CN111047122A

CN111047122A - Enterprise data maturity evaluation method and device and computer equipment

Info

Publication number: CN111047122A
Application number: CN201811182573.5A
Authority: CN
Inventors: 刘亚东
Original assignee: Beijing Gridsum Technology Co Ltd
Current assignee: Beijing Gridsum Technology Co Ltd
Priority date: 2018-10-11
Filing date: 2018-10-11
Publication date: 2020-04-21

Abstract

The invention discloses an enterprise data maturity evaluation method, a device and a computer device, wherein the embodiment applies a plurality of dimensionalities, namely a plurality of first attribute evaluation indexes, of the maturity from data analysis of a target enterprise, comprehensively evaluates the maturity of the target enterprise, a plurality of users score the target enterprise according to the plurality of first attribute evaluation indexes so as to enable each first attribute evaluation index to have a plurality of first attribute scores, then, the data maturity evaluation result of the target enterprise can be obtained according to a preset data maturity analysis algorithm by combining the distribution weight, namely the first attribute weight, of each first attribute evaluation index, compared with the prior art that one expert directly evaluates the maturity of the target enterprise, the embodiment utilizes a data maturity analysis algorithm to comprehensively evaluate the multidimensional parameters of the target enterprise, the evaluation accuracy of the enterprise data analysis maturity is improved.

Description

Enterprise data maturity evaluation method and device and computer equipment

Technical Field

The invention relates to the technical field of data processing, in particular to an enterprise data maturity evaluation method, an enterprise data maturity evaluation device and computer equipment.

Background

The enterprise can generate data in normal operation, deep mining and analyzing the data can judge the whole market environment and the macro economic trend, and can also go deep into each link of production and operation and each client of service consumption to know the real situation, thereby providing data support for enterprise strategic target achievement and operation management decision.

Particularly in the current big data era, enterprises in all industries pay great attention to the application of big data analysis, and at present, data analysis and evaluation of the enterprises are usually manual qualitative analysis, namely, an enterprise data analysis maturity evaluator scores the enterprises, and subjectively determines the data analysis maturity level of the enterprises according to the scores, so that the accuracy is poor.

Disclosure of Invention

In view of the above, the present invention is proposed to provide an enterprise data maturity assessment method, apparatus and computing device that overcome or at least partially solve the above problems.

The embodiment of the invention provides an enterprise data maturity evaluation method, which comprises the following steps:

determining a plurality of first attribute evaluation indexes of a target enterprise, wherein the first attribute evaluation indexes are used for representing the data analysis capability of the target enterprise under the attribute;

acquiring a plurality of first attribute scores corresponding to each first attribute evaluation index and first attribute weights corresponding to the first attribute scores, wherein the first attribute scores respectively correspond to different users and are distinguished by different user identifiers;

and acquiring a data maturity evaluation result of the target enterprise by using a preset data maturity analysis algorithm according to the acquired first attribute score and the first attribute weight.

Optionally, the obtaining, according to the obtained first attribute score and the first attribute weight, a data maturity evaluation result of the target enterprise by using a preset data maturity analysis algorithm includes:

calculating corresponding undetermined evaluation scores of the target enterprise by using the respective first attribute scores and first attribute weights of the plurality of first attribute evaluation indexes corresponding to the same user identifier;

and performing weighted average calculation on the plurality of undetermined evaluation scores obtained by calculation to obtain a target evaluation score of the target enterprise, wherein the plurality of undetermined evaluation scores correspond to different user identifications.

Optionally, the obtaining, according to the obtained first attribute score and the first attribute weight, a data maturity evaluation result of the target enterprise by using a preset data maturity analysis algorithm further includes:

and evaluating the maturity of the target object by using the target evaluation score of the target enterprise according to a data maturity classification algorithm, and determining the current maturity level of the target enterprise.

Optionally, the method further includes:

obtaining level labels corresponding to a plurality of maturity levels contained in a maturity evaluation standard, wherein the maturity evaluation standard is determined based on evaluation scores of a plurality of sample enterprises corresponding to the user identifications;

obtaining a plurality of evaluation scores of each of the plurality of sample enterprises;

training the plurality of evaluation scores and the plurality of grade labels based on a machine learning algorithm to obtain a maturity classification model;

the method for evaluating the maturity of the target object by using the target evaluation score of the target enterprise according to a preset data maturity classification algorithm to determine the current maturity level of the target enterprise comprises the following steps:

and inputting the target evaluation score of the target enterprise into the maturity classification model for classification processing to obtain the current maturity level of the target enterprise.

Optionally, the evaluating the maturity of the target object by using the target evaluation score of the target enterprise according to a preset data maturity classification algorithm to determine the current maturity level of the target enterprise includes:

determining a plurality of maturity grades contained in the maturity evaluation standard to respectively correspond to the evaluation score ranges;

comparing the target evaluation score of the target enterprise with each evaluation score range;

and taking the maturity grade corresponding to the evaluation score range to which the target evaluation score belongs as the current maturity grade of the target enterprise.

Optionally, the method further includes:

acquiring user weights of a plurality of users determined by a third party, and associating the user weights with user identifications of corresponding users;

judging whether the obtained user weights are the same or not;

if yes, performing weighted average calculation on the calculated multiple undetermined evaluation scores to obtain a target evaluation score of the target enterprise;

if not, carrying out weighted average calculation by using the user weights respectively corresponding to the user identifications and the to-be-determined evaluation score to obtain the target evaluation score of the target enterprise.

Optionally, the method further includes:

determining a plurality of second attribute evaluation indexes of the target enterprise, and at least one first attribute evaluation index contained in each second attribute evaluation index;

the calculating the corresponding undetermined evaluation score of the target enterprise by using the respective first attribute score and first attribute weight of the plurality of first attribute evaluation indexes corresponding to the same user identifier includes:

aiming at the same user identification, calculating the product of the first attribute score and the first attribute weight corresponding to each first attribute evaluation index contained in each second attribute evaluation index;

carrying out quotient calculation on the obtained product result sums and the first attribute weight sum of each first attribute evaluation index contained in the second attribute evaluation index to obtain a second attribute score of the corresponding second attribute evaluation index;

carrying out quotient calculation on the first attribute weight sum of each first attribute evaluation index contained in the second attribute evaluation index and the first attribute weight sum of each first attribute evaluation index contained in each second attribute evaluation index to obtain a second attribute weight of the corresponding second attribute evaluation index;

calculating the sum of products of the second attribute scores and the second attribute weights corresponding to the second attribute evaluation indexes;

and carrying out quotient calculation on the sum of the products and the sum of the second attribute weights of the second attribute evaluation indexes to obtain the undetermined evaluation score of the target enterprise associated with the current user identifier.

The embodiment of the invention also provides an enterprise data maturity evaluation device, which comprises:

the first evaluation index determining module is used for determining a plurality of first attribute evaluation indexes of the target enterprise, and the first attribute evaluation indexes are used for representing the data analysis capability of the target enterprise under the attribute;

the first obtaining module is used for obtaining a plurality of first attribute scores corresponding to each first attribute evaluation index and first attribute weights corresponding to the first attribute scores, wherein the first attribute scores respectively correspond to different users and are distinguished by different user identifiers;

and the maturity evaluation result acquisition module is used for acquiring a data maturity evaluation result of the target enterprise by using a preset data maturity analysis algorithm according to the acquired first attribute score and the first attribute weight.

Optionally, the maturity evaluation result obtaining module includes:

the first calculation unit is used for calculating corresponding undetermined evaluation scores of the target enterprise by using the first attribute scores and the first attribute weights of the first attribute evaluation indexes corresponding to the same user identifier;

the second calculation unit is used for performing weighted average calculation on the calculated multiple undetermined evaluation scores to obtain a target evaluation score of the target enterprise, and the multiple undetermined evaluation scores correspond to different user identifications;

and the maturity grade determining unit is used for evaluating the maturity of the target object by using the target evaluation score of the target enterprise according to a data maturity classification algorithm, and determining the current maturity grade of the target enterprise.

An embodiment of the present invention further provides a computer device, where the computer device includes:

a communication interface;

a memory for storing a program for implementing the enterprise data maturity assessment method as described above;

and the processor is used for loading and executing the program stored in the memory so as to realize the steps of the enterprise data maturity evaluation method.

By the above technical solution, in the enterprise data maturity evaluation method, apparatus and computer device provided by the present invention, after determining a plurality of first attribute evaluation indexes of the target enterprise from a plurality of dimensions affecting the data analysis application maturity of the target enterprise, a plurality of users (such as experts in the field) score the target enterprise according to the plurality of first attribute evaluation indexes to obtain first attribute scores corresponding to the first attribute evaluation indexes respectively associated with the plurality of users, and combining the distribution weight of each first attribute evaluation index, namely the first attribute weight, obtaining a data maturity evaluation result of the target enterprise by using a preset data maturity analysis algorithm, wherein the evaluation method improves the data analysis maturity evaluation accuracy of the enterprise compared with the traditional scheme that a certain expert scores the whole enterprise and subjectively provides the maturity evaluation result of the enterprise.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

FIG. 1 is a flowchart illustrating a method for evaluating enterprise data maturity according to an embodiment of the present invention;

FIG. 2 is a flow chart illustrating another enterprise data maturity assessment method provided by an embodiment of the present invention;

FIG. 3 is a flowchart illustrating another enterprise data maturity assessment method according to an embodiment of the present invention;

FIG. 4 is a flowchart illustrating a further enterprise data maturity assessment method according to an embodiment of the present invention;

FIG. 5 is a schematic diagram illustrating an application flow of a method for evaluating enterprise data maturity according to an embodiment of the present invention;

FIG. 6 is a schematic diagram illustrating an application flow of another enterprise data maturity evaluation method according to an embodiment of the present invention;

FIG. 7 is a schematic structural diagram illustrating an enterprise data maturity assessment apparatus according to an embodiment of the present invention;

FIG. 8 is a schematic structural diagram of another enterprise data maturity assessment apparatus provided by the embodiment of the present invention;

FIG. 9 is a schematic structural diagram of another enterprise data maturity assessment apparatus provided in the embodiment of the present invention;

fig. 10 is a schematic diagram illustrating a hardware structure of a computer device according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

Referring to fig. 1, a flow chart of an enterprise data maturity evaluation method provided in an embodiment of the present invention is schematically illustrated, where the method may be applied to a computer device, such as a terminal device or a server, and the embodiment does not limit a product type of the computer device, and the method may specifically include, but is not limited to, the following steps:

step S11, determining a plurality of first attribute evaluation indexes of the target enterprise;

the target enterprise in this embodiment may be any enterprise, and attributes in various aspects, which reflect the data analysis application capability of the enterprise, are used as evaluation indexes, that is, the maturity of the enterprise in data analysis application is evaluated from multiple dimensions, the attributes affecting the data analysis application of the enterprise may include data organization, human resources, technical tools, application range, value, and the like, and each attribute may be further refined.

Specifically, the data organization may be divided into management organization of data analysis, strategic planning of data analysis, and attribute evaluation indexes of secondary dimensions such as policy and system of data analysis; the human resources can be divided into attribute evaluation indexes of secondary dimensions such as high-level leadership support, professionals for data analysis, data analysis culture atmosphere and the like; the data analysis technology can be divided into attribute evaluation indexes of secondary dimensions such as enterprise informatization construction maturity, data acquisition capability, data analysis tools, platform modes, optimization capability and the like; the application range can be divided into attribute evaluation indexes of secondary dimensions such as group company global application, department level application, limited personnel application in a department and the like; the value embodiment can be divided into two-level dimensional attribute evaluation indexes such as the data analysis result supporting enterprise strategic decision, the efficiency of data analysis application, the range of data analysis result decision application and the like.

It should be noted that the attribute information for analyzing the maturity of the enterprise is not limited to the data evaluation indexes of the first-level dimension and the second-level dimension listed above, and this embodiment is only described as an example, and the analysis and calculation processes of other attribute evaluation indexes are similar, and the embodiment is not described in detail.

In addition, the plurality of first attribute evaluation indexes determined in step S11 in this embodiment may refer to the attribute evaluation indexes of the secondary dimensions listed above, but is not limited thereto, and in combination with the above analysis, the first attribute evaluation index may represent the data analysis capability of the target enterprise under the attribute, or in other words, the maturity of the target object from the viewpoint of the attribute.

Step S12, obtaining a first attribute weight and a plurality of first attribute scores of each first attribute evaluation indicator;

optionally, the attribute score of this embodiment may be determined by an expert in the field where the enterprise is located, specifically, the enterprise may be scored by a plurality of experts in an expert panel in the field according to the plurality of determined first attribute evaluation indexes, and the obtained score is the first attribute score of the corresponding first attribute evaluation index. Of course, besides the scoring by experts in the field, the embodiment can also have the scoring by employees of enterprises, the operation process is similar, and the embodiment is not detailed here.

Therefore, for each first attribute evaluation index of the target enterprise, each expert, enterprise employee, and the like may, from this perspective, score the target enterprise, that is, evaluate the maturity of the target enterprise under the attribute, and this embodiment may determine the user identifier of the user scoring the target enterprise, and associate the user identifier with the scored first attribute score, so that the plurality of first attribute scores of each first attribute evaluation index obtained in step S12 may respectively correspond to different users and be distinguished by different user identifiers.

For the first attribute weight, the contribution of the corresponding primary dimension is obtained, and the first attribute weight can be obtained according to big data statistics, and the specific obtaining method of the proportion of each secondary dimension attribute evaluation index in the data analysis maturity evaluation of the target enterprise (i.e. the first attribute weight) is not limited in this embodiment.

Optionally, for the plurality of experts scored above, because different experts have different abilities, in order to improve the accuracy of evaluating the maturity of the enterprise data analysis, in this embodiment, a weight, that is, a user weight, of each expert may be set, and may be evaluated by an expert reviewer and associated with the unique user identifier of the expert, so that the corresponding user weight is obtained through the user identifier of the expert.

At this time, since the user weights of the experts may be the same or different, and the user weights of the experts determine that the proportions of the evaluation scores of the corresponding experts to the target enterprise are different, and further determine the final data analysis maturity evaluation score of the target enterprise, in order to improve the accuracy of the enterprise data analysis application maturity evaluation, this embodiment may determine whether the user weights are consistent after obtaining the user weights corresponding to the multiple user identifiers, if so, perform the subsequent steps, and if not, then when subsequently calculating the data maturity evaluation result of the target enterprise, the user weights also need to be considered.

And step S13, acquiring a data maturity evaluation result of the target enterprise by using a preset data maturity analysis algorithm according to the acquired first attribute score and the first attribute weight.

The preset data maturity analysis algorithm may include: and obtaining a final evaluation score of the user with the user identifier to the target enterprise by an algorithm for processing the first attribute scores and the first attribute weights of the plurality of first attribute evaluation indexes corresponding to each user identifier, namely obtaining a maturity evaluation result considered by the plurality of attribute evaluation indexes. The method may further include a maturity classification algorithm for determining the maturity level of the target enterprise based on the evaluation score of the target enterprise, and the like, in this embodiment, the content of the preset data maturity analysis algorithm is not limited,

based on this, in combination with the above analysis, the first attribute scores and the first attribute weights respectively corresponding to the first attribute evaluation indexes of the multiple secondary dimensions of the target enterprise are obtained in this embodiment, and if the user weights of the multiple users who score the first attribute evaluation indexes are consistent, the embodiment may directly calculate the data maturity evaluation scores of the experts for the target enterprise according to a preset calculation formula based on the multiple first attribute scores and the first attribute weights. If the user weights are not consistent, calculating to obtain a data maturity evaluation result of the target enterprise according to the acquired first attribute score, the acquired first attribute weight and the acquired user weights, and referring to the description of the corresponding part in the following specific implementation process.

Optionally, after the data maturity evaluation scores of the target enterprise, namely the to-be-evaluated scores, are obtained through calculation by the present embodiment, a pre-constructed maturity classification model of the object data analysis application may be used, the obtained multiple evaluation scores are used as model input, and the maturity classification model is input to obtain the maturity grade of the data analysis application of the target enterprise.

The maturity classification model may be a star model, a tree model, or other models with various structures, and is obtained by training a large amount of sample data using a machine learning algorithm.

Optionally, the machine learning algorithm used for training the maturity classification model may include K-nearest neighbor, logistic regression, gradient boosting tree, support vector machine, naive bayes, decision tree, random forest and other algorithms, a required algorithm may be selected according to actual needs, and the training of the maturity classification model is completed according to a data processing principle of the algorithm, which is not described in detail in the embodiment.

In this embodiment, the sample data for model training may include a plurality of scores scored by a plurality of experts on an enterprise, a large number of evaluation scores scored on a plurality of enterprises in the same industry, and a plurality of maturity levels (or referred to as development stages) obtained by dividing a process in which the enterprise data is analyzed according to maturity values of different types of enterprises, such as a data analysis method startup stage (which may be referred to as a stage a), a data analysis method limited utilization stage (which may be referred to as a stage B), a data analysis enterprise application stage (which may be referred to as a stage C), a data analysis type enterprise stage (which may be referred to as a stage D), an intelligent data analysis type enterprise stage (which may be referred to as an stage E), and the like, in this embodiment, the level tags corresponding to these stages respectively may be determined, so that the obtained plurality of level tags are also used as training sample data, in this way, the multiple evaluation scores of the target enterprise are calculated by using the trained maturity classification model, and the maturity level of the target evaluation score of the target enterprise is determined.

Of course, in this embodiment, a preset calculation formula may also be used to directly calculate the multiple first attribute scores of the target enterprise, and after obtaining the maturity evaluation score of the target enterprise, compare the maturity evaluation score with a preset maturity evaluation standard to determine the maturity level of the target enterprise, which is not limited to the above obtaining the maturity evaluation result of the target enterprise based on the maturity classification model.

In practical application of this embodiment, after the source data of the sample data used for model training is obtained in the manner described above, because the source data usually includes normal data and damaged data, the source data may be preprocessed, such as removing null/single values, invalid features, processing missing value features, performing one-hot coding processing on class features extracted from the source data, and the like, and then, performing feature analysis, such as relevance analysis and importance analysis, on the preprocessed data, using the obtained data as the sample data, and performing model training using a machine learning algorithm, that is, performing continuous feature analysis and model iteration, to obtain a stable maturity classification model.

For the maturity classification model obtained by training, the implementation can further continue to optimize and promote the maturity classification model in a way of expanding a data source, for example, model optimization is performed in a way of carrying out grid parameter calling, cross validation and the like on various sample data, accuracy of the maturity classification model for enterprises is improved, importance of various input data characteristics of the model can be obtained, weight of each attribute evaluation index is adjusted accordingly, accuracy of maturity evaluation is further improved, and detailed description is not given in the embodiment of a specific implementation process.

To sum up, in the present embodiment, a plurality of first attribute evaluation indexes of a target enterprise are determined from a plurality of dimensions affecting the data analysis application maturity of the target enterprise, then, the target enterprise is comprehensively evaluated from the dimensions, specifically, a plurality of users (such as experts in the field, etc.) score the target enterprise according to the plurality of first attribute evaluation indexes so that each first attribute evaluation index has a plurality of first attribute scores, then, a data maturity evaluation result of the target enterprise is obtained according to a preset data maturity analysis algorithm by combining the assigned weight of each first attribute evaluation index, that is, the first attribute weight, compared to the prior art in which a certain expert directly evaluates the maturity of the target enterprise, the present embodiment uses the data maturity analysis algorithm to comprehensively evaluate the multidimensional parameters of the target enterprise, the evaluation accuracy of the enterprise data analysis maturity is improved.

As another alternative embodiment of the present invention, a flowchart of an enterprise data maturity evaluation method provided by another alternative embodiment as shown in fig. 2 may be applied to a computer device, and referring to fig. 2, the method may include the following steps:

step S21, determining a plurality of first attribute evaluation indexes of the target enterprise;

with respect to the process of determining the first attribute evaluation index at step S21, reference may be made to the description of the corresponding part of step S11 of the above-described embodiment.

Step S22, obtaining a first attribute weight and a plurality of first attribute scores of each first attribute evaluation index;

for the manner of obtaining the first attribute score and the first attribute weight in this embodiment, reference may be made to the description of the corresponding part of step S12 in the above embodiment.

Step S23, calculating corresponding undetermined evaluation scores of the target enterprise by using the respective first attribute scores and first attribute weights of a plurality of first attribute evaluation indexes corresponding to the same user identifier;

by combining the above descriptions of the attribute evaluation indexes of the evaluation enterprise data maturity, the determined primary dimension and the secondary dimension, the embodiment can obtain an enterprise data analysis maturity dimension and a weight schematic diagram shown in table 1 below;

TABLE 1

Assuming that N experts participate in scoring the secondary dimension attribute evaluation indexes of the enterprise, each expert may score each secondary dimension attribute evaluation index in table 1 above to obtain a maturity score Pi, i ═ 1, 2, …, and N of the expert for the enterprise, where N is an integer, and 5 primary dimension attribute evaluation indexes (denoted as second attribute evaluation indexes) shown in table 1 above are described as an example, and each primary dimension attribute evaluation index subdivides 3 secondary dimension attribute evaluation indexes (i.e., the first attribute evaluation indexes). The maturity score of each expert on the target enterprise, namely the pending evaluation score, can be calculated by adopting the following formula.

As can be seen from the formula (1), the maturity score of each expert on the target enterprise can be obtained by dividing the sum of the products of the second attribute scores of the second attribute evaluation indexes corresponding to the primary dimension and the corresponding second attribute weights by the sum of the second attribute weights of the second attribute evaluation indexes in the dimension.

In combination with the above analysis, each primary dimension may be subdivided into a plurality of attribute evaluation indicators of secondary dimensions, and therefore, for the second attribute score of the second attribute evaluation indicator of each primary dimension, the second attribute score of each first attribute evaluation indicator of the corresponding secondary dimension and the first attribute weight may be calculated, specifically, the calculation process of the above formula (2) and formula (3). In this embodiment, a plurality of second attribute evaluation indexes of the target enterprise and at least one first attribute evaluation index included in each second attribute evaluation index may be determined, and then, the data maturity evaluation score of each expert on the target enterprise is calculated according to the above formulas (1), (2) and (3) and is recorded as the pending evaluation score.

Step S24, obtaining user weights corresponding to a plurality of user identifications respectively;

if the user is an expert in the field of the target enterprise, the user weight of the user can be determined by an expert panel and is sent to the computer device.

Step S25, judging whether the user weights are consistent, if yes, entering step S26; if not, go to step S27;

step S26, carrying out weighted average calculation on the plurality of undetermined evaluation scores obtained by calculation to obtain a target evaluation score of the target enterprise; after the N undetermined evaluation scores Pi are obtained by calculation in the above manner, if the user weights of the scored users are the same, the embodiment may directly perform weighted average calculation on the N undetermined evaluation scores by using formula (4) to obtain a target evaluation score P of the target enterprise, where the weighted average calculation formula (4) is:

in the case where the user weights of the plurality of users who perform the scoring are the same, the calculation method of calculating the target evaluation score of the target business is not limited to the weighted average calculation method described above.

Step S27, carrying out weighted average calculation by using the user weight corresponding to each user identification and the corresponding to-be-determined evaluation score to obtain the target evaluation score of the target enterprise;

in this embodiment, if the user weights of the users who perform the scoring are different, the weighted average calculation cannot be directly performed on the obtained multiple scores to be evaluated, and the calculation needs to be performed in combination with the user weights of the users, and specifically, the product of each score to be evaluated and the user weight of the corresponding user can be calculated, and after the product is used as a new score to be evaluated, the weighted average calculation is performed according to the above manner, so as to obtain the target evaluation score.

And step S28, evaluating the maturity of the target enterprise according to the data maturity classification algorithm by using the target evaluation score of the target enterprise, and determining the current maturity level of the target enterprise.

The data maturity classification algorithm may include the data maturity classification model obtained by the pre-training described in the above embodiment, or may be a comparison algorithm for determining the maturity of the enterprise based on the evaluation score range corresponding to each maturity grade obtained by the maturity classification model, where the content of the data maturity classification algorithm is not limited in this embodiment.

Alternatively, in this embodiment, the obtained target evaluation score of the target enterprise may be directly output, and the current maturity level of the target enterprise is determined according to the maturity level evaluation criteria known by the user, that is, the computer device may directly output the target evaluation score obtained in step S26 and step S27, and step S28 may not be executed.

In order to improve the accuracy of evaluating the maturity of the enterprise data, the processing method described in this embodiment may be adopted, that is, after the target evaluation score obtained in step S26 or step S27 is executed, step S28 may be executed, and the computer device automatically determines the current maturity level of the target enterprise, instead of manually evaluating the current maturity level of the target enterprise according to the target evaluation score of the target enterprise, so as to avoid the problem that different users perceive different maturity levels, which results in non-uniform maturity level evaluation results.

The data maturity evaluation criteria may be obtained by enterprise data analysis in the field, or set by expert committees in the field, or determined based on an evaluation score range corresponding to each maturity level obtained by the maturity classification model, and the determination method is not limited in this embodiment. In practical applications, the data maturity evaluation criteria may include the division criteria for the enterprise development stages as described above, and specifically may be a plurality of maturity levels listed above, i.e., stages a to E, and the present embodiment does not limit how the data maturity evaluation criteria represent the plurality of maturity levels, for example, the evaluation scores for the enterprise data maturity may be divided into a plurality of corresponding ranges, which respectively correspond to different maturity levels, and so on.

Based on this, after obtaining the target evaluation score of the target enterprise, the present embodiment may determine the current maturity level of the target enterprise in a manner as shown in fig. 3, that is, the specific implementation process of step S28 may include, but is not limited to:

step A1, determining a plurality of maturity grades contained in the maturity evaluation standard to respectively correspond to the evaluation score ranges;

it should be noted that, in this embodiment, the method for dividing the evaluation score range by the multiple maturity levels is not limited, and in general, the evaluation score ranges corresponding to consecutive maturity levels are connected, and the higher the maturity level is, the larger the corresponding evaluation score range is, which indicates that the development of the target enterprise is better.

Taking the five maturity levels of the above-listed a stage to E stage as an example, since the stages are divided according to the process of enterprise data analysis, obviously, the enterprise data corresponding to the a stage is higher in maturity than the enterprise data of the E stage, that is, according to the sequence of A, B, C, D and the E stage, the evaluation scores from low to high are divided into ranges of each stage, and an evaluation score range corresponding to each of the multiple maturity levels is obtained. In practical applications, the dividing process can be performed by experts in the field and transmitted to the computer device, but is not limited to this manual dividing manner.

Step A2, comparing the target evaluation score of the target enterprise with each evaluation score range;

the embodiment may compare the obtained target evaluation score with each evaluation score range one by one to determine which evaluation score range the target evaluation score falls in, but is not limited to this comparison manner.

And step A3, taking the maturity level corresponding to the evaluation score range to which the target evaluation score belongs as the current maturity level of the target enterprise.

As another optional embodiment of the present invention, the present invention may also pre-construct a maturity classification model, input the obtained target assessment score of the target enterprise into the maturity classification model, and output to obtain the current maturity level of the target enterprise, at this time, the assessment score range corresponding to each maturity level may be determined by the data classification maturity model, and the specific implementation process is not limited.

Referring to the flowchart of the method for training the maturity classification model shown in fig. 4, the embodiment of the present invention may use this method to train to obtain the maturity classification model, as shown in fig. 4, the method may include:

step B1, obtaining a grade label corresponding to each of a plurality of maturity grades contained in the maturity evaluation standard;

regarding the maturity evaluation criteria and the contents of the plurality of maturity levels included in the maturity evaluation criteria, reference may be made to the description of the corresponding parts of the foregoing embodiments, and the description of the embodiments is not repeated. In order to distinguish different maturity levels, the present embodiment may set a unique level label for each maturity level, and the content of the level label is not limited in the present embodiment, and may be a number or letter code, and the like.

In addition, in combination with the above analysis, the maturity evaluation criterion may be determined based on evaluation scores of a plurality of sample enterprises corresponding to the plurality of user identifiers, respectively, and a specific determination method is not limited.

Step B2, obtaining a plurality of evaluation scores of a plurality of sample enterprises;

the sample enterprises can be other enterprises in the same field as the target enterprise, the acquisition mode of the evaluation scores of the sample enterprises is similar to that of the evaluation scores of the target enterprises, the evaluation scores can be scored by a plurality of experts, and each sample enterprise can be scored by the experts for multiple times so as to improve the accuracy of model training.

Optionally, as time advances, the cognitive degree of the expert is also changing continuously, and then scoring values of the same attribute evaluation index for the same enterprise may be different, and in order to further improve the accuracy of the model obtained by training, multiple experts can perform scoring evaluation for multiple times within a certain time in this embodiment.

The evaluation scores of the experts involved in the above embodiments of the present invention may be determined by using a questionnaire, and the specific implementation process is not described in detail in this embodiment.

Step B3, training the multiple evaluation scores and the multiple grade labels based on a machine learning algorithm to obtain a maturity classification model;

the Machine Learning (ML) algorithm may include a plurality of algorithm models such as a decision tree, a cluster, a bayesian classification, a support vector Machine, and the like, and the required algorithm model may be selected according to actual needs, for example, the decision tree model is used for Machine Learning, and the required maturity classification model is obtained through training.

The maturity classification model can be used for directly determining the current maturity level of the data maturity of the target enterprise, so that the sample data determined during model training can include a plurality of level labels representing a plurality of maturity levels in addition to the plurality of evaluation scores obtained by the experts, and with reference to a model training process schematic diagram shown in fig. 5, after obtaining the rating questionnaire survey results of a plurality of experts, the undetermined evaluation score of each expert on the data maturity of the same object can be calculated according to the calculation method described in the above embodiment, and then each undetermined evaluation score can be used as a feature value, and each feature value and the plurality of level labels are trained and learned according to a machine learning algorithm to obtain the data interval of each maturity level of the data maturity classification model.

In this embodiment, after the attribute score obtained by the expert is used to calculate the to-be-evaluated score of the sample enterprise, the expert may determine the level label of the sample enterprise according to the to-be-evaluated score, that is, the expert determines the level label corresponding to the to-be-evaluated score, in this way, in the process of performing machine learning training by using the collected sample data, the maturity classification model obtained by each training is used to determine that the accuracy rate of the maturity grade of each sample enterprise reaches a preset value (for example, more than 90%), it can be considered that the maturity classification model obtained by this training is accurate enough, that is, a convergence condition of model training is reached, and the finally obtained maturity classification model is used as a model for subsequent practical application to obtain the maturity grade of the target enterprise.

It should be noted that, in the embodiment, how to utilize the machine learning algorithm to train the specific training processes of the multiple evaluation scores and the multiple level labels of the sample enterprise is not limited, and the training processes are different based on different model architectures. Generally, in order to improve the accuracy of a model obtained by training, it is necessary to ensure that sample data, i.e., input data, is sufficient, and the sample data may be obtained by scoring each object multiple times and scoring multiple objects in the same industry, but the method is not limited thereto.

And step B4, inputting the target evaluation scores of the target enterprises into the maturity classification model for classification processing, and obtaining the current maturity levels of the target enterprises.

In summary, with reference to the flow diagrams shown in fig. 5 and fig. 6, in this embodiment, attributes of the enterprise in various aspects are considered comprehensively to evaluate the maturity of the enterprise in data analysis application, and the evaluation calculates undetermined evaluation scores of the expert on the enterprise through the scoring of the expert and the weights of the attributes, and then, a plurality of undetermined evaluation scores and different maturity grades of the expert on the enterprise are used as sample data, model training is performed on the sample data by using a machine learning algorithm to obtain a maturity classification model, and the undetermined evaluation scores of the target enterprise are input, so that the current maturity grade of the target enterprise, that is, the capability condition of the target enterprise in data analysis application, can be obtained accurately and quickly.

Therefore, the unified quantitative evaluation model is adopted in the embodiment, the maturity of data analysis among enterprises in different industries can be quickly and accurately realized, and the stage of the data analysis application of the current enterprise can be accurately known, so that the range, the value and the technical route of the enterprise in the data analysis application can be determined, and data support is provided for achieving the strategic objective of the enterprise and making business management decisions.

As shown in fig. 7, a schematic structural diagram of an enterprise data maturity evaluation apparatus provided in an embodiment of the present invention may be applied to a computer device, and may specifically include, but is not limited to, the following functional structures:

a first evaluation index determination module 71, configured to determine a plurality of first attribute evaluation indexes of the target enterprise;

the first attribute evaluation index may be an evaluation index of a secondary dimension that affects the maturity of the data analysis application of the target enterprise, that is, the data analysis capability of the target enterprise under the attribute is represented, and the specific obtaining manner and the content may refer to the description of the corresponding part of the above method embodiment.

A first obtaining module 72, configured to obtain a first attribute weight and a plurality of first attribute scores of each first attribute evaluation indicator;

the plurality of first attribute scores respectively correspond to different users and are distinguished by different user identifications.

In this embodiment, the attribute scores corresponding to the attribute evaluation indexes may be obtained by performing questionnaire survey on experts in the field of the target enterprise, but are not limited to this obtaining manner. In general, scores of different experts on the same attribute evaluation index of the same target enterprise are not consistent, and because the influence capabilities of different attribute evaluation indexes on the data analysis maturity of the target enterprise are different, the attribute weights corresponding to the attribute evaluation indexes are often different, and the specific obtaining mode can refer to the description of the corresponding part of the embodiment of the method.

Optionally, in this embodiment, the user weight corresponding to each expert may also be obtained, so that the data maturity evaluation result of the target enterprise is determined by combining the user weights of the experts under the condition that the user weights of the experts are different.

And the maturity evaluation result obtaining module 73 is configured to obtain a data maturity evaluation result of the target enterprise by using a preset data maturity analysis algorithm according to the obtained first attribute score and the first attribute weight.

In combination with the above analysis of the user weight of each scoring expert, the apparatus provided in this embodiment may further include:

the second evaluation index determining module is used for determining a plurality of second attribute evaluation indexes of the target enterprise and at least one first attribute evaluation index contained in each second attribute evaluation index;

accordingly, in order to achieve the maturity evaluation score of each expert on the target enterprise, in combination with the above formulas (1) to (3), the calculating module 73 may include:

the first product unit is used for calculating the product of the first attribute score and the first attribute weight corresponding to each first attribute evaluation index contained in each second attribute evaluation index;

the first quotient obtaining unit is used for carrying out quotient obtaining operation on the obtained product result sums and the first attribute weight sum of each first attribute evaluation index contained in the second attribute evaluation index to obtain a second attribute score of the corresponding second attribute evaluation index;

the second quotient calculating unit is used for performing quotient calculation on the first attribute weight sum of each first attribute evaluation index contained in the second attribute evaluation index and the first attribute weight sum of each first attribute evaluation index contained in each second attribute evaluation index to obtain a second attribute weight of the corresponding second attribute evaluation index;

the second product unit is used for calculating the sum of products of the second attribute scores and the second attribute weights corresponding to the second attribute evaluation indexes;

and the third quotient calculation unit is used for performing quotient calculation on the sum of the products and the sum of the second attribute weights of the second attribute evaluation indexes to obtain the undetermined evaluation score of the target enterprise associated with the current user identifier.

Alternatively, as shown in fig. 8, the maturity evaluation result obtaining module 73 may include:

a first calculating unit 731, configured to calculate a to-be-evaluated score corresponding to the target enterprise by using a first attribute score and a first attribute weight of each of a plurality of first attribute evaluation indicators corresponding to the same user identifier;

in this embodiment, the calculation process of the to-be-determined evaluation score of each expert for the target enterprise may be obtained by combining the formulas (1) to (3) and the description of the corresponding parts, and details are not repeated herein.

The second calculating unit 732 is configured to perform weighted average calculation on the multiple calculated undetermined evaluation scores to obtain a target evaluation score of the target enterprise.

Optionally, the apparatus provided in this embodiment may further include:

the user weight acquisition module is used for acquiring user weights of a plurality of users determined by a third party and associating the user weights with user identifications of corresponding users;

the user weight judging module is used for judging whether the obtained user weights are the same or not, and if so, triggering the second calculating unit to perform weighted average calculation on the calculated multiple to-be-determined evaluation scores to obtain the target evaluation score of the target enterprise;

the specific implementation process of the weighted average calculation may refer to the description of the corresponding part of formula (4) in the above method embodiment.

And the evaluation score calculating module is used for performing weighted average calculation by using the user weights respectively corresponding to the plurality of user identifications and the to-be-evaluated score to obtain a target evaluation score of the target enterprise if the judgment result of the user weight judging module is negative.

As another alternative, referring to fig. 8, the calculating module may further include:

the maturity level determining unit 733 is configured to perform maturity evaluation on the target enterprise according to a data maturity classification algorithm by using the target evaluation score of the target enterprise, and determine a current maturity level of the target enterprise.

Optionally, in practical applications, a pre-constructed maturity classification model may be used to determine a current maturity level of a target enterprise, and based on this, referring to fig. 9, the apparatus provided in this embodiment may further include:

a grade label obtaining module 74, configured to obtain grade labels corresponding to a plurality of maturity grades included in the maturity evaluation standard;

an evaluation score obtaining module 75 configured to obtain a plurality of evaluation scores of each of the plurality of sample enterprises;

a model training module 76, configured to train the multiple evaluation scores and the multiple rating labels based on a machine learning algorithm, so as to obtain a maturity classification model;

accordingly, the maturity level determining unit 733 may be specifically configured to input the target evaluation score of the target enterprise into the maturity classification model for classification processing, so as to obtain the current maturity level of the target enterprise.

As still another alternative embodiment of the present invention, the maturity level determination unit 733 may include:

the first determining subunit is used for determining that a plurality of maturity grades contained in the maturity evaluation standard respectively correspond to the evaluation score ranges;

the comparison subunit is used for comparing the target evaluation score of the target enterprise with each evaluation score range;

and the second determining subunit is used for taking the maturity level corresponding to the evaluation score range to which the target evaluation score belongs as the current maturity level of the target enterprise. In this embodiment, the enterprise data maturity evaluation apparatus described in each of the above embodiments may include a processor and a memory, where the first evaluation index determining module, the first obtaining module, the maturity evaluation result obtaining module, the first calculating unit, the second calculating unit, the maturity level determining unit, the level label obtaining module, the evaluation score obtaining module, the model training module, and the like are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

The processor may include a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the scoring and the weighting of the first attribute evaluation indexes of the target enterprise are respectively calculated by the experts by adjusting kernel parameters, so that the data maturity evaluation result of the target enterprise is obtained.

The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

An embodiment of the present invention provides a storage medium, on which a program is stored, where the program, when executed by a processor, implements the enterprise data maturity assessment method described in the above method embodiment.

The embodiment of the invention provides a processor, which is used for running a program, wherein the enterprise data maturity evaluation method is executed when the program runs.

As shown in the hardware structure diagram of fig. 10, an embodiment of the present invention provides a computer device, which may include: a communication interface 101, a memory 102, and a processor 103, wherein:

the number of the communication interface 101, the memory 102, and the processor 103 may be at least one, and the communication interface 101, the memory 102, and the processor 103 may communicate with each other through a communication bus.

The communication interface 101 may be used to receive messages sent by external devices, such as the scoring of each expert, the user weights of each expert, and the like.

Optionally, the communication interface may include a wired or wireless network interface, such as a WIFI network interface, a GPRS network interface, and the like, and the type of the interface included in the communication interface is not limited in this embodiment.

A memory 102 for storing a program for implementing the enterprise data maturity assessment method described above;

a processor 103 for loading and executing the memory-stored program, the program for:

acquiring a first attribute weight and a plurality of first attribute scores corresponding to each first attribute evaluation index, wherein the plurality of first attribute scores respectively correspond to different users and are distinguished by different user identifications;

Optionally, the following steps may be implemented when the processor executes the program:

calculating corresponding undetermined evaluation scores of the target enterprise by using respective first attribute scores and first attribute weights of a plurality of first attribute evaluation indexes corresponding to the same user identifier;

and evaluating the maturity of the target enterprise according to a data maturity classification algorithm by using the target evaluation score of the target enterprise, and determining the current maturity level of the target enterprise.

acquiring level labels corresponding to a plurality of maturity levels contained in a maturity evaluation standard, wherein the maturity evaluation standard is determined based on evaluation scores of a plurality of sample enterprises corresponding to the user identifications;

obtaining a plurality of evaluation scores of a plurality of sample enterprises;

the method for evaluating the maturity of the target enterprise by using the target evaluation score of the target enterprise according to a preset data maturity classification algorithm to determine the current maturity level of the target enterprise comprises the following steps:

judging whether the obtained user weights are the same or not;

the calculating the corresponding undetermined evaluation score of the target enterprise by using the respective first attribute score and first attribute weight of the plurality of first attribute evaluation indexes corresponding to the same user identifier comprises:

In practical applications, the computer device provided by the embodiment may be a terminal device, a server, or the like.

Embodiments of the present invention further provide a computer program product, which, when executed on a computer device, is adapted to execute a program that initializes the following method steps:

Optionally, the computer product may further implement the following steps when executing the program:

and carrying out weighted average calculation on the plurality of to-be-evaluated scores obtained through calculation to obtain the target evaluation score of the target enterprise.

obtaining grade labels respectively corresponding to a plurality of maturity grades contained in the maturity evaluation standard;

obtaining a plurality of evaluation scores of a sample enterprise;

judging whether the obtained user weights are the same or not;

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, computer device or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, computer devices and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable message processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computer device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.

The above are merely examples of the present invention, and are not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for assessing maturity of enterprise data, the method comprising:

acquiring a first attribute weight and a plurality of first attribute scores of each first attribute evaluation index, wherein the plurality of first attribute scores respectively correspond to different users and are distinguished by different user identifiers;

2. The method of claim 1, wherein obtaining the data maturity assessment result of the target enterprise according to the obtained first attribute score and the first attribute weight by using a preset data maturity analysis algorithm comprises:

3. The method of claim 2, wherein the obtaining of the data maturity assessment result of the target enterprise by using a preset data maturity analysis algorithm according to the obtained first attribute score and the first attribute weight further comprises:

and evaluating the maturity of the target enterprise according to a preset data maturity classification algorithm by using the target evaluation score of the target enterprise, and determining the current maturity level of the target enterprise.

4. The method of claim 3, further comprising:

5. The method of claim 3, wherein the determining the current maturity level of the target business by evaluating the maturity of the target business according to a preset data maturity classification algorithm using the target evaluation score of the target business comprises:

6. The method of claim 2, further comprising:

judging whether the obtained user weights are the same or not;

7. The method of claim 2, further comprising:

8. An enterprise data maturity assessment apparatus, said apparatus comprising:

the first acquisition module is used for acquiring a first attribute weight and a plurality of first attribute scores of each first attribute evaluation index, wherein the plurality of first attribute scores respectively correspond to different users and are distinguished by different user identifiers;

9. The apparatus of claim 8, wherein the maturity assessment result obtaining module comprises:

10. A computer device, characterized in that the computer device comprises:

a communication interface;

a memory for storing a program for implementing the enterprise data maturity assessment method according to any one of claims 1 to 7;

a processor for loading and executing the program stored in the memory to implement the steps of the enterprise data maturity assessment method according to any one of claims 1 to 7.