata.

[0030] In one of the technical solutions of the above data asset management method, the step of "analyzing the data information into a triplet form" comprises:

[0031] According to a preset analysis rule, a data warehouse technology is applied to analyze the SQL syntax tree of the data bloodline to obtain the triplet of the data bloodline; and / or,

[0032] The business metadata and / or the technical metadata and / or the operation metadata are respectively saved in a triplet form; and / or,

[0033] The business data flow is saved in a triplet form.

[0034] In a second aspect, a control device is provided, which comprises a processor and a storage device, the storage device is adapted to store a plurality of program codes, the program codes are adapted to be loaded and run by the processor to execute the data asset management method of any one of the technical solutions of the above data asset management method.

[0035] In a fourth aspect, a computer readable storage medium is provided, which stores a plurality of program codes, the program codes are adapted to be loaded and run by a processor to execute the data asset management method of any one of the technical solutions of the above data asset management method.

[0036] The above one or more technical solutions of the present application have at least one or more of the following advantages

[0037] Advantages:

[0038] In the technical solutions of the present application, the data information to be managed can be parsed into the form of triples, and the triples are stored in a knowledge graph, so as to generate a data asset panoramic map of the data information, and perform data evaluation on the data asset panoramic map based on preset rules. Through the above configuration mode, the present application can comprehensively evaluate the data information based on the data asset panoramic map, which is more conducive to discovering quality problems existing in the data, achieving effective management of the data information, and effectively improving the consistency, accuracy and management efficiency of data management. BRIEF DESCRIPTION OF DRAWINGS

[0039] The disclosure of the present application will become more apparent with reference to the drawings. It should be understood by those skilled in the art that the drawings are only for illustrative purposes, and are not intended to limit the scope of protection of the present application. Among them:

[0040] Figure 1 is a main step flow diagram of a data asset management method according to an embodiment of the present application;

[0041] Figure 2 is a main step flow diagram of a data asset management method according to an embodiment of the present application;

[0042] Figure 3 is a schematic diagram of a data asset panoramic map according to an example of an embodiment of the present application. DETAILED DESCRIPTION

[0043] Some embodiments of the present application will be described below with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application, and are not intended to limit the scope of protection of the present application.

[0044] In the description of the present application, "module" and "processor" can include hardware, software or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include a software portion such as program code, and can be a combination of software and hardware. The processor can be a central processor, a microprocessor, a graphic processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor can be implemented in software, hardware or a combination of both. The non-transitory computer readable storage medium includes any suitable medium that can store program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B or both A and B. The term "at least one of A or B" or "at least one of A and B" has a similar meaning as "A and / or B" and can include only A, only B or both A and B. The singular form of the term "one", "this" can also include the plural form.

[0045] Some terms related to the present application are explained here.

[0046] Metadata, data about data, is mainly information describing data properties, which is used to support functions such as indicating storage location, historical data, resource search, file record, etc. Metadata can include three basic types: business metadata, technical metadata and operational metadata. Among them, business metadata describes the business meaning and business rules of data. Technical metadata is structured data processed for convenience of computer or database identification, storage, transmission and conversion of data. Operational metadata describes the operational properties of data, including management department, management responsibility person, operation time, etc.

[0047] Business data flow is a sequence of ordered, starting and ending byte data. It includes input flow and output flow.

[0048] Data lineage refers to the link relationship between data.

[0049] Triple refers to a set like (x, y, z). It is mainly used to store a compressed form of sparse matrix.

[0050] Knowledge graph is a graph-based data structure composed of nodes and edges. Each node represents an "entity", and each edge represents the "relationship" between entities. Knowledge graph is essentially a semantic network.

[0051] Data asset panoramic map refers to a systematic management mode of data assets in a global, flowing and structured mode.

[0052] Reference Signs List Figure 1 , Figure 1 is a main step flow schematic diagram of a data asset management method according to an embodiment of the present application. As shown in Figure 1 , the data asset management method in the embodiment of the present application mainly includes the following steps S101-S104.

[0053] Step S101: obtaining data information to be managed.

[0054] In the embodiment, the data information to be managed can be obtained first.

[0055] In an embodiment, the source of the data information can be a database operation log, a data warehouse design draft (including a data model, a data flow diagram), a data warehouse development log (including SQL code of etl (Extract-Transform-Load, data warehouse technology) in the data warehouse).

[0056] In an embodiment, the data information to be managed can include business data flow, data bloodline, business metadata of source data, technical metadata, operation metadata, etc., and can also include etl code, etc.

[0057] In an embodiment, the business data flow can be derived from system uploading, manual input, etc. The technical metadata can be derived from data automatically obtained by the system from the target database and data uploaded by the system. The operation metadata can be derived from data automatically obtained by the system from the target database operation log. The business source data of the source data can be derived from system uploading, and the etl code can be derived from the system.

[0058] In an embodiment, the statistical method of the operation metadata can be to count the latest modifier, the latest modification date, etc. from the target operation log.

[0059] Step S102: parsing the data information into a triple form.

[0060] In the embodiment, the data information can be parsed into a triple form.

[0061] In an embodiment, the SQL syntax tree of the data bloodline can be parsed by applying the data warehouse technology according to a preset parsing rule to obtain the triple of the data bloodline. That is, the parsing rule can be preset, the SQL syntax tree is parsed according to the etl process, and the triple form is matched and parsed based on the parsing rule.

[0062] The process of obtaining the triple of the data bloodline will be described below through an example:

[0063] Suppose table_a has two fields id, num, table_b has id field

[0064] etl code insert into table_a(num) values(select count(distinct(id)) from table_b where id>1);

[0065] parsed as (num blood relation_1, id)

[0066] (blood relation_1, attribute, count(distinct(id)))

[0067] In an embodiment, the business metadata, technical metadata and operation metadata, and data blood relation can be saved in the form of triplets respectively.

[0068] The technical metadata of table_a is

[0069] (table_a, contains, id)

[0070] (table_a, contains, num).

[0071] The attribute of id is (constraint: not null, type: varchar, length: 512, data source: xx system, last modifier: xxx, last modification time: 2022-10-01).

[0072] The attribute of num is (constraint: not null, type: int, data source: xx system, last modifier: xxx, last modification time: 2022-10-01).

[0073] The business data flow can be saved in the form of triplet (num, data flow_1, id).

[0074] Step S103: store the triplet into the knowledge graph to generate a data asset panoramic map of data information.

[0075] In this embodiment, the triplet obtained in step S102 can be stored in the knowledge graph to construct a data asset panoramic map.

[0076] In an embodiment, the node attribute of the data asset panoramic map can be saved in the knowledge graph.

[0077] In an embodiment, the node attribute of the data asset panoramic map can be saved in the relational database.

[0078] In an embodiment, the node attribute of the data asset panoramic map can be obtained according to the triplet of technical metadata and operation metadata.

[0079] Step S104: According to the preset rule, data evaluation is performed on the data asset panoramic map to realize management of the data information.

[0080] In the embodiment, the preset rule for data evaluation can be set, and the preset rule is applied to perform data evaluation on the data asset panoramic map, thereby realizing management of the data information.

[0081] In one embodiment, the preset rule can include a consistency rule and an accuracy rule. The consistency rule is a rule for discovering consistency problems of the data information according to the data bloodline. The accuracy rule is a rule for judging accuracy problems of the data information according to the consistency of the data bloodline and the business data flow.

[0082] Based on the above steps S101-S104, the data information to be managed can be parsed into a form of a triple, and the triple is stored in a knowledge graph, thereby generating a data asset panoramic map of the data information, and performing data evaluation on the data asset panoramic map based on the preset rule. Through the above configuration, the embodiment of the present application can comprehensively evaluate the data information based on the data asset panoramic map, which is more conducive to discovering quality problems existing in the data, realizing effective management of the data information, and effectively improving the consistency, accuracy and management efficiency of data management.

[0083] The steps S103 and S104 will be further described below.

[0084] In one embodiment of the present application, the step S103 can further include the following steps S1031 and S1032:

[0085] Step S1031: The triple is stored in the knowledge graph, and a dependency relationship tree of the data information is constructed according to the business data flow and the data bloodline.

[0086] Step S1032: According to the business metadata and the dependency relationship tree, the subsequent nodes of the dependency relationship tree inherit the business metadata from the previous nodes to generate the data asset panoramic map of the data information.

[0087] In the embodiment, the parsed triple of the data information can be stored in the knowledge graph to construct the dependency relationship tree of the data information according to the business data flow and the data bloodline, and based on the business metadata and the dependency relationship tree, the subsequent nodes of the dependency relationship data inherit the business metadata from the previous nodes, thereby generating the data asset panoramic map of the data information. In this way, the business metadata can be automatically generated through the data bloodline, and the efficiency can be effectively improved.

[0088] Please refer to the attachedFigure 3 , Figure 3 is a schematic diagram of a data asset panoramic map according to an example of an embodiment of the present application. As shown in Figure 3 table_a contains id, num two fields, table_b contains id field, the num field of table_a and the id field of table_b exist data data blood relationship (blood_1) and business data flow (data flow) relationship, blood_1 and data flow are both from id to num.

[0089] In an embodiment of the present application, step S104 can further include the following steps S1041 and S1043:

[0090] Step S1041: according to the consistency rule, for each node of the data asset panoramic map, a directed acyclic graph is generated according to the data blood relationship.

[0091] In this embodiment, each node in the data asset panoramic map can be generated according to the data blood relationship. The directed acyclic graph refers to a loop-free directed graph, which can reflect the data blood relationship between the current node and other nodes.

[0092] Step S1042: obtaining the final encoding of the node according to the directed acyclic graph.

[0093] In this embodiment, step S1042 can further include the following steps S10421 and S10422:

[0094] Step S10421: applying a preset traversal method, traversing the directed acyclic graph to generate the pre-encoding of the node.

[0095] In this embodiment, the directed acyclic graph can be traversed to obtain the pre-encoding of the node.

[0096] In an embodiment, the BFS (Breadth First Search) traversal method can be used to traverse the directed acyclic graph.

[0097] In an embodiment, the DFS (Depth First Search) traversal method can be used to traverse the directed acyclic graph.

[0098] Step S10422: applying MD5 algorithm, obtaining the final encoding of the node according to the pre-encoding.

[0099] In the embodiment, the MD5 algorithm (Message-Digest Algorithm) can be applied to obtain the final encoding of the node according to the pre-encoding. The MD5 algorithm can generate a 128-bit hash value based on the input, which is used to ensure the integrity of information transmission.

[0100] Step S1043: According to the final encoding of all nodes, the final inconsistency problem of the data information is obtained as the data evaluation result.

[0101] In the embodiment, step S1043 can further include steps S10431-S10433:

[0102] Step S10431: The node name and the final encoding of each node are compared with the node name and the final encoding of other nodes, respectively.

[0103] Step S10432: When the node names of two nodes are inconsistent but the final encodings are consistent, it is determined as a suspected inconsistency problem.

[0104] Step S10433: For the node corresponding to the suspected inconsistency problem, a preset machine learning classification model is applied to classify and screen the node to obtain the final inconsistency problem; wherein the machine learning classification model is constructed according to the node features of the node; the node features at least include the link update frequency and / or usage frequency and / or data source corresponding to the node.

[0105] In the embodiment, all nodes can be traversed, and when the node names of two nodes are inconsistent but the final encodings are consistent, it can be determined as a suspected inconsistency problem; for the suspected inconsistency problem, a machine learning classification model can be applied to classify and screen it, so as to obtain the final inconsistency problem and take the final inconsistency problem as the data evaluation result.

[0106] In one embodiment, the machine learning classification model can be a model constructed based on the GBDT (Gradient Boosting Decision Tree) algorithm, a model constructed based on the LR (Linear Regression) algorithm, a model constructed based on the GBDT and LR algorithms, or a model constructed based on other machine learning algorithms.

[0107] In one embodiment of the embodiment of the application, step S104 can further include steps S1044 and S1045:

[0108] Step S1044: According to the accuracy rule, the consistency between the business data flow topology and the corresponding data blood relationship topology in the data asset panoramic map is judged.

[0109] Step S1045: According to the judgment result, the number of inconsistent edges between the business data flow topology and the corresponding data blood relationship topology is obtained as the data evaluation result.

[0110] In this embodiment, the consistency of the business data flow topology and the data blood relationship topology in the data asset panoramic map can be judged, so that the number of inconsistent edges between the business data flow topology and the data blood relationship topology is obtained as the data evaluation result according to the judgment result.

[0111] In the data asset panoramic map, the business data flow topology is a pre-designed topology, and the data blood relationship topology is an actually occurring topology. If the two are inconsistent, it means that there is a problem of accuracy, and the number of inconsistent edges can be recorded as the data evaluation result.

[0112] In one embodiment, the data evaluation result obtained based on the consistency planning and the accuracy rule can be fed back and reported to analyze the deep-seated problems of the data information. The data evaluation result can also be displayed, and the data information can be scored based on the data evaluation result.

[0113] In one embodiment, reference can be made to the accompanying drawings Figure 2 , Figure 2 is a schematic diagram of the main steps of a data asset management method according to an embodiment of an embodiment of the present application. As shown in Figure 2 , the data asset management method can include the following steps S201 to S204:

[0114] Step S201: Obtain data information.

[0115] In this embodiment, the method of step S201 is similar to step S101, and for the sake of simplicity, it will not be described here.

[0116] Step S202: Analyze the data information into a triple form.

[0117] In this embodiment, the method of step S202 is similar to step S102, and for the sake of simplicity, it will not be described here.

[0118] Step S203: Construct a panoramic data map (data asset panoramic map) based on a knowledge graph.

[0119] In this embodiment, the method of step S203 is similar to step S103, and for the sake of simplicity, it will not be described here.

[0120] Step S204: establishing quality inspection rules by using an algorithm.

[0121] In the embodiment, the panoramic data map can be evaluated by using an algorithm to establish quality inspection rules (preset rules).

[0122] It should be noted that, although the steps in the above embodiments are described in a specific order, those skilled in the art can understand that, in order to achieve the effect of the present application, the different steps do not have to be executed in such an order, they can be executed simultaneously (in parallel) or in other orders, and these changes are within the protection scope of the present application.

[0123] Those skilled in the art can understand that all or part of the processes in the method of the above embodiment can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable storage medium can include any entity or device, medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable storage medium does not include electrical carrier signals and telecommunication signals.

[0124] Further, the present application also provides a control device. In an embodiment of the control device according to the present application, the control device includes a processor and a storage device, the storage device can be configured to store a program for executing the data asset management method of the above method embodiments, and the processor can be configured to execute the program in the storage device, which includes but is not limited to the program for executing the data asset management method of the above method embodiments. For the convenience of description, only the parts related to the embodiments of the present application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the present application. The control device can be a control device device formed by various electronic devices.

[0125] Further, the present application also provides a computer readable storage medium. In an embodiment of the computer readable storage medium according to the present application, the computer readable storage medium can be configured to store a program of the data asset management method of the above-mentioned method embodiment, which can be loaded and run by the processor to implement the above-mentioned data asset management method. For the convenience of description, only the parts related to the embodiments of the present application are shown, and the specific technical details are not disclosed. Please refer to the method part of the embodiments of the present application. The computer readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer readable storage medium in the embodiments of the present application is a non-transitory computer readable storage medium.

[0126] Further, it should be understood that, since the setting of each module is only for illustrating the functional units of the device of the present application, the corresponding physical device of the module can be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of the software and the hardware. Therefore, the number of each module in the figure is only illustrative.

[0127] Those skilled in the art can understand that each module in the device can be adaptively split or combined. Such splitting or combining of the specific module does not cause the technical solution to deviate from the principles of the present application, therefore, the technical solution after splitting or combining will fall within the protection scope of the present application.

[0128] So far, the technical solution of the present application has been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without deviating from the principles of the present application, and the technical solution after the changes or replacements will fall within the protection scope of the present application.

Claims

1. A data asset management method characterized by, The method comprises: acquiring data information to be managed; parsing the data information into a form of triples; storing the triples in a knowledge graph to generate a data asset panoramic map of the data information; performing data evaluation on the data asset panoramic map according to preset rules to realize management of the data information; the data information comprises business data flow and / or data blood relationship and / or business metadata and / or technical metadata and / or operation metadata; the preset rules comprise a consistency rule, and the step of "performing data evaluation on the data asset panoramic map according to preset rules" comprises: according to the consistency rule, for each node of the data asset panoramic map, a directed acyclic graph is generated according to the data blood relationship; according to the directed acyclic graph, the final encoding of the node is acquired; according to the final encoding of all nodes, the final inconsistency problem of the data information is acquired as a data evaluation result; the step of "according to the final encoding of all nodes, acquiring the final inconsistency problem of the data information" comprises: comparing the node name and final encoding of each node with the node name and final encoding of other nodes respectively; when the node names of two nodes are inconsistent but the final encodings are consistent, it is determined as a suspected inconsistency problem; for the node corresponding to the suspected inconsistency problem, a preset machine learning classification model is applied to classify and filter the node to obtain a final inconsistency problem; wherein the machine learning classification model is constructed according to node features of the node; the node features at least comprise link update frequency and / or usage frequency and / or data source corresponding to the node.

2. The data asset management method of claim 1, wherein, the step of "according to the directed acyclic graph, acquiring the final encoding of the node" comprises: applying a preset traversal method to traverse the directed acyclic graph to generate a pre-encoding of the node; applying an MD5 algorithm to acquire the final encoding of the node according to the pre-encoding.

3. The data asset management method of claim 1, wherein, the preset rules comprise an accuracy rule, and the step of "performing data evaluation on the data asset panoramic map according to preset rules" comprises: according to the accuracy rule, judging the consistency between the business data flow topology and the corresponding data blood relationship topology in the data asset panoramic map; according to the judgment result, the number of inconsistent edges between the business data flow topology and the corresponding data blood relationship topology is acquired as a data evaluation result.

4. The data asset management method of claim 1, wherein, the step of "storing the triples in a knowledge graph to generate a data asset panoramic map of the data information" comprises: storing the triples in a knowledge graph, constructing a dependency relationship tree of the data information according to the business data flow and the data blood relationship; according to the business metadata and the dependency relationship tree, realizing inheritance of the business metadata from a previous node to a subsequent node of the dependency relationship tree to generate a data asset panoramic map of the data information; wherein the node attributes of the data asset panoramic map are stored in the knowledge graph or a relational database; the node attributes are obtained according to triples of the technical metadata and / or the operation metadata.

5. The data asset management method of claim 1, wherein, The step of "resolving the data information into a form of triple" comprises: According to a preset resolution rule, a data warehouse technology is applied to resolve the SQL syntax tree of the data bloodline to obtain a triple of the data bloodline; and / or, The business metadata and / or the technical metadata and / or the operation metadata are respectively saved in a form of triple; and / or, The business data flow is saved in a form of triple.

6. A control device comprising a processor and a storage device, the storage device being adapted to store a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to execute the data asset management method in any one of claims 1 to 5.

7. A computer readable storage medium having stored therein a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to execute the data asset management method in any one of claims 1 to 5.

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