Business and financial data governance optimization method for dynamic management of main data

Abstract

The invention relates to the technical field of data management, and discloses a business and financial data governance optimization method for main data dynamic management, which comprises the following steps: step 1, carrying out multi-source data acquisition, including business system data acquisition, financial system data acquisition and other channel data acquisition, step 2, pre-processing the acquired data, numbering and storing, and step 3, carrying out data management on the acquired data. 3, calculating a data quality index, a data change condition, a data value and a data comprehensive coefficient based on the stored data set, and 4, performing feedback according to the data comprehensive coefficient, so that various data sources inside and outside an enterprise can be comprehensively covered, a data island phenomenon is reduced, and meanwhile, the quality and credibility of data are improved; the data comprehensive coefficient is comprehensively evaluated by combining the data quality index, the data change condition and the data value, the data level is comprehensively reflected, a comprehensive perspective is provided for enterprises to see the data, and the enterprises can better understand and manage the data and find potential problems and opportunities.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to a business and financial data governance optimization method for dynamic management of master data. Background Art

[0002] As enterprises expand and their businesses diversify, master data, a crucial pillar of core business operations, continues to grow in volume and complexity. Master data not only encompasses various internal business systems but is also closely tied to key areas such as finance, customers, and suppliers. Therefore, dynamic management of master data is crucial for improving operational efficiency, ensuring data quality, and achieving business collaboration.

[0003] The development of big data and cloud computing technologies has provided strong technical support for the dynamic management of master data. Through cloud platforms and big data technologies, enterprises can achieve efficient storage, processing, and analysis of master data, thereby better supporting their business operations and financial management. As data governance concepts gain widespread adoption and penetration, more and more companies are establishing their own data governance frameworks and standards. This provides clear guidance and standards for the dynamic management of master data, helping enterprises achieve effective data management and utilization.

[0004] The sources of master data management are complex. Due to multi-source data collection and entry, data from different systems or departments may have differences in format, standards, and content, resulting in data inconsistency, affecting the accuracy and credibility of the data, and thus affecting the accuracy of business decisions. At the same time, existing data management lacks an effective data quality assessment mechanism and cannot accurately quantify key data indicators, resulting in data problems that are difficult to discover and correct in a timely manner, affecting the use value of the data. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides a business and financial data governance optimization method for dynamic management of master data. By collecting data from multiple systems and channels, it can comprehensively cover various data sources inside and outside the enterprise, reduce the phenomenon of data silos, and at the same time improve the quality and credibility of the data. It combines data quality indicators, data changes, and data value to comprehensively evaluate the data comprehensive coefficient, comprehensively reflect the level of the data, and provide enterprises with a comprehensive perspective to view the data, which helps enterprises better understand and manage data and discover potential problems and opportunities.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a business and financial data governance optimization method for dynamic master data management, comprising the following steps:

[0009] Step 1: Collect data from multiple sources, including business system data, financial system data, and other channels;

[0010] Step 2: Pre-process the collected data and save them by number;

[0011] Step 3: Calculate data quality indicators, data changes, data value, and data comprehensive coefficient based on the saved data set;

[0012] Step 4: Provide feedback based on the comprehensive coefficient of the data.

[0013] Preferably, the data preprocessing includes:

[0014] a1. Establish standard format specifications for master data, perform format check and conversion on collected data, and automatically or manually convert data that does not conform to the format specifications to ensure data consistency;

[0015] a2. Use a data duplication detection algorithm to identify and merge duplicate master data records.

[0016] a3. Establish data quality monitoring rules to mark and prompt obviously erroneous data.

[0017] Preferably, the business system data is stored in the following format: Ys1, Ys2, Ys3, ..., Ys n , where Ys1 represents the first data in the business system data, Ys n Represents the nth data in the business system data.

[0018] Preferably, the numbering format of the financial system data is: Cs1, Cs2, Cs3, ..., Cs n , where Cs1 represents the first data in the financial system data, Cs n Represents the nth data in the financial system data.

[0019] Preferably, the numbering format of the other channel data is: Ts1, Ts2, Ts3, ..., Ts n , where Ts1 represents the first data in other channel data, Ts n Represents the nth data in other channel data.

[0020] Preferably, the calculation formula of the data quality index is:

[0021]

[0022] In the calculation formula, ZZzb i Represents the i-th data quality indicator, i = Ys, Cs, Ts, Sz Represents the total amount of data, S c Represents the amount of erroneous data, S f Represents the amount of non-empty data, S θ Represents the amount of data that complies with business rules, S g Represents the amount of data updated within the time limit, S s Represents the amount of repeated data, S h represents the amount of data that meets the format and domain value requirements, α1, α2, α3, α4, α5, and α6 represent weights, α1+α2+α3+α4+α5+α6=1.

[0023] Preferably, the calculation formula for the data change is:

[0024]

[0025] In the calculation formula, SJbh i Represents the change of the i-th data, i = Ys, Cs, Ts, S b represents the number of changes in a cycle, T represents the statistical duration, S b / T represents the frequency of change, S l Represents the maximum historical change frequency, ∈ represents the minimum amount of zero prevention, S new Represents the new value after the change, S old represents the old value before the change, ∈ represents the small amount of zero removal, S x Represents the standard deviation of historical changes, represents the mean of historical changes, β1, β2, and β3 represent weights, and β1+β2+β3=1.

[0026] Preferably, the calculation formula for the data value is:

[0027]

[0028] In the calculation formula, SJjz i Represents the data value of the i-th data, i = Ys, Cs, Ts, W k Represents the weight of the business scenario, U k Represents the frequency of data calls in the scene, U v represents the benchmark call volume, k represents the application scenario, m represents the number of scenarios, σ represents the weight of the revenue, R represents data-driven revenue, S d represents the loss reduced due to the improvement of data quality, γ1 and γ2 represent weights, γ1+γ2=1.

[0029] Preferably, the calculation formula of the data comprehensive coefficient is:

[0030] SJjz=μ1·ZZzb Ys+Cs+Tsi+μ2·SJbh Ys+Cs+Tsi +μ3·SJjz Ys+Cs+Tsi

[0031] In the calculation formula, SJjz represents the data comprehensive coefficient, ZZzb Ys+Cs+Tsi Represents the overall data quality index of business system data, financial system data, and other channel data, SJbh Ys+Cs+Tsi Represents the data changes of business system data, financial system data, and other channel data, SJjz Ys+Cs+Tsi Represents the data value of business system data, financial system data, and other channel data;

[0032] μ1, μ2, and μ3 represent weights, μ1+μ2+μ3=1.

[0033] Preferably, the data comprehensive coefficient generates a data evaluation report and distributes it to the business department and the finance department.

[0034] Compared with the existing technology, the present invention provides a business and financial data governance optimization method for dynamic management of master data, which has the following beneficial effects:

[0035] 1. By collecting data from multiple systems and channels, the present invention can comprehensively cover various data sources inside and outside the enterprise, reduce data silos, and at the same time, data preprocessing improves data quality and credibility.

[0036] 2. The present invention calculates data quality indicators based on the saved data sets, which can quantitatively evaluate the quality level of the data, help to discover potential problems and defects in the data, and provide a basis for the optimization and improvement of the main data. By calculating the data changes, the dynamic changes of the main data can be monitored in real time, which helps enterprises to promptly discover the changing trends and abnormal fluctuations of the data, and provide strong support for the dynamic management of the main data. By calculating the data value based on the saved data sets, it can provide enterprises with more intuitive and accurate data value information, improve the application value and economic benefits of the main data, and comprehensively evaluate the data comprehensive coefficient by combining data quality indicators, data changes, and data value to comprehensively reflect the level of the data, provide enterprises with a comprehensive perspective to view the data, help enterprises better understand and manage data, and discover potential problems and opportunities. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a step diagram of the method of the present invention;

[0038] Figure 2 This is a diagram of the data preprocessing steps in the method of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] See also Figure 1-2 A business and financial data governance optimization method for dynamic master data management includes the following steps:

[0041] Step 1: Collect data from multiple sources, including business system data, financial system data, and other channels;

[0042] The business system data includes multiple data obtained through the business system, the financial system data includes multiple data obtained through the financial system, and the other channel data includes multiple data obtained from third-party systems other than the business system and the financial system;

[0043] Step 2: Pre-process the collected data and save them by number;

[0044] Data preprocessing includes:

[0045] a1. Establish standard formats for master data and perform format checks and conversions on collected data. For example, standardize the date format to "YYYY-MM-DD" and the numeric format to specific decimal places and precision requirements. Automatically or manually convert data that does not conform to the format specifications to ensure data consistency.

[0046] a2. Use a data duplication detection algorithm to identify and merge duplicate master data records. For example, duplicate customer records are identified based on their unique identifier (such as ID number, tax number, etc.), and the relevant information is integrated into a complete record to avoid data redundancy.

[0047] a3. Establish data quality monitoring rules to flag and indicate obvious errors. For example, if a product price is negative or outside a reasonable range, the system will automatically issue an alarm and a dedicated person will verify and correct it. For data that cannot be confirmed to be erroneous, it will be temporarily stored and further investigated and verified before being processed.

[0048] The business system data is stored in the following format: Ys1, Ys2, Ys3,..., Ys n , where Ys1 represents the first data in the business system data, Ys n Represents the nth data in the business system data;

[0049] The numbering format for financial system data is: Cs1, Cs2, Cs3,..., Cs n , where Cs1 represents the first data in the financial system data, Cs n Represents the nth data in the financial system data;

[0050] The numbering format for other channel data is: Ts1, Ts2, Ts3, ···, Ts n , where Ts1 represents the first data in other channel data, Ts n Represents the nth data in other channel data;

[0051] By collecting data from multiple systems and channels, we can fully cover various data sources inside and outside the enterprise, reducing data silos. At the same time, data preprocessing improves data quality and credibility.

[0052] Step 3: Calculate data quality indicators, data changes, data value, and data comprehensive coefficient based on the saved data;

[0053] The calculation formula of data quality index is:

[0054]

[0055] In the calculation formula, ZZzb i Represents the i-th data quality indicator, i = Ys, Cs, Ts, S z Represents the total amount of data, S c Represents the amount of erroneous data, S f Represents the amount of non-empty data, S θ Represents the amount of data that complies with business rules, S g Represents the amount of data updated within the time limit, S s Represents the amount of repeated data, S h represents the amount of data that meets the format and domain value requirements, α1, α2, α3, α4, α5, and α6 represent weights, α1+α2+α3+α4+α5+α6=1;

[0056] Represents the proportion of calculated incorrect data, and subtracts this proportion from 1 to obtain the proportion of correct data. α1 represents the weight of this part;

[0057] Represents the proportion of non-empty data, reflecting the integrity of the data, and α2 represents the weight of this part;

[0058] It represents the proportion of data that complies with business rules, reflecting the accuracy and compliance of the data. α3 represents the weight of this part.

[0059] Represents the update timeliness of the data, reflecting the update status of the data, and α4 represents the weight of this part; The proportion of non-duplicate data in the representative data calculation reflects the uniqueness of the data, and α5 represents the weight of this part;

[0060] It represents the proportion of data that meets the format and domain value requirements, reflecting whether the format and type of the data are correct. α6 represents the weight of this part;

[0061] Data quality is assessed by comprehensively considering multiple aspects, including accuracy, completeness, consistency, timeliness, and uniqueness. A comprehensive data quality score is calculated through weighted summation to help understand the overall quality of the data.

[0062] The calculation formula for data changes is:

[0063]

[0064] In the calculation formula, SJbh i Represents the change of the i-th data, i = Ys, Cs, Ts, S b represents the number of changes in a cycle, T represents the statistical duration, S b / T represents the frequency of change, S l represents the maximum historical change frequency, ∈ represents the minimum amount of zero prevention, S new Represents the new value after the change, S old represents the old value before the change, ∈ represents the elimination of zero small amount, S x Represents the standard deviation of historical changes, represents the mean of historical changes, β1, β2, and β3 represent weights, β1+β2+β3=1;

[0065] This reflects the relative frequency of data changes within a period. By combining the number of changes with the historical maximum change frequency and the statistical duration, and adding zero-minimum quantification, a standardized change frequency index is obtained, which represents the contribution of change frequency to the comprehensive index.

[0066] It reflects the mean change intensity of all data points in the data set and indicates the contribution of change intensity to the comprehensive index;

[0067] It reflects the relationship between current data changes and historical data fluctuations, and indicates the contribution of the degree of fluctuation in the comprehensive index;

[0068] By combining the relative frequency of data changes, the average change intensity of all data points in the dataset, and the relationship between current data changes and historical data fluctuations, a comprehensive data change assessment indicator is formed. By adjusting the weight parameters, the contribution of each aspect in the comprehensive indicator can be flexibly adjusted according to specific application scenarios and needs, comprehensively evaluating data variability.

[0069] The formula for calculating data value is:

[0070]

[0071] In the calculation formula, SJjz i Represents the data value of the i-th data, i = Ys, Cs, Ts, W k Represents the weight of the business scenario, U k Represents the frequency of data calls in the scene, U v represents the benchmark call volume, k represents the application scenario, m represents the number of scenarios, μ represents the weight of the revenue, R represents data-driven revenue, S d represents the loss reduced due to the improvement of data quality, γ1 and γ2 represent weights, γ1+γ2=1;

[0072] Considering the importance of data value in different business scenarios, [σ·R+(1-σ)·S d ] Considers the economic benefits that data brings to the enterprise directly or indirectly;

[0073] This formula comprehensively reflects the value of data by taking into account the importance of business scenarios, data call frequency, data-driven revenue, and loss reduction from improved data quality. By introducing weight parameters and adjustable revenue weights, the formula can adapt to different business scenarios and needs, providing strong flexibility.

[0074] The calculation formula of the data comprehensive coefficient is:

[0075] SJjz=μ1·ZZzb Ys+Cs+Tsi +μ2·SJbh Ys+Cs+Tsi +μ3·SJjz Ys+Cs+Tsi

[0076] In the calculation formula, SJjz represents the data comprehensive coefficient, ZZzb Ys+Cs+Tsi Represents the overall data quality index of business system data, financial system data, and other channel data, SJbh Ys+Cs+Tsi Represents the data changes of business system data, financial system data, and other channel data, SJjz Ys+Cs+Tsi Represents the data value of business system data, financial system data, and other channel data;

[0077] v1, μ2, and μ3 represent weights, μ1+μ2+μ3=1;

[0078] Calculating data quality indicators based on saved data sets can quantitatively evaluate the quality level of data, help discover potential problems and defects in the data, and provide a basis for optimizing and improving master data. By calculating data changes, dynamic changes in master data can be monitored in real time, helping enterprises to promptly discover data change trends and abnormal fluctuations, and providing strong support for the dynamic management of master data. By calculating data value based on saved data sets, more intuitive and accurate data value information can be provided to enterprises, improving the application value and economic benefits of master data. The comprehensive evaluation of data comprehensive coefficients based on data quality indicators, data changes, and data value comprehensively reflects the level of data, providing enterprises with a comprehensive perspective on data, helping enterprises to better understand and manage data and discover potential problems and opportunities.

[0079] Step 4: Provide feedback based on the comprehensive data coefficient, generate a data evaluation report and distribute it to the business department and the finance department.

[0080] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A business and financial data governance optimization method for dynamic master data management, characterized by: The following steps are involved: Step 1: Collect data from multiple sources, including business system data, financial system data, and other channels; Step 2: Pre-process the collected data and save them by number; Step 3: Calculate data quality indicators, data changes, data value, and data comprehensive coefficient based on the saved data set; Step 4: Provide feedback based on the comprehensive coefficient of the data.

2. The business and financial data governance optimization method for dynamic master data management according to claim 1 is characterized by: The data preprocessing includes: a1. Establish standard format specifications for master data, perform format check and conversion on collected data, and automatically or manually convert data that does not conform to the format specifications to ensure data consistency; a2. Use a data duplication detection algorithm to identify and merge duplicate master data records. a3. Establish data quality monitoring rules to mark and prompt obviously erroneous data.

3. The business and financial data governance optimization method for dynamic master data management according to claim 2 is characterized by: The business system data is stored in the following format: Ys1, Ys2, Ys3, ..., Ys n , where Ys1 represents the first data in the business system data, Ys n Represents the nth data in the business system data.

4. The business and financial data governance optimization method for dynamic master data management according to claim 3 is characterized by: The numbering format of the financial system data is: Cs1, Cs2, Cs3,..., Cs n , Among them, Cs1 represents the first data in the financial system data, Cs n Represents the nth data in the financial system data.

5. The business and financial data governance optimization method for dynamic master data management according to claim 4 is characterized by: The numbering format of the other channel data is: Ts1, Ts2, Ts3, . . ., Ts n , where Ts1 represents the first data in other channel data, Ts n Represents the nth data in other channel data.

6. The business and financial data governance optimization method for dynamic master data management according to claim 5 is characterized by: The calculation formula of the data quality index is: In the calculation formula, ZZzb i Represents the i-th data quality indicator, i = Ys, Cs, Ts, S z Represents the total amount of data, S c Represents the amount of erroneous data, S f Represents the amount of non-empty data, S θ Represents the amount of data that complies with business rules, S g Represents the amount of data updated within the time limit, S s Represents the amount of repeated data, S h represents the amount of data that meets the format and domain value requirements, α1, α2, α3, α4, α5, and α6 represent weights, α1+α2+α3+α4+α5+α6=1.

7. The business and financial data governance optimization method for dynamic master data management according to claim 6 is characterized by: The calculation formula for the data change is: In the calculation formula, SJbh i Represents the change of the i-th data, i = Ys, Cs, Ts, S b represents the number of changes in a cycle, T represents the statistical duration, S b / T represents the frequency of change, S l Represents the maximum historical change frequency, ∈ represents the minimum amount of zero prevention, S new Represents the new value after the change, S old represents the old value before the change, ∈ represents the small amount of zero removal, S x Represents the standard deviation of historical changes, S θ represents the mean of historical changes, β1, β2, and β3 represent weights, and β1+β2+β3=1.

8. The business and financial data governance optimization method for dynamic master data management according to claim 7 is characterized by: The calculation formula for the data value is: In the calculation formula, SJjz i Represents the data value of the i-th data, i = Ys, Cs, Ts, W k Represents the weight of the business scenario, U k Represents the frequency of data calls in the scene, U v represents the benchmark call volume, k represents the application scenario, m represents the number of scenarios, σ represents the weight of the revenue, R represents data-driven revenue, S d represents the loss reduced due to the improvement of data quality, γ1 and γ2 represent weights, γ1+γ2=1.

9. The business and financial data governance optimization method for dynamic master data management according to claim 8, characterized in that: The calculation formula of the data comprehensive coefficient is: SJjz=μ1·ZZzb Ys+Cs+Tsi +μ2·SJbh Ys+Cs+Tsi +μ3·SJjz Ys+Cs+Tsi In the calculation formula, SJjz represents the data comprehensive coefficient, ZZzb Ys+Cs+Tsi Represents the overall data quality index of business system data, financial system data, and other channel data, SJbh Ys+Cs+Tsi Represents the data changes of business system data, financial system data, and other channel data, SJjz Ys+Cs+Tsi Represents the data value of business system data, financial system data, and other channel data; μ1, μ2, and μ3 represent weights, μ1+μ2+μ3=1.

10. The business and financial data governance optimization method for dynamic master data management according to claim 9, characterized in that: The data comprehensive coefficient generates a data evaluation report and distributes it to the business department and the financial department.

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