Enterprise management data management method based on big data analysis

Abstract

The invention relates to an enterprise management data governance method based on big data analysis. The method comprises the following steps: 1, obtaining a data governance demand of an enterprise; generating a data governance scheme through a preset dynamic governance model decision according to the data governance demand; collecting and treating the enterprise data according to the data treatment scheme; and performing data analysis on the treated data to generate suggestions required by data treatment requirements. The method can effectively improve the data governance effect and governance efficiency.

Description

Technical Field

[0001] The present application relates to the field of data governance, and in particular to an enterprise management data governance method based on big data analysis. Background Art

[0002] Data governance is a series of activities to manage enterprise data assets, including data standard formulation, data quality improvement, data security, data process optimization, etc. Its purpose is to ensure the accuracy, integrity, consistency, timeliness and security of data, so that data can effectively support enterprise decision-making, operations and strategic planning.

[0003] The Chinese patent with publication number CN118297483A discloses an enterprise data governance system and method based on big data analysis. In view of the problem that the data of existing enterprise internal systems is scattered and the data quality of the entire enterprise cannot be evaluated as a whole, the scattered original big data of the enterprise is effectively counted and normalized by constructing dimension tables and granularity measurement tables of enterprise data, which facilitates the downstream to conduct an overall evaluation of the data quality of the enterprise. The application conducts quality inspection on enterprise data by constructing a quality inspection indicator system with multiple dimensions, and screens out data that does not meet the quality inspection standards, avoiding subsequent data cleaning work.

[0004] However, with the development of corporate business, the increase in data sources and the diversification of data types, traditional static data governance methods are often difficult to cope with. Enterprises are faced with problems such as low data quality, high data security risks and low data analysis efficiency. Summary of the invention

[0005] In order to solve the above problems, the present application provides an enterprise management data governance method based on big data analysis.

[0006] In the first aspect, the present application provides an enterprise management data governance method based on big data analysis, which adopts the following technical solutions:

[0007] A data governance method for enterprise management based on big data analysis includes the following steps:

[0008] Obtain the data governance needs of the enterprise;

[0009] Generate a data governance solution through a preset dynamic governance model decision according to data governance needs, wherein the dynamic governance model decision is obtained by iterative training of a machine learning model through the enterprise data governance historical data; the data governance solution includes at least one data governance strategy, data governance indicator, data governance tool and data governance process;

[0010] Collect and manage enterprise data according to the data governance plan;

[0011] Perform data analysis on the governed data and generate recommendations for data governance needs.

[0012] Preferably, it also includes collecting data governance data regularly and after completing the enterprise's data governance needs to produce training samples to iteratively train the dynamic governance model.

[0013] Preferably, the data governance strategy can be any one of a data quality optimization strategy, a data security enhancement strategy, a data storage and processing optimization strategy, a data sharing and collaboration strategy, and a data emergency handling strategy.

[0014] Preferably, generating a data governance solution through a preset dynamic governance model decision according to data governance requirements specifically includes the following steps:

[0015] Generate multiple alternative governance solutions that meet data governance needs through pre-set dynamic governance model decisions according to data governance needs;

[0016] Evaluate each alternative governance plan and calculate the evaluation value of each alternative governance plan using a preset evaluation value calculation formula;

[0017] The alternative governance solutions are ranked based on the evaluation values, and the alternative governance solution with the highest evaluation value is selected as the data governance solution.

[0018] Preferably, the step of evaluating each candidate governance solution and calculating the evaluation value of each candidate governance solution by using a preset evaluation value calculation formula specifically includes the following steps:

[0019] Evaluate and rank each alternative governance solution based on technical feasibility, economic cost-effectiveness, data governance effect, implementation cycle, and risk level, and obtain a technical feasibility sequence, an economic cost-effectiveness sequence, a data governance effect sequence, an implementation cycle sequence, and a risk level sequence;

[0020] Obtain the technical feasibility sequence index S of each alternative treatment solution by comparing it with the preset sequence index comparison table TFi , Economic cost-benefit sequence index S ECBi , Data governance effect sequence indicator S DGEi 、Implementation of cycle sequence indicator S ICi And the risk level sequence index S RDi ;

[0021] Assign weights to the above sequences to obtain the technical feasibility sequence weights W of each alternative governance solution: TF , economic cost-benefit sequence weight W ECB , Data governance effect sequence weight W DGE , implement the periodic sequence weight W ICand the risk level sequence weight W RD , and W TF +W DGE +W ECB +W IC +W RD =1;

[0022] The evaluation value of each alternative governance solution is calculated using a preset evaluation value calculation formula.

[0023] Preferably, the evaluation value calculation formula is specifically:

[0024] V i =W TF *S TFi +W ECB *S ECBi +W DGE *S DGEi +W IG *S ICi +W RD *S RDi ;

[0025] Among them, V i is the evaluation value of the i-th alternative governance solution.

[0026] Preferably, the weight allocation for each of the above sequences specifically includes the following steps:

[0027] Sending questionnaires to managers to confirm governance preferences and obtain governance preference questionnaires;

[0028] Collect corporate financial report information, input the governance tendency questionnaire, corporate financial report information and data governance requirements into a pre-set weight allocation model to generate recommended weights; the weight allocation model is a machine learning model trained through historical weight allocation data;

[0029] The recommended weights are sent to the management staff, and after confirmation or modification, the weights are assigned to each sequence.

[0030] Preferably, the data analysis of the governed data to generate the suggestions required for the data governance needs specifically includes: performing correlation analysis on the governed data through a preset data analysis model, selecting relevant data related to the data governance needs, and using the data analysis model to mine and analyze the relevant data based on the data governance needs to generate the suggestions required for the data governance needs; the data analysis model is a machine learning model obtained by training through historical suggestion data.

[0031] In a second aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0032] A computer-readable storage medium, characterized in that it stores a computer program that can be loaded by a processor and execute any of the above methods.

[0033] In summary, the present application includes at least one of the following beneficial technical effects:

[0034] 1. Viewing data governance as a dynamically changing system and responding to changes in the data environment in real time can effectively improve data governance efficiency and accuracy. With the development of the enterprise business, according to the enterprise's data governance needs, intelligent decision-making can dynamically adapt to enterprise changes, improve governance efficiency, and provide data-driven decision-making suggestions to better meet business needs, thereby effectively improving data governance effects and governance efficiency.

[0035] 2. Generate multiple alternative governance solutions for dynamic governance model decisions, and evaluate and rank them based on technical feasibility, economic cost-effectiveness, data governance effect, implementation cycle, and risk level. Multiple solutions can be compared quantitatively, and weights can be assigned to multiple evaluation directions based on the actual situation of the enterprise, and then the evaluation value of each alternative governance solution can be calculated. On the basis of comprehensive and intelligent scoring of each alternative, the governance tendency of the enterprise is taken into account, which helps to improve the scientificity and rationality of the scoring of the alternative governance solutions, and meet the business needs of the enterprise on the basis of dynamically adapting to the changes of the enterprise and improving the efficiency of data governance;

[0036] 3. Analyzing the governed data can uncover hidden patterns, trends, and associations in the data, provide in-depth insights for the enterprise, help the enterprise establish a data-driven decision-making culture, promote the close integration of data governance and business goals, and further expand the results in a data-oriented manner based on meeting data governance needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a method flow chart of the enterprise management data governance method in an embodiment of the present application;

[0038] Figure 2 It is a flow chart of a method for generating a data governance solution through dynamic governance model decision making in an embodiment of the present application;

[0039] Figure 3 is a flow chart of a method for calculating the evaluation value of each alternative governance solution in an embodiment of the present application;

[0040] Figure 4 It is a flow chart of a method for allocating weights to each sequence in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following is combined with Figure 1-4 This application is described in further detail.

[0042] The present application embodiment discloses a method for enterprise management data governance based on big data analysis. Figure 1 , an enterprise management data governance method based on big data analysis, comprising the following steps:

[0043] S1. Obtain the data governance needs of the enterprise;

[0044] Specifically, communicate with various departments of the enterprise to understand their pain points and needs for data and determine the enterprise's data governance needs; for example, the sales department may focus on the accuracy and completeness of customer data in order to better conduct customer segmentation and precision marketing; the finance department may need to ensure the compliance and accuracy of financial data to support the preparation and audit of financial statements; of course, you can also study the enterprise's strategic planning and goals from the enterprise's perspective to determine the enterprise's data governance needs. For example, if the enterprise plans to expand into new market areas in the next year, then the data governance needs may include collecting and integrating relevant data of the new market, as well as analyzing existing data to assess the feasibility of market entry;

[0045] S2. Generate a data governance plan: Generate a data governance plan through a preset dynamic governance model decision according to data governance requirements, wherein the dynamic governance model decision is obtained by iteratively training a machine learning model through the enterprise data governance historical data, wherein the specific training steps of the machine learning model are prior art and are not described here; the data governance plan includes at least one data governance strategy, data governance indicator, data governance tool and data governance process;

[0046] S3. Collect and manage enterprise data according to the data management plan;

[0047] S4. Perform data analysis on the governed data and generate recommendations for data governance needs. Through the above steps, data governance is regarded as a dynamically changing system, and real-time response to changes in the data environment can effectively improve data governance efficiency and accuracy; as the business of the enterprise develops, according to the data governance needs of the enterprise, intelligent decision-making to meet the needs of data governance solutions can dynamically adapt to enterprise changes, improve governance efficiency, and provide data-driven decision-making recommendations to better meet business needs, so as to effectively improve data governance effects and governance efficiency.

[0048] In addition, data governance data is collected regularly and after the enterprise's data governance needs are met to create training samples for iterative training of the dynamic governance model. Regularly collecting data governance data for iterative training of the dynamic governance model will help the dynamic governance model adapt to changes in the enterprise data environment, improve the model's accuracy and generalization capabilities, and further enhance the effectiveness and efficiency of data governance.

[0049] In addition, regularly collecting data governance data to iteratively train the dynamic governance model can also help maintain the timeliness and compliance of the model. Regulations and policies related to data governance are constantly being improved. Regularly collecting data and training models can ensure that the model is aware of the requirements of regulatory changes for data governance in a timely manner. For example, when data protection regulations require companies to strengthen the encrypted storage and access audit of user personal information, by timely updating training samples and iterating models, the model can reflect these compliance requirements in the generated data governance plan to help companies avoid legal risks. Data governance best practices in the industry are also constantly evolving. By collecting data and training the model in combination with the latest industry experience, the company's data governance work can be synchronized with the industry's advanced level. For example, when new data quality assessment tools or methods are widely adopted in the industry, the model can learn these new practices and recommend their use in the company's data governance, thereby improving the overall level of the company's data governance.

[0050] The above data governance strategy can be any one of the following: data quality optimization strategy, data security enhancement strategy, data storage and processing optimization strategy, data sharing and collaboration strategy, and data emergency response strategy. According to the various data governance strategies in the data governance plan, the enterprise data can be efficiently governed. Through the data quality optimization strategy, the data accuracy can be improved, the data integrity can be enhanced, and the data consistency can be ensured; through the data security enhancement strategy, sensitive information can be effectively protected, data confidentiality can be guaranteed, and data can be quickly restored in the event of data loss or damage, so that the enterprise business can return to normal as soon as possible to ensure business continuity; through the data storage and processing optimization strategy, storage efficiency can be effectively improved, data processing efficiency can be accelerated, and data access performance can be improved; through the data sharing and collaboration strategy, data sharing mechanisms and collaboration processes can be established to break down data barriers between departments, promote information flow, improve data utilization, and promote innovation and knowledge sharing; and through the data emergency response strategy, when data emergencies occur, crises can be quickly responded to, business risks can be reduced, and enterprise resilience can be enhanced.

[0051] Reference Figure 2 The method of generating a data governance solution through a preset dynamic governance model decision according to data governance requirements specifically includes the following steps:

[0052] A1. Generate alternative governance solutions: Generate multiple alternative governance solutions that meet data governance requirements through pre-set dynamic governance model decisions based on data governance requirements;

[0053] A2. Evaluation of alternative governance solutions: Evaluate each alternative governance solution and calculate the evaluation value of each alternative governance solution using a preset evaluation value calculation formula;

[0054] A3. Select data governance plan: Sort the alternative governance plans based on the evaluation value, and select the alternative governance plan with the highest evaluation value as the data governance plan. Through the above steps, according to the actual needs of the enterprise, intelligent decision-making can dynamically adapt to enterprise changes, improve governance efficiency, and provide data-driven decision-making suggestions to better meet business needs, thereby effectively improving data governance effects and governance efficiency.

[0055] Reference Figure 3 The step of evaluating each candidate governance solution and calculating the evaluation value of each candidate governance solution by using a preset evaluation value calculation formula specifically includes the following steps:

[0056] B1. Evaluation and ranking: Evaluate and rank each alternative governance solution based on technical feasibility, economic cost-effectiveness, data governance effect, implementation cycle, and risk level, and obtain a technical feasibility sequence, an economic cost-effectiveness sequence, a data governance effect sequence, an implementation cycle sequence, and a risk level sequence;

[0057] B2. Obtain indicators: Obtain the technical feasibility sequence indicators S of each alternative treatment solution by comparing with the preset sequence indicator comparison table. TFi , Economic cost-benefit sequence index S ECBi , Data governance effect sequence indicator S DGEi 、Implementation of cycle sequence indicator S ICi And the risk level sequence index S RDi ;

[0058] B3. Weight allocation: Weight allocation is performed on each of the above sequences to obtain the weight W of the technical feasibility sequence of each alternative governance solution. TF , economic cost-benefit sequence weight W ECB , Data governance effect sequence weight W DGE , implement the periodic sequence weight W IC and the risk level sequence weight W RD , and W TF +W DGE +W ECB +W IC +W RD =1;

[0059] B4. Calculate the evaluation value: Calculate the evaluation value of each alternative governance solution using the preset evaluation value calculation formula. The above evaluation value calculation formula is as follows:

[0060] V i =W TF *S TFi +W ECB *S ECBi +W DGE *SDGEi +W IG *S ICi +W RD *S RDi ;

[0061] Among them, V i is the evaluation value of the i-th alternative governance solution. The dynamic governance model generates multiple alternative governance solutions based on technical feasibility, economic cost-effectiveness, data governance effect, implementation cycle and risk level. Multiple solutions can be compared quantitatively, and weights are assigned to multiple evaluation directions based on the actual situation of the enterprise, and then the evaluation value of each alternative governance solution is calculated. On the basis of a comprehensive and intelligent scoring of each alternative, the governance tendency of the enterprise is taken into account, which helps to improve the scientificity and rationality of the scoring of the alternative governance solutions, and meet the business needs of the enterprise on the basis of dynamically adapting to the changes of the enterprise and improving the efficiency of data governance.

[0062] Reference Figure 4 The weight allocation for each of the above sequences specifically includes the following steps:

[0063] C1. Collecting questionnaires: Sending questionnaires to managers to confirm governance preferences and obtain governance preference questionnaires;

[0064] C2. Generate recommended weights: Collect corporate financial report information, input the governance tendency questionnaire, corporate financial report information and data governance requirements into a pre-set weight allocation model to generate recommended weights; the weight allocation model is a machine learning model trained by historical weight allocation data; the specific training steps of the machine learning model are prior art and will not be elaborated here;

[0065] C3. Weight allocation: The recommended weights are sent to the management personnel, and after confirmation or modification, the weights are allocated to each sequence. The weight allocation model is based on the company's financial report information, the management personnel questionnaire information and the data governance needs, and the company's strategic focus is determined to generate recommended weights. Scientific weight allocation recommendations are provided for each evaluation direction. Combined with the company's own evaluation bias, scientific evaluation of each alternative governance plan is achieved, which helps to screen out the best data governance plan and effectively improve the data governance effect and governance efficiency.

[0066] For example, if the enterprise is currently facing strict supervision of data security regulations, the weight of data governance effect (especially data security and privacy protection) may be higher; if the enterprise is short of funds, the weight of economic cost-effectiveness may account for a larger proportion. The sum of the weights should be 1, and the weight allocation model is used to achieve accurate and efficient weight allocation recommendations.

[0067] In addition, the above step S4 performs data analysis on the governed data to generate the recommendations required for data governance needs, specifically including: performing correlation analysis on the governed data through a preset data analysis model, selecting relevant data related to the data governance needs, and using the data analysis model to mine and analyze the relevant data based on the data governance needs to generate the recommendations required for the data governance needs; the data analysis model is a machine learning model trained through historical recommendation data. Analyzing the governed data can mine the hidden patterns, trends, and associations in the data, provide in-depth insights for the enterprise, help the enterprise establish a data-driven decision-making culture, promote the close integration of data governance and business goals, and further expand the results based on meeting the data governance needs with a data-oriented approach.

[0068] For example, data processing requirements require optimization of data processing processes, by analyzing the flow and changes of data in business processes, identifying bottlenecks and inefficient links in the process. For example, in the procurement process, analyze the time interval data from the generation of purchase requisitions to purchase orders, as well as the frequency of data modification in each link, find out the key steps that cause process delays, and generate suggestions.

[0069] Another example is that data processing needs require the management of business data to increase product sales, and cluster analysis is used to classify customers according to factors such as purchasing behavior, consumption amount, and region, to explore the characteristics and preferences of different customer groups. For example, by analyzing the categories and frequency of customer purchases, customers can be divided into different groups such as high-value frequent purchasers and low-value occasional purchasers. Based on customer segmentation, precise marketing strategy suggestions are provided to the marketing department.

[0070] The embodiment of the present application also discloses a computer-readable storage medium, which stores a computer program that can be loaded by a processor and executed as in the above method. The computer-readable storage medium includes, for example: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0071] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the scope of protection of the invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on these embodiments, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in this field can still combine, add, delete or make other adjustments to the features in the various embodiments of the present invention according to the circumstances without conflict, without making creative work, so as to obtain different other technical solutions that do not deviate from the concept of the present invention in essence, and these technical solutions also belong to the scope of protection of the present invention.

Claims

1. A method for enterprise management data governance based on big data analysis, characterized in that: The following steps are involved: Obtain the data governance needs of the enterprise; Generate a data governance solution through a preset dynamic governance model decision according to data governance needs, wherein the dynamic governance model decision is obtained by iterative training of a machine learning model through the enterprise data governance historical data; the data governance solution includes at least one data governance strategy, data governance indicator, data governance tool and data governance process; Collect and manage enterprise data according to the data governance plan; Perform data analysis on the governed data and generate recommendations for data governance needs.

2. According to the enterprise management data governance method based on big data analysis according to claim 1, it is characterized in that: It also includes collecting data governance data regularly and after completing the company's data governance needs, creating training samples and iteratively training the dynamic governance model.

3. According to the enterprise management data governance method based on big data analysis according to claim 1, it is characterized in that: The data governance strategy can be any one of a data quality optimization strategy, a data security enhancement strategy, a data storage and processing optimization strategy, a data sharing and collaboration strategy, and a data emergency response strategy.

4. The enterprise management data governance method based on big data analysis according to claim 1 is characterized in that: The method of generating a data governance solution by using a preset dynamic governance model decision according to data governance requirements specifically includes the following steps: Generate multiple alternative governance solutions that meet data governance needs through pre-set dynamic governance model decisions according to data governance needs; Evaluate each alternative governance plan and calculate the evaluation value of each alternative governance plan using a preset evaluation value calculation formula; The alternative governance solutions are ranked based on the evaluation values, and the alternative governance solution with the highest evaluation value is selected as the data governance solution.

5. The enterprise management data governance method based on big data analysis according to claim 4 is characterized in that: The step of evaluating each candidate governance solution and calculating the evaluation value of each candidate governance solution by using a preset evaluation value calculation formula specifically includes the following steps: Evaluate and rank each alternative governance solution based on technical feasibility, economic cost-effectiveness, data governance effect, implementation cycle, and risk level, and obtain a technical feasibility sequence, an economic cost-effectiveness sequence, a data governance effect sequence, an implementation cycle sequence, and a risk level sequence; Obtain the technical feasibility sequence index S of each alternative treatment solution by comparing it with the preset sequence index comparison table TFi , Economic cost-benefit sequence index S ECBi , Data governance effect sequence indicator S DGEi 、Implementation cycle sequence indicator S ICi And the risk level sequence index S RDi ; Assign weights to the above sequences to obtain the technical feasibility sequence weights W of each alternative governance solution: TF , economic cost-benefit sequence weight W ECB , Data governance effect sequence weight W DGE , implement the periodic sequence weight W IC and the risk level sequence weight W RD , and W TF +W DGE +W ECB +W IC +W RD =1; The evaluation value of each alternative governance solution is calculated using a preset evaluation value calculation formula.

6. The enterprise management data governance method based on big data analysis according to claim 5 is characterized in that: The evaluation value calculation formula is specifically: V i =W TF *S TFi +W ECB *S ECBi +W DGE *S DGEi +W IG *S ICi +W RD *S RDi ; Among them, V i is the evaluation value of the i-th alternative governance solution.

7. The enterprise management data governance method based on big data analysis according to claim 5 is characterized in that: The weight allocation for each of the above sequences specifically includes the following steps: Sending questionnaires to managers to confirm governance preferences and obtain governance preference questionnaires; Collect corporate financial report information, input the governance tendency questionnaire, corporate financial report information and data governance requirements into a pre-set weight allocation model to generate recommended weights; the weight allocation model is a machine learning model trained through historical weight allocation data; The recommended weights are sent to the management staff, and after confirmation or modification, the weights are assigned to each sequence.

8. The enterprise management data governance method based on big data analysis according to claim 1 is characterized in that: The data analysis of the governed data to generate the recommendations required for the data governance needs specifically includes: performing correlation analysis on the governed data through a preset data analysis model, selecting relevant data related to the data governance needs, and using the data analysis model to mine and analyze the relevant data based on the data governance needs to generate the recommendations required for the data governance needs; the data analysis model is a machine learning model trained through historical recommendation data.

9. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 8.

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