CN117422327A - Data management capability evaluation method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the disclosure provides a data management capability assessment method, a device, equipment and a storage medium, which are applied to the technical field of data management. The method comprises the steps of obtaining to-be-evaluated data of a target enterprise; the data to be evaluated comprises a plurality of data dimensions, initial weights of the data dimensions and scores of the data dimensions; according to the initial weight, calculating the weight of the data dimension based on a preset formula; calculating to obtain a score of the corresponding data dimension according to the weight and the score; determining a total score of the data to be evaluated according to the sum value of the scores; and determining the data management capability of the target enterprise according to the total score. In this way, each key dimension of enterprise data management can be comprehensively considered, and the weight of the index can be adjusted according to the specific requirements of the enterprise, so that the objectivity and reliability of the evaluation result are ensured.
Description
Technical Field
The present disclosure relates to the field of data management technologies, and in particular, to a method, an apparatus, a device, and a storage medium for evaluating data management capability.
Background
In the context of information technology and economic society, explosive growth of data has become a common phenomenon. With the continuous accumulation and application of data, data has become a national strategic resource and has a critical impact on the global industry, economy and society. However, there are data management challenges that come with ever increasing scale and complexity. In the modern digital age, data has become a core resource for enterprises, and thus, data management capability has become an essential element in enterprise work. The data management capability is to guarantee the quality, safety and reliability of data and the like so as to meet the requirements and targets of enterprises. The good data management capability can enable enterprises to strengthen collaboration among departments through data exchange, sharing and use, and ensure the service quality of the enterprises.
Problems and defects existing in the prior art are as follows:
(1) Lack of comprehensiveness: existing approaches tend to focus only on certain aspects of data management, without fully considering the individual critical dimensions of enterprise data management. This results in the evaluation result being likely to be one-sided or incomplete, failing to provide an accurate assessment of the overall data management capabilities of the enterprise.
(2) Lack of personalization: the conventional method generally adopts a fixed index system and weight distribution, and cannot be adjusted in a personalized way according to specific requirements of enterprises and service importance. The evaluation result and the actual situation of the enterprise have deviation, and the applicability and the accuracy of the evaluation method are limited.
(3) Lack of quantization index: existing methods often rely on subjective assessment or qualitative descriptions, lacking specific quantitative indicators to measure the data management capabilities of an enterprise. Such assessment results may lack objectivity and comparability, and it may be difficult to provide an enterprise with a clear direction and priority of improvement.
Disclosure of Invention
The present disclosure provides a data management capability assessment method, apparatus, device, and storage medium.
According to a first aspect of the present disclosure, a data management capability assessment method is provided. The method comprises the following steps:
acquiring to-be-evaluated data of a target enterprise; the data to be evaluated comprises a plurality of data dimensions, initial weights of the data dimensions and scores of the data dimensions;
according to the initial weight, calculating the weight of the data dimension based on a preset formula;
calculating to obtain a score of the corresponding data dimension according to the weight and the score;
determining a total score of the data to be evaluated according to the sum value of the scores;
and determining the data management capability of the target enterprise according to the total score.
In the aspect and any possible implementation manner as described above, there is further provided an implementation manner, before calculating the weight of the data dimension based on a preset formula according to the initial weight, including:
acquiring important coefficients corresponding to a plurality of data dimensions;
sequencing a plurality of data dimensions according to the important coefficients to generate a data dimension sequence;
in the aspect and any possible implementation manner described above, there is further provided an implementation manner, where the calculating, according to the initial weight, a weight value of the data dimension based on a preset formula includes:
according to the initial weight, calculating the weight of the last data dimension in the data dimension sequence based on a preset formula;
and calculating to obtain the weight of the previous data dimension according to the weight of the last data dimension and the initial weight of the last data dimension.
Aspects and any one of the possible implementations as described above, further provides an implementation,
the data to be evaluated further comprises a sequence number corresponding to the data dimension;
the sequence number is generated according to the data dimension sequence;
the preset formula is as follows:
where k represents the sequence number of the data dimension, ri represents the initial weight,the weight representing the last data dimension and n represents the maximum value of the sequence number of the data dimension.
Aspects and any one of the possible implementations as described above, further providing an implementation, the method further including:
comparing the score with an average value, wherein the average value is obtained by averaging scores of the same data dimension as the data dimension corresponding to the score in the selected multiple enterprises;
and if the score is lower than the average value, the data dimension corresponding to the score is a weak item of the data management capability of the target enterprise.
Aspects and any one of the possible implementations as described above, further providing an implementation, the method further including:
and outputting corresponding data dimension and performing early warning in response to the score being lower than the average value, so that the manager of the target enterprise can strengthen data management according to the output data dimension.
According to a second aspect of the present disclosure, there is provided a data management capability assessment apparatus. The device comprises:
the data acquisition module is used for acquiring the data to be evaluated of the target enterprise; the data to be evaluated comprises a plurality of data dimensions, initial weights of the data dimensions and scores of the data dimensions;
the weight calculation module is used for calculating the weight of the data dimension based on a preset formula according to the initial weight;
the scoring calculation module is used for calculating and obtaining the score of the corresponding data dimension according to the weight and the score;
the score calculating module is used for determining the total score of the data to be evaluated according to the sum value of the scores;
and the assessment module is used for determining the data management capacity of the target enterprise according to the total score.
According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method as described above when executing the program.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method according to the first aspect of the present disclosure.
The embodiment of the disclosure provides a data management capability assessment method, a device, equipment and a storage medium, designs a comprehensive, personalized and quantitative assessment method, can comprehensively consider each key dimension of enterprise data management, and adjusts the weight of an index according to specific requirements of an enterprise. In addition, the method combines expert opinion and actual data to ensure the objectivity and reliability of the evaluation result.
It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. For a better understanding of the present disclosure, and without limiting the disclosure thereto, the same or similar reference numerals denote the same or similar elements, wherein:
FIG. 1 illustrates a flow chart of a data management capability assessment method according to an embodiment of the present disclosure;
FIG. 2 illustrates a block diagram of a data management capability assessment device according to an embodiment of the present disclosure;
fig. 3 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
In this disclosure, data dimensions that are eight key from data strategy, data governance, data architecture, data application, data security, data quality, data standard, and data lifecycle are expanded. Each dimension represents an important aspect of data management and is supported by several capability items, such a design ensuring a comprehensive assessment of enterprise data management capabilities. The most critical of these is the determination of the weight of each data dimension, which is considered based on the relative importance of the data dimension and the degree of contribution to the enterprise data management capability. The influence degree of different data dimensions on the overall data management capacity can be reflected more accurately through reasonable weight distribution. In the final evaluation process, the score of each data dimension is multiplied by the corresponding weight, and the scores are accumulated and summed, so that the comprehensive score of the whole index weight system is obtained. This composite score will reflect the composite performance of the enterprise in terms of data management capabilities and provide a comparable index to the enterprise for internal and external assessment.
Fig. 1 shows a flowchart of a data management capability assessment method 100 according to an embodiment of the present disclosure. The method 100 comprises the following steps:
step 110, obtaining to-be-evaluated data of a target enterprise; the data to be evaluated includes a plurality of data dimensions, initial weights for the data dimensions, and scores for the data dimensions.
In some embodiments, in performing enterprise data management capability assessment, the first step is to invite experts familiar with each data dimension area that will be responsible for scoring several capability items in each data dimension and determining the capability level to which each capability item belongs according to predefined capability level criteria (initial, managed, robust, quantitative management, optimization). And then calculating the score of the corresponding data dimension through the scores of the capability level and the capability item. For example, if an enterprise has clear data strategic goals in strategic targeting, and these goals are consistent with business goals, it may be rated as a robust Level (Level 3). The data table of the capability item corresponding to each data dimension is as follows:
for example, according to the capability items under the data strategy: after the weights of the capacity items of the three data strategy planning, the data strategy implementation and the data strategy evaluation are determined, the scores of the capacity items of the data strategy planning, the data strategy implementation and the data strategy evaluation are multiplied by the weights to obtain the corresponding scores of the capacity items, and then the scores of the data dimension of the data strategy are summed.
In some embodiments, before calculating the weight of the data dimension based on a preset formula according to the initial weight, the method includes:
and acquiring important coefficients corresponding to the plurality of data dimensions, and sequencing the plurality of data dimensions according to the important coefficients to generate a data dimension sequence.
In some embodiments, the important coefficients of the eight data dimensions of the data strategy, data governance, data architecture, data application, data security, data quality, data standard, and data lifecycle are obtained. The importance coefficient is obtained by weighting average or other suitable method after scoring the eight data dimensions by combining business characteristics and industry characteristics of the target enterprise by a plurality of experts. The importance coefficient obtained in this way is matched with the business of the target enterprise, and the importance degree of the data to be evaluated of the target enterprise in the eight data dimension levels can be reflected at best, so that the whole data management capability of the enterprise can be evaluated accurately and comprehensively.
In some embodiments, the eight data dimensions, e.g., data strategy, data governance, data architecture, data application, data security, data quality, data standard, and data lifecycle, are represented as: x is x 1 、x 2 ...x 8 The data dimension sequence obtained after sequencing according to the size of the important coefficients is as follows:where ">" means that the importance coefficient is greater than or equal to, i.e., the importance of the data dimension on the left is higher than the data dimension on the right, or the data dimension on the left is equally important as the data dimension on the right.
In some embodiments, the initial weights are assigned by an expert toFor example, if->And->Equally important, then->Initial weight r of (2) 1 =1.0; if->Ratio->Slightly important, then->Initial weight r of (2) 1 =1.2; if->Ratio->Obviously important, then->Initial weight r of (2) 1 =1.4; if->Ratio->Strongly important, then->Initial weight r of (2) 1 =1.6; if it isRatio->Is important at the end, then->Initial weight r of (2) 1 =1.8. Alternatively, after the assignment score is made by a plurality of experts, an average value may be obtained, and the obtained average value may be used as the initial weight. For initial weights of the last data dimension in the sequence of data dimensions, e.g. +.>Initial weight r of (2) 8 According to->Ratio->Is assigned to the important program of (1)>Initial weight r of (2) 7 Then, calculate +_or vice versa>Initial weight r of (2) 8 ＝1/r 7 In addition, for the initial weight of each data dimension, after the assignment scoring is performed by a plurality of experts, an average value is obtained, and the obtained average value is used as the initial weight of the corresponding data dimension.
In some embodiments, when the data management capability of the target enterprise needs to be evaluated, multiple data dimensions of the data to be evaluated, initial weights corresponding to the data dimensions, and scores corresponding to the data dimensions need to be obtained.
Step 120, calculating the weight of the data dimension based on a preset formula according to the initial weight.
In some embodiments, according to the initial weight, calculating a weight of a last data dimension in the sequence of data dimensions based on a preset formula; and calculating to obtain the weight of the previous data dimension according to the weight of the last data dimension and the initial weight of the last data dimension.
In some embodiments, the data to be evaluated further includes a sequence number corresponding to the data dimension; the sequence number is generated according to the data dimension sequence; the preset formula is as follows:
where k represents the sequence number of the data dimension, ri represents the initial weight,the weight representing the last data dimension and n represents the maximum value of the sequence number of the data dimension.
In some embodiments, the sequence of data dimensions described above is:for example, according to the above formula, first, the +.>Weight of +.>Then based on the calculated weight +.>Initial weight r 8 Multiplying the two to obtain +.>Weight of +.>And so on. By the specific amountThe method (the formula) makes the calculated weight more objective.
And 130, calculating to obtain the corresponding data dimension scores according to the weight values and the scores.
In some embodiments, the corresponding score and weight for the data dimension are multiplied, and the score for the data dimension is calculated, e.g., the data dimension is calculatedWeight of +.>And score a of 8 Multiplying to obtain data dimension +.>Score z of (2) 8 And similarly, scoring corresponding to the other seven data dimensions respectively.
And step 140, determining the total score of the data to be evaluated according to the sum value of the scores.
In some embodiments, the scores of the eight data dimensions are summed to obtain a sum value that is the total score of the data to be evaluated.
And step 150, determining the data management capability of the target enterprise according to the total score.
In some embodiments, the data management capabilities of the target enterprise may be determined based on the total score obtained in step 140. For example, a score below 60 is poor and a score above 95 is excellent. The total score can be represented in a numerical form, can be displayed to a target enterprise in a form of a chart or report and the like, reflects the comprehensive performance of the enterprise in terms of data management capability, can help the enterprise to know the comprehensive performance of the enterprise in terms of data management capability, and provides guidance for improvement and promotion so as to evaluate the inside and the outside.
Based on the foregoing embodiment, the method of a further embodiment provided in the present disclosure further includes: comparing the score with an average value, wherein the average value is obtained by averaging scores of the same data dimension as the data dimension corresponding to the score in the selected multiple enterprises; and if the score is lower than the average value, the data dimension corresponding to the score is a weak item of the data management capability of the target enterprise.
In some embodiments, an average value of scores of data dimensions of enterprises highly related to a target enterprise is obtained for each data dimension, and then the scores of the data dimensions of the target enterprise are compared with the average value, so that the target enterprise can determine whether the data management capacity of the enterprise in the data dimension is lower than the industry average level according to the comparison result, and if the data management capacity is lower than the industry average level, the data management in the data dimension is a weak item of the target enterprise. Of course, the average value can be set independently according to the management requirement of the target enterprise, for example, the key management on the data management of a certain data dimension can be set to be higher than the average value of the industry average level, so that the customization and personalized management of the enterprise can be achieved.
Based on the foregoing embodiment, the method of a further embodiment provided in the present disclosure further includes: and outputting corresponding data dimension and performing early warning in response to the score being lower than the average value, so that the manager of the target enterprise can strengthen data management according to the output data dimension.
In some embodiments, when the score of a certain data dimension is lower than the average value, early warning is performed to help the manager of the target enterprise to timely and purposefully strengthen the data management of the data dimension, so as to improve the overall data management capability level of the enterprise.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.
Fig. 2 shows a block diagram of a data management capability assessment device 200 according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus 200 includes:
a data acquisition module 210, configured to acquire to-be-evaluated data of a target enterprise; the data to be evaluated comprises a plurality of data dimensions, initial weights of the data dimensions and scores of the data dimensions;
the weight calculation module 220 is configured to calculate, according to the initial weight, a weight of the data dimension based on a preset formula;
the score calculating module 230 is configured to calculate a score of the corresponding data dimension according to the weight and the score;
a score calculating module 240, configured to determine a total score of the data to be evaluated according to a sum of the scores;
and the assessment module 250 is used for determining the data management capability of the target enterprise according to the total score.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
According to an embodiment of the disclosure, the disclosure further provides an electronic device, a readable storage medium.
Fig. 3 shows a schematic block diagram of an electronic device 300 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
The electronic device 300 includes a computing unit 301 that can perform various appropriate actions and processes according to a computer program stored in a ROM302 or a computer program loaded from a storage unit 308 into a RAM 303. In the RAM303, various programs and data required for the operation of the electronic device 300 may also be stored. The computing unit 301, the ROM302, and the RAM303 are connected to each other by a bus 304. I/O interface 305 is also connected to bus 304.
Various components in the electronic device 300 are connected to the I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, etc.; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, an optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 301 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the respective methods and processes described above, such as the data management capability evaluation method. For example, in some embodiments, the data management capability assessment method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 300 via the ROM302 and/or the communication unit 309. When the computer program is loaded into the RAM303 and executed by the computing unit 301, one or more steps of the data management capability evaluation method described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the data management capability assessment method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (9)
1. A data management capability assessment method, comprising:
acquiring to-be-evaluated data of a target enterprise; the data to be evaluated comprises a plurality of data dimensions, initial weights of the data dimensions and scores of the data dimensions;
according to the initial weight, calculating the weight of the data dimension based on a preset formula;
calculating to obtain a score of the corresponding data dimension according to the weight and the score;
determining a total score of the data to be evaluated according to the sum value of the scores;
and determining the data management capability of the target enterprise according to the total score.
2. The method of claim 1, comprising, prior to calculating the weight of the data dimension based on a preset formula according to the initial weight:
acquiring important coefficients corresponding to a plurality of data dimensions;
and sequencing the plurality of data dimensions according to the important coefficients to generate a data dimension sequence.
3. The method of claim 2, wherein the calculating the weight of the data dimension based on a preset formula according to the initial weight comprises:
according to the initial weight, calculating the weight of the last data dimension in the data dimension sequence based on a preset formula;
and calculating to obtain the weight of the previous data dimension according to the weight of the last data dimension and the initial weight of the last data dimension.
4. The method of claim 3, wherein the step of,
the data to be evaluated further comprises a sequence number corresponding to the data dimension;
the sequence number is generated according to the data dimension sequence;
the preset formula is as follows:
where k represents the sequence number of the data dimension, r i The initial weight is indicated as being indicative of the initial weight,the weight representing the last data dimension and n represents the maximum value of the sequence number of the data dimension.
5. The method according to claim 1, wherein the method further comprises:
comparing the score with an average value, wherein the average value is obtained by averaging scores of the same data dimension as the data dimension corresponding to the score in the selected multiple enterprises;
and if the score is lower than the average value, the data dimension corresponding to the score is a weak item of the data management capability of the target enterprise.
6. The method of claim 5, wherein the method further comprises:
and outputting corresponding data dimension and performing early warning in response to the score being lower than the average value, so that the manager of the target enterprise can strengthen data management according to the output data dimension.
7. A data management capability assessment apparatus, comprising:
the data acquisition module is used for acquiring the data to be evaluated of the target enterprise; the data to be evaluated comprises a plurality of data dimensions, initial weights of the data dimensions and scores of the data dimensions;
the weight calculation module is used for calculating the weight of the data dimension based on a preset formula according to the initial weight;
the scoring calculation module is used for calculating and obtaining the score of the corresponding data dimension according to the weight and the score;
the score calculating module is used for determining the total score of the data to be evaluated according to the sum value of the scores:
and the assessment module is used for determining the data management capacity of the target enterprise according to the total score.
8. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
9. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-6.
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| CN202311301185.5A CN117422327A (en) | 2023-10-09 | 2023-10-09 | Data management capability evaluation method, device, equipment and storage medium |
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