A data extraction method for enterprise business income

Abstract

The application provides a kind of data extraction method of enterprise business income, comprising: obtaining the index data corresponding to enterprise business income, respectively combing the relationship between index data in business system, to build data relationship model;According to the filtering rule and data relationship model, the data extraction of each business system is completed in turn to generate linear task structure;Business analysis decision model of enterprise business income is constructed, and the calculation level relationship between index data is combed in the associated business system data;Data relationship model and calculation level relationship are jointly used as the criterion and basis of ETL data extraction work;According to the requirement of business analysis decision model and calculation level relationship, multiple linear task structures are fused into scheduling tasks, so that a multi-level tree data extraction structure is combined.The application can convert single data extraction into a tree, multi-level and business meaningful three-dimensional structure model from a boring and meaningless linear structure.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method for extracting enterprise operating revenue data. Background Technology

[0002] Operating revenue is the foundation of a company's survival and development, and its control and management are of paramount importance in business management. With the growth and expansion of business operations and the application of information management tools in various business systems, the methods for controlling and managing operating revenue have become increasingly complex and diverse.

[0003] Inconsistencies in the construction and development of information systems have led to the independent existence of various information systems within an enterprise, resulting in the phenomenon of "data silos." To eliminate data silos and enhance the value of data in comprehensive enterprise management and decision support, data integration has become a necessary approach to data governance.

[0004] In data integration, data extraction is an essential and crucial step. Various technical methods exist for data extraction, the most commonly used being: web services (a type of application), REST (Representational State Transfer) services, and data platforms. However, because the data extracted using these methods is generally of a single structure and lacks interrelationships, the extracted data is not intuitive or comprehensive. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention provides a method for extracting enterprise operating revenue data.

[0006] The technical solution adopted in this invention is as follows:

[0007] A first aspect of the present invention provides a method for extracting enterprise operating revenue data, comprising: acquiring indicator data corresponding to enterprise operating revenue; associating the indicator data with corresponding business systems, wherein the indicator data includes at least one; sorting out the relationships between the indicator data in the business systems to construct a data relationship model; performing data transformation on the indicator data in the data relationship model in ETL (Extract-Transform-Load, an efficient real-time data processing tool); and sequentially extracting data from each business system according to filtering rules and the data relationship model, and processing the extracted data... Linear tasks are linearly linked into the extraction scheduling task to generate a linear task structure; a business analysis and decision-making model of enterprise operating revenue is constructed, and the top-level data requirements of the business analysis and decision-making model are decomposed step by step downwards, and the computational hierarchy relationship between the data to be extracted is sorted out in the related business system data; the data relationship model and the computational hierarchy relationship are used as the criteria and basis for ETL data extraction, and the extraction configuration of the data to be extracted is performed. The extraction tasks are executed serially as linear tasks. Then, according to the business analysis and decision-making model and the computational hierarchy relationship, multiple sets of linear task structures are merged into the scheduling task, thereby combining a multi-level tree-like data extraction structure.

[0008] According to one embodiment of the present invention, the indicator data includes: sales contracts, production plans, and the production value of the business unit's inbound and outbound products.

[0009] According to one embodiment of the present invention, sales contracts, production plans, and the production value of business units entering and leaving the warehouse are respectively linked to the Production Planning Management System (PMIS), the Marketing and Sales Management System (CMS), the Warehouse Management System, and the Financial and Price Management System.

[0010] According to one embodiment of the present invention, the above method further includes: visually presenting the tree-like data extraction structure using BI (Business Intelligence) tools.

[0011] The beneficial effects of this invention are as follows:

[0012] In the data extraction process, this invention clarifies the dependencies and relationships between data by sorting out the data relationship model, and constructs a linear data extraction relationship structure based on the data relationship model. Then, it integrates the business analysis model to sort out the computational hierarchy relationship between data, and constructs a tree-like, multi-level data extraction structure based on the data hierarchy relationship. This transforms the simple data extraction from a dry and meaningless linear structure into a tree-like, multi-level, and business-meaning three-dimensional structure model. Attached Figure Description

[0013] Figure 1This is a flowchart of a method for extracting enterprise operating revenue data according to an embodiment of the present invention;

[0014] Figure 2 This is a schematic diagram of a linear task structure according to a specific example of the present invention;

[0015] Figure 3 This is a schematic diagram of a tree-like data extraction structure according to a specific example of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Figure 1 This is a flowchart of a method for extracting enterprise operating revenue data according to an embodiment of the present invention, such as... Figure 1 As shown, this data extraction method includes the following steps:

[0018] S1. Obtain the indicator data corresponding to the company's operating revenue and associate the indicator data with the corresponding business system. The indicator data includes at least one.

[0019] In one specific embodiment of the present invention, the indicator data includes: sales contracts, production plans, and the production value of the business unit upon receipt and upon delivery. The sales contracts, production plans, and the production value of the business unit upon receipt and upon delivery are respectively linked to the Production Planning Management System (PMIS), the Marketing and Sales Management System (CMS), and the warehouse management and financial pricing management systems.

[0020] S2, analyze the relationships between the indicator data in the business system to build a data relationship model.

[0021] Specifically, indicator data is the cornerstone of the analytical model. Indicator data directly generates data relationships through business rules or operational logic. After combining or operating on the indicator data through data relationships, a data relationship model is formed. The data relationship is the business rules and operational logic on which this revenue analysis model is based.

[0022] S3 performs data transformation on indicator data in the data relationship model within ETL.

[0023] Specifically, data transformation is one of the most important fundamental functional components of ETL. Through data transformation, we can achieve end-to-end data transmission and data structure transformation, extracting and transforming business data stored on different servers and application systems, and then placing it into the execution data warehouse. In this invention, data transformation mainly completes the screening, filtering, cleaning, and transformation of business fact data. Each data transformation instance can be built with independent data transformation logic as needed, and a single data transformation and its internal logic form a relatively simple linear task structure.

[0024] S4, based on the filtering rules and data relationship model, sequentially extracts data from each business system and linearly connects the extracted data into the extraction scheduling task to generate a linear task structure.

[0025] Specifically, the filtering rules are determined based on business requirements and data validity. Data that is irrelevant to the business, redundant in the system, or contains noise is filtered out, effectively slimming down the data and improving the accuracy and computational efficiency. The filtering rules are constrained by the data relationship model, and data content related to data relationships cannot be filtered out.

[0026] For each system's extractable data content, specific data filtering requirements are defined. For example, in the sales system, contracts and contract details are each assigned a separate "transformation" task in the ETL tool. Filtering rules are defined, adhering to the dependency principle in the data relationship model: contracts precede contract details, and there is a strong dependency between contracts and details. This means that if contract data extraction fails, contract detail data extraction will not proceed. Finally, all "transformations" related to the current business analysis model from all sales contracts are linearly linked into the extraction scheduling task, forming a branching line within the extraction task. Then, extraction tasks for production plans, warehouse inbound / outbound data, and financial data are established one by one, and integrated to complete the basic business data extraction model. See [link to details]. Figure 2 As shown.

[0027] S5 constructs a business analysis and decision-making model for enterprise operating revenue, and decomposes the top-level data requirements of the business analysis and decision-making model step by step downwards, sorting out the calculation hierarchy relationship between the data to be extracted in the related business system data.

[0028] Specifically, the business analysis decision model is the target model that business analysis ultimately aims to achieve, namely the cumulative revenue completion rate model and the rhythm control model.

[0029] The business analysis and decision-making model for enterprise operating revenue of this invention covers data information from multiple business systems, including contracts, planning, production, supply chain, and inventory. From the three-dimensional perspective of the model, contracts and delivery are at the top level, and the data supporting delivery needs to be obtained from planning, production, and inventory. Production, in turn, depends on the supply chain. The process of deducing business dependencies and data operation relationships step by step is called decomposition.

[0030] The data to be extracted refers to the basic business data scattered across various business systems that is relevant to the business analysis and decision-making model. For example, operating revenue consists of two parts: new product sales and repair expenses, which exist in two separate systems. New product sales and repair expenses can be considered two indicators. The basic data for calculating these indicators may be scattered across different functions within the systems; that is, the top-level summary data for building the indicator model cannot necessarily be directly obtained from the system and needs to be calculated from the basic data aggregated from the business systems. Calculating the hierarchical relationship is the process of analyzing and sorting out the data relationships between the data to be extracted from systems in different business domains.

[0031] S6 uses the data relationship model and the computational hierarchy relationship as the criteria and basis for ETL data extraction. It configures the extraction of the data to be extracted, and the extraction tasks are executed in a linear sequence. Then, based on the business analysis decision model and the computational hierarchy relationship, multiple sets of linear task structures are integrated into the scheduling task, thereby combining a multi-level tree-like data extraction structure.

[0032] Specifically, when using ETL tools for data extraction, the first step is to analyze the relationships between business data and build a comprehensive data relationship model, which serves as the foundation for ETL data extraction. The data relationship model includes dependencies and associations between data. The data extraction task built based on this model is still a flat, linear structure. Because the data relationship model alone cannot reflect the hierarchical computational relationships between data, it is necessary to further analyze the hierarchical computational relationships between the data to be extracted, taking into account the requirements of the business analysis and decision-making model. The data relationship model and the hierarchical computational relationships are used together as the criteria and basis for ETL data extraction, guiding the configuration of business foundation data extraction within a single system or function. These extraction tasks are executed sequentially as linear tasks. Then, based on the requirements of the business analysis and decision-making model and the hierarchical computational relationships, multiple sets of linear task structures are integrated into the scheduling task, thus creating a multi-level, tree-like data extraction structure. (See [link to relevant documentation]). Figure 3 As shown.

[0033] After further integration with data relationship models and business analysis models, it also endows data extraction work with clearer business behaviors and allows data extraction work to evolve from a single linear work method into a more comprehensive and clear three-dimensional structure.

[0034] In other words, after the basic business data is extracted, no data association is formed. The data is merely aggregated together. It is still necessary to aggregate the data to form business analysis data based on the requirements of business relationships or logical operations. This process relies on the constructed tree-like basic business data extraction task model. Therefore, a new extraction task model is formed on top of the tree-like extraction task model of the basic business. The two models are nested together to form a three-dimensional, multi-level tree-like extraction structure.

[0035] In one embodiment of the present invention, the above method further includes: using a BI tool to visually represent the tree-like data extraction structure.

[0036] In summary, the data extraction method for enterprise operating revenue according to an embodiment of the present invention clarifies the dependencies and relationships between data by sorting out the data relationship model, constructs a linear data extraction relationship structure based on the data relationship model, integrates a business analysis model to sort out the hierarchical relationship between data, and constructs a tree-like, multi-level data extraction structure based on the hierarchical relationship between data. Thus, the single data extraction is transformed from a dry and meaningless linear structure into a tree-like, multi-level, three-dimensional structure model containing business meaning.

[0037] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0038] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0039] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0040] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for extracting enterprise operating revenue data, characterized in that, include: Obtain the indicator data corresponding to the company's operating revenue, and associate the indicator data with the corresponding business system, wherein the indicator data includes at least one; The relationships between the indicator data in the aforementioned business systems are analyzed to construct a data relationship model; In ETL, the indicator data in the data relationship model is transformed; Based on the filtering rules and data relationship model, data extraction is completed for each business system in sequence, and the extracted data is linearly connected to the extraction scheduling task to generate a linear task structure. Construct a business analysis and decision-making model for enterprise operating revenue, and decompose the top-level data requirements of the business analysis and decision-making model step by step downwards, sorting out the calculation hierarchy relationship between the data to be extracted in the related business system data; The data relationship model and the computational hierarchy are used together as the criteria and basis for ETL data extraction. The extraction configuration of the data to be extracted is carried out, and the extraction tasks are executed serially as linear tasks. Then, according to the business analysis and decision model and the computational hierarchy, multiple sets of linear task structures are integrated into the scheduling task, thereby combining a multi-level tree-like data extraction structure.

2. The method for extracting enterprise operating revenue data according to claim 1, characterized in that, The data indicators include: sales contracts, production plans, and the value of output entering and leaving the business unit.

3. The method for extracting enterprise operating revenue data according to claim 2, characterized in that, Sales contracts, production plans, and the production value of business units entering and leaving the warehouse are respectively linked to the Production Planning Management System (PMIS), the Marketing and Sales Management System (CMS), the Warehouse Management System, and the Financial and Price Management System.

4. The method for extracting enterprise operating revenue data according to claim 1, characterized in that, Also includes: The tree-like data extraction structure can be visualized using BI tools.

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