Root cause analysis model construction method
By constructing a root cause analysis model, the problem of data not being able to be dynamically located was solved, enabling data trend analysis and decision support, and improving decision-making capabilities in marketing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-04-10
AI Technical Summary
Existing technologies cannot achieve dynamic and flexible data location, cannot effectively perform root cause analysis, and cannot support efficient decision-making.
Build a root cause analysis model, including creating account detail tables, dimension tables, integrated models, attribution configuration information models, and indicator models, and conduct penetrating analysis by combining graphical displays.
It enables dynamic data analysis, accurately explores data trends, provides strong support for decision-making, and improves the accuracy and efficiency of decision-making.
Smart Images
Figure CN121836765A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of business analysis in marketing, and more particularly to a method for constructing a root cause analysis model. Background Technology
[0002] In the marketing context, root cause analysis has emerged as a functional approach, defined as "an analytical method for understanding how business dimensions contribute to a specific business objective." Its goal is to reveal the causes of data across various business dimensions, providing decision support for enterprises and ultimately achieving a competitive market advantage. Root cause analysis, through the collection and analysis of large amounts of data combined with analytical dimensions, explores the dynamics and trends of the data to better understand the generation of drivers, providing strong support for subsequent decision-making.
[0003] As businesses grow, they accumulate a large amount of operational analysis data. This analysis includes traditional reports and graphical displays such as line charts and pie charts. However, neither reports nor graphs can dynamically and flexibly pinpoint the causes of data anomalies, thus hindering the analysis of the root causes of abnormal data. Therefore, a "root cause analysis" model was designed, incorporating the characteristics of fishbone diagrams.
[0004] Existing technical solutions: 1. Existing technologies mostly use conventional reports to display data.
[0005] 2. Currently, static pie charts and line charts are generated based on the distribution of the data.
[0006] 3. Currently, it is impossible to dynamically display the driving forces behind the data, thus failing to provide efficient support for decision-making.
[0007] The existing technology has the following drawbacks: the current data display is only static data, and the composition of the data cannot be obtained; some reports have data dimensions, but cannot realize the causes of dynamic dimensions; there is no effective analysis model to support the root cause analysis, and therefore it cannot support decision-making. Summary of the Invention
[0008] In view of the above problems, the present invention is proposed to provide a method for constructing a root cause analysis model to overcome or at least partially solve the above problems.
[0009] According to one aspect of the present invention, a method for constructing a root cause analysis model is provided, the method comprising: Create an account details table model; Create a dimension table model; Create an integrated model; Create an attribution configuration information model table model; Create an indicator model table; Perform a thorough root cause analysis.
[0010] Optionally, the creation of the account details table model specifically includes: The business account details table stores detailed business data; Create model table data.
[0011] Optionally, the creation of the dimension table model specifically includes: The dimension table stores dimension data, providing analytical dimensions for subsequent root cause analysis. The product dimension table stores product dimension data, providing analytical dimensions for subsequent root cause analysis. The organization dimension table stores organization dimension data, providing analytical dimensions for subsequent root cause analysis. The bar dimension table stores bar dimension data, providing analytical dimensions for subsequent root cause analysis. The currency dimension table stores currency dimension data, providing analytical dimensions for subsequent root cause analysis.
[0012] Optionally, the creation of the integrated model specifically includes: The integrated model stores detailed business data and integrates the data according to the dimensions that need to be analyzed; Summarize the data in the model table.
[0013] Optionally, the creation of the attribution configuration information model table specifically includes: The attribution configuration information model storage requires attribution analysis to integrate data according to the dimensions to be analyzed; Summarize the data in the model table.
[0014] Optionally, the creation of the indicator model table specifically includes: The indicator model table establishes a link between the business summary model and the dimensional model to prepare for subsequent indicator configuration. The model table is generated through configuration; the model is both a physical table and a physical view. Create a physical table model; Create an indicator model table; Summarize the data in the model table.
[0015] Optionally, the root cause analysis specifically includes: Based on the indicator information configured in the attribution configuration information model table; Based on the attribution configuration information table, drill down to display the configured metrics and dimensions according to the configured dimensions; The composition of each dimension can be analyzed by drilling down the displayed content. The graphics are displayed in tree and fishbone shapes; The root cause analysis model is designed and graphically presented to show the reasons for the data changes.
[0016] This invention provides a method for constructing a root cause analysis model. The method includes: creating an account details table model; creating a dimension table model; creating an integrated model; creating an attribution configuration information table model; creating an indicator model table; and performing root cause analysis. Root cause analysis, by collecting and analyzing large amounts of data combined with analytical dimensions, explores the dynamics and trends of the data to better understand the generation of driving forces and provides strong support for subsequent decision-making.
[0017] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description
[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 A flowchart illustrating a root cause analysis model construction method provided in an embodiment of the present invention. Detailed Implementation
[0020] Exemplary embodiments of the present disclosure will now be described in more detail 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 implemented 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.
[0021] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.
[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.
[0023] In the marketing context, root cause analysis has emerged as a functional approach, defined as "an analytical method for understanding a product's relevance to a specific target market." Its goal is to reveal consumer demand for a product or service, providing decision support for businesses and ultimately achieving a competitive market advantage. Root cause analysis, through the collection and analysis of large amounts of data combined with various analytical dimensions, explores the dynamics and trends of the data to better understand the drivers behind its development, providing strong support for subsequent decision-making.
[0024] As businesses grow, they accumulate a large amount of operational analysis data. This analysis includes traditional reports and graphical displays such as line charts and pie charts. However, neither reports nor graphs can dynamically and flexibly pinpoint the causes of data anomalies, thus hindering the analysis of the root causes of abnormal data. Therefore, a "root cause analysis" model was designed, incorporating the characteristics of fishbone diagrams.
[0025] Business Process Overall business process Root cause analysis focuses on operational metrics data generated from business data.
[0026] Business data contains dimensional information, such as products, subjects, organizations, and business lines.
[0027] Because business data contains many dimensions such as term, interest accrual date, contract number, and account number, which are essential for root cause analysis, and considering factors such as product factors, customer factors, and account factors, dimensionality reduction is necessary to eliminate unnecessary dimensions.
[0028] The dimensionality reduction model can generate indicator data, such as simulated profit and operating revenue for institutional clients' products.
[0029] When an organization's simulated profits are particularly outstanding, and a significant contribution is needed to that product, a root cause analysis model needs to be triggered.
[0030] Before root cause analysis is triggered, a new attribution configuration needs to be created, including attribution metrics, organizational level, and attribution dimensions.
[0031] Root cause analysis models need to be combined with dimension tables to analyze the specific dimensions that influence the causes.
[0032] The process of constructing a root cause analysis model is as follows: Figure 1 As shown, the physical model involved in the specific process design is as follows: Business account details for the data mart; The dimension table stores the dimensions that need to be stored as causes, such as: subject, product, customer, organization; An integrated table, a summary table generated by reducing dimensions; Indicator information table, containing operational indicators that require business analysis; The attribution configuration information table stores the indicator information that needs to be configured for attribution analysis.
[0033] Step 1: Create an account details table model; 1.1 The business account details table stores detailed business data. 1.2 Sample data for the model table is as follows: Step 2: Create the dimension table model 2.1 Dimension tables store dimensional data, providing analytical dimensions for subsequent root cause analysis. The following is a sample of data for the dimension table model table: 2.2 The product dimension table stores product dimension data, providing analytical dimensions for subsequent root cause analysis. The following is a sample of data from the product dimension table model table: 2.3 The organizational dimension table stores organizational dimension data, providing analytical dimensions for subsequent root cause analysis. The following is a sample of the data in the organizational dimension table model: 2.4 The line dimension table stores line dimension data, providing analytical dimensions for subsequent root cause analysis. The following is a sample of data from the bar dimension table model: 2.5 The currency dimension table stores currency dimension data, providing analytical dimensions for subsequent root cause analysis. The following is a sample of the currency dimension table model data: Step 3: Create an integration model 3.1 Integrate the detailed business data of the integrated model storage according to the dimensions to be analyzed, so as to lay a good foundation for subsequent analysis; 3.2 A sample of the summary model table data is as follows: Step 4: Create the attribution configuration information model table 4.1 The attribution configuration information model storage requires attribution analysis to integrate data according to the dimensions to be analyzed, laying a good foundation for subsequent analysis; 4.2 A sample of the summary model table data is as follows: Step 5: Indicator Model Table 5.1 The purpose of the indicator model table is to: establish a link between the business summary model and the dimensional model in preparation for subsequent indicator configuration; 5.2 This model table is generated through configuration; 5.3 This model is both a physical table and a physical view (dynamic statement); 5.4 Physical Table Model 5.5 Indicator Model Table 5.6 A sample of the summary model table data is as follows: Step 6: Root Cause Analysis 6.1 Based on the indicator information configured in the attribution configuration information model table; 6.2 Based on the attribution configuration information table, drill down and display the configured metrics and dimensions according to the configured dimensions; 6.3 Drilling down the display allows for analysis of the composition of each dimension; the graphical display can be a tree structure or a fishbone structure.
[0034] 6.4 By designing a root cause analysis model and displaying it graphically, the reasons for data changes are revealed, providing strong support for subsequent leadership decision-making.
[0035] Beneficial effects: In a complex market environment, the development of various businesses places high demands on decision-making capabilities. Effective decision-making requires accurate data support, and accurate data support necessitates precise identification and analysis of the causes of each data point.
[0036] Current analytical approaches that rely on static data are insufficient to meet the demands of highly dynamic markets. This invention effectively solves this problem by employing dynamic root cause analysis, combined with graphical representations, to provide a clear and comprehensive analysis of the data.
[0037] The emergence of root cause analysis of data enables analysis of all dimensions of data generation, ultimately uncovering the root causes of data generation, predicting data trends, and providing support for decision-making.
[0038] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for constructing a root cause analysis model, characterized by, The construction method includes: Create an account details table model; Create a dimension table model; Create an integrated model; Create an attribution configuration information model table model; Create an indicator model table; Perform a thorough root cause analysis.
2. The root cause analysis model construction method of claim 1, wherein, The creation of the account details table model specifically includes: The business account details table stores detailed business data; Create model table data.
3. The root cause analysis model construction method of claim 1, wherein, The creation of the dimension table model specifically includes: The dimension table stores dimension data, providing analytical dimensions for subsequent root cause analysis. The product dimension table stores product dimension data, providing analytical dimensions for subsequent root cause analysis. The organization dimension table stores organization dimension data, providing analytical dimensions for subsequent root cause analysis. The bar dimension table stores bar dimension data, providing analytical dimensions for subsequent root cause analysis. The currency dimension table stores currency dimension data, providing analytical dimensions for subsequent root cause analysis.
4. The root cause analysis model construction method of claim 1, wherein, The creation of the integrated model specifically includes: The integrated model stores detailed business data and integrates the data according to the dimensions that need to be analyzed; Summarize the data in the model table.
5. The method for constructing a root cause analysis model according to claim 1, characterized in that, The creation of the attribution configuration information model table specifically includes: The attribution configuration information model storage requires attribution analysis to integrate data according to the dimensions to be analyzed; Summarize the data in the model table.
6. The method for constructing a root cause analysis model according to claim 1, characterized in that, The creation of the indicator model table specifically includes: The indicator model table establishes a link between the business summary model and the dimensional model to prepare for subsequent indicator configuration. The model table is generated through configuration; the model is both a physical table and a physical view. Create a physical table model; Create an indicator model table; Summarize the data in the model table.
7. The method for constructing a root cause analysis model according to claim 1, characterized in that, The specific steps of conducting root cause analysis include: Based on the indicator information configured in the attribution configuration information model table; Based on the attribution configuration information table, drill down to display the configured metrics and dimensions according to the configured dimensions; The composition of each dimension can be analyzed by drilling down the displayed content. The graphics are displayed in tree and fishbone shapes; The root cause analysis model is designed and graphically presented to show the reasons for the data changes.