CN116151632A

CN116151632A - Data architecture method

Info

Publication number: CN116151632A
Application number: CN202310098868.9A
Authority: CN
Inventors: 邬凯强; 钱冬蕾; 吴佚超; 朱馨怡; 舒思佩; 许松超
Original assignee: Shanghai Shuhe Information Technology Co Ltd
Current assignee: Shanghai Shuhe Information Technology Co Ltd
Priority date: 2023-02-08
Filing date: 2023-02-08
Publication date: 2023-05-23

Abstract

The invention discloses a data architecture method, which belongs to the technical field of data architecture and comprises the following steps: s1: and (3) data acquisition: s2: data use: s3: and (3) formulating a data standard: s4: building a business data model: s5: and constructing a data distribution. The data architecture method of the invention carries out quality assessment on the collected data, so that a large amount of data collection is carried out on the production line, the data collection speed is ensured, the integrity of the original project of the data collection is improved, the data collection efficiency is improved so as to meet any specific standardized requirement, the data can help judge business problems and optimize the business, the final result is that the product is guaranteed to be expected as expected, the defect can be improved, and the invention can play a better role in the operation and the fine management of enterprises through data analysis.

Description

Data architecture method

Technical Field

The present invention relates to the field of data architecture technologies, and in particular, to a data architecture method.

Background

At present, in the activities of enterprise management, operation, strategic planning and business expansion, various roles in enterprises need to have comprehensive, multi-angle and multi-level knowledge and use for the existing processes, fields, systems, personnel division and the like of the enterprises. In daily work of enterprises, the important information is not well managed by the enterprises, so that the operation efficiency of the enterprises cannot reach an ideal level.

Disclosure of Invention

The invention aims to provide a data architecture method, which ensures the speed of data acquisition, improves the integrity of original data acquisition projects, improves the data acquisition efficiency so as to meet any specific standardized requirement, can help judge business problems and optimize business, and finally ensures that products are expected to reach expectations, can improve aiming at defects, can play a better role in the operation and fine management of enterprises through data analysis, and solves the problems in the background technology.

In order to achieve the above purpose, the present invention provides the following technical solutions:

a data structuring method comprising the steps of:

s1: and (3) data acquisition: acquiring big data information required to be related to the management and operation of an enterprise through multiple channels, combining the information and the information according to the management, operation, strategic planning and business expansion of the enterprise, and integrating the information and the information to be used as content expansion storage;

s2: data use: hierarchical management is carried out according to enterprise management, a knowledge base and a data model are established according to information reserved in actual business, and collected data are reorganized and used;

s3: and (3) formulating a data standard: the unified data standard is divided into two parts from a classification angle, wherein the two parts comprise a basic standard and a special standard, and the special standard establishes standards for specific service problems and specific service users according to a general method of standardized work;

s4: building a business data model: constructing a business data model by combining a data standard, and realizing description of data and relations through E-R modeling;

s5: constructing data distribution: a panoramic view of the data flowing on the business process and the IT system is constructed, and the coming and going pulses of the data are identified, so that the company-level data distribution is constructed.

Further, for S1, the collected data is used as input according to the service domain directory, and the data asset directory is constructed by combining the characteristics of the data architecture, and the general partitioning rule includes a topic domain group, a topic domain, a service object, a logical entity object and an attribute.

Further, for S2, the step of data use includes the steps of:

s201: a unified data standard is established in the enterprise range, unified main management information of the enterprise is provided according to a data warehouse and a management information system, and a universal data standard established on the basis of the unified data standard is suitable for the whole enterprise to be used as a reference standard for data exchange and integration;

s202: establishing a data exchange platform which accords with a unified data standard, establishing the unified data exchange platform, realizing data integration among different databases through the exchange platform, realizing data-level integration of the original business systems, and ensuring data exchange and sharing among the different databases;

s203: enterprise data warehouse meeting unified data standard is established, data can be stored in different types of databases, the data warehouse is to store heterogeneous data sources in a unified model organization at a single site so as to support management decision, and the data warehouse technology comprises data cleaning, data integration, online analysis processing and data mining.

Further, for S3, the standard structured framework may be expressed as a framework structure part, an information service part, a data management part, a data model part, and a dedicated standard part;

frame structure part: determining how many standards the frame structure standard is divided into and describing how the standards are combined together, and including reference models and consistency and testing;

information service part: defining an expression method of coding rules and information, wherein the coding rules and the information comprise terms, codes and services;

a data management section: describing the basic principle of the quality of the data set and the quality evaluation process, describing the data, metadata and element catalogues, and comprising a quality basic element, an evaluation program, metadata and an element cataloging method;

data model part: carrying out data modeling according to the definition elements and the characteristics thereof, wherein the data modeling only comprises a time mode and an application mode rule, and further expands along with the deep standardization process to increase a flight mode, a passenger mode and the like;

special standard part: the construction technology of the special standard is defined, the requirements of specific application, specific functions and specific users are met, and the problem of unified data standard is solved.

Further, for S3, the unified data standard includes the following steps:

s301: determining the application range of the data, establishing related terms for the data, implementing a term base maintenance system, and determining a method for carrying out consistency test on the data;

s302: uniformly modeling the data, determining the time mode of the data, establishing the application mode of the data, performing element cataloging on the data for data exchange and data sharing, implementing an element cataloging maintenance system, establishing a related code table on the data, and implementing a code table maintenance system;

s303: determining basic elements and programs for evaluating the quality of data, and determining metadata to be submitted simultaneously when the data is submitted so as to carry out refinement management on the acquisition and the submission of the data;

s304: and providing standardized data service for each user of the data, selecting another item of data to be standardized, and repeating the process.

Further, for S4, before modeling, defining a standardized framework of the enterprise data field through a reference model, determining a basic principle to be followed by the standardized work, wherein the reference model comprises a conceptual modeling, a domain reference model, an architecture reference model and a special standard;

the conceptual modeling is used for defining conversion and exchange services of various data of enterprises and ensuring integration and mutual coordination among various standards;

the domain reference model generally describes the data structure and the data content, and mainly aims at the structure and the data management of various data of enterprises;

architecture reference model describes the general type of data services handled by a computer system, enumerates service interfaces, and standardizes these data and interfaces for data exchange;

specific criteria different criteria of the above series of criteria are combined together and the rules in these criteria are materialized to meet the specific application requirements.

Further, for S4, building a service data model includes the following steps:

s401: in the process of developing the service and constructing the service model, the generated requirements form the service requirements, and the service requirements are not usually brought to users, so that a complete closed loop process can be formed;

s402: recording the performance of the business and the user of the product, and storing the data, wherein the data can be divided into three types when the data are used, including result data, behavior data and process data;

s403: an expectation is established for designing the service, various types of data are generated aiming at the service, the data are utilized, the service can be optimized, the direction of a product is explored, the development and iteration of the service are accompanied, the formulation of target data is also adversely affected, new data dimensions are expanded, and the data are analyzed according to the data, so that a service data model is constructed.

Further, for S403, when the business data model is to be built, the business model is to be built with data as a guide, the first step is to set data expectations, where the data expectations include data of the data expectations and created data expectations, and when the data expectations are set, the data needs to be disassembled into life cycle stages of the product, each life cycle of the product corresponds to different data expectations, the same data, and in different life cycle stages of the product, expected values are not necessarily the same, and long-range targets are specified according to the overall data expectations;

after setting the data expectation, a product manager needs to disassemble the data expectation into the data capable of falling to the ground, the disassembled data capable of falling to the ground is required to correspond to key nodes of the service, the key node data is found, the data dimension which can influence the expected data is found according to the existing service and the service data, the data dimension is subdivided, and the disassembled data value is set by combining the specific value of the expected data;

after the needed data is finalized, the link of actually using the data is reached, the first step of the data is to verify the data, the verification data is also divided into two types, namely data tracking and data copying, the data is to be stared at any time in the running process of the service, the performance of the data is to be concerned at any time, meanwhile, the periodic whole data copying is also needed, when the data is abnormal or has a difference value with the expected data, the data is analyzed at the first time, the reason is searched, the coping strategy is searched, and if the result data is abnormal, the process data is started;

the core is to reconstruct the service through the data iteration service model, the existing service model is improved through the data, the iteration direction of the service is searched through the data, the service model is optimized, the behavior data and the process data which can promote the expected data are mainly found, then the behavior data and the process data are changed through adjusting and optimizing the service, and finally the purpose of promoting the expected data is achieved.

Further, for S5, constructing the data distribution includes the steps of:

s501: the basic data platform construction work comprises basic data platform construction, data standardization, data warehouse establishment, data quality, unified service caliber and the like, wherein data are scattered on servers of products of each department and data of each service system, unified data standardization is trained, service system data are reported according to the unified caliber, standardized SDK and reporting protocols are carried out, and a layered module diagram is constructed according to enterprise defects;

s502: on the basis of establishing a data platform and visualization, carrying out various analyses on the existing sales user behaviors, income data and the like, outputting daily reports, weekly reports, monthly reports and various thematic analysis reports, taking the Internet as an example, and forming a data analysis graph through common data analysis software;

s503: the machine is used for monitoring service operation, the monitored information is constructed in a graphic mode, and on the basis of the trend of graphic presentation, people can perform more adequacy experience analysis and strategic judgment of staff.

Further, for S5, the self-service line is more biased to the production enterprise, and needs to make financial analysis, and relates to analysis of income, various costs and profits of each self-service product, and there is also a financial expertise for amortization of fixed asset depreciation management cost, and the drawn service flow chart is used for sorting out internal and external clients needing service, and data appeal of the clients in each link, and drawing a table, a carded service flow, each client appeal, combining company strategic guidance and existing resources of a data department, drawing a strategic execution map, presetting data service content to be built, and taking user operation as an example, the service objective is mainly to know the overall situation of the user, promote the total amount of the user, promote retention, promote daily activity, promote payment and the like, and further refining to data service which can be provided and simply carding data source.

Compared with the prior art, the invention has the beneficial effects that:

1. according to the data architecture method provided by the invention, on the basis of referring to the formed data acquisition convention, the data are reclassified, the data acquisition responsibilities of each department are clarified, and the quality evaluation is carried out on the acquired data, so that a large amount of data acquisition is carried out on the production line, the data acquisition speed is ensured, the integrity of the original data acquisition project is improved, and the data acquisition efficiency is improved.

2. According to the data architecture method provided by the invention, each comprehensive standardization work needs to establish a structured standard framework so as to ensure the quality of the standard, ensure the comprehensive, consistent and wide application of the standard, and each part of the structured standard acts on the same specific application requirement at the same time so as to meet any specific standardization requirement.

3. The data architecture method provided by the invention mainly finds out the behavior data and the process data which can promote the expected data, then changes the behavior data and the process data by adjusting and optimizing the service, finally achieves the purpose of promoting the expected data, namely, the method for constructing the service model by taking the data as the guide meets the requirements of complex and changeable users, and when facing the business market of competitive excitation, the data can help to judge the service problem and optimize the service, and the final result also ensures that the product is expected to reach expectations.

4. The data architecture method provided by the invention combines the business process of carding, each customer appeal, the company strategic guidance and the existing resources of the data department, draws a strategic execution map, presets the data service content to be built, takes the user operation as an example, and the business targets are mainly to know the overall situation of the user, promote the total quantity of the user, promote the retention, promote the daily activity, promote the payment and the like, further refine to the data service which can be provided and simply comb the data source, analyze the data graph of the component, intuitively know the defect of the enterprise operation, improve the defect, and play a better role in the operation and the refined management of the enterprise through the data analysis.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application and to provide a further understanding of the application with regard to the other features, objects and advantages of the application. The drawings of the illustrative embodiments of the present application and their descriptions are for the purpose of illustrating the present application and are not to be construed as unduly limiting the present application. In the drawings:

FIG. 1 is a block diagram of a data structure according to the present invention;

FIG. 2 is a flow chart of the present invention;

FIG. 3 is a block diagram of a data usage module of the present invention;

FIG. 4 is a block diagram of the data standard of the present invention;

FIG. 5 is a block diagram of a business data module according to the present invention;

FIG. 6 is a block diagram of a constructed data distribution according to the present invention.

Detailed Description

In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the present application described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In the present application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate an azimuth or a positional relationship based on that shown in the drawings. These terms are used primarily to better describe the present application and its embodiments and are not intended to limit the indicated device, element or component to a particular orientation or to be constructed and operated in a particular orientation.

Also, some of the terms described above may be used to indicate other meanings in addition to orientation or positional relationships, for example, the term "upper" may also be used to indicate some sort of attachment or connection in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.

In addition, the term "plurality" shall mean two as well as more than two.

It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Referring to fig. 1-2, a data architecture method includes the following steps:

The collected data is used as input according to the service domain catalogue, the data asset catalogue is built by combining the characteristics of the data architecture, a general partitioning rule comprises theme domain grouping, theme domain, service objects, logic entity objects and attributes, the data is reclassified on the basis of the formed data collection convention according to the actual process of the service flow, the data collection responsibilities of all departments are clarified, quality evaluation is carried out on the collected data, a large amount of data collection is carried out on a production line, the data collection speed is ensured, the integrity of data collection original projects is improved, and the data collection efficiency is improved.

Referring to fig. 3, for S2, the steps of data usage include the following steps:

For S3, the standard structured framework may be expressed as a framework structure part, an information service part, a data management part, a data model part, and a dedicated standard part;

Each comprehensive standardization work needs to establish a structured standard framework to ensure the quality of the standard itself and ensure the comprehensive, consistent and wide application of the standard, and each part of the structured standard acts on the same specific application requirement at the same time so as to meet any specific standardization requirement.

Referring to fig. 4, for S3, the unified data standard includes the following steps:

For S4, before modeling, defining a standardized framework in the enterprise data field through a reference model, determining a basic principle to be followed by the standardized work, wherein the reference model comprises a conceptual modeling, a domain reference model, an architecture reference model and a special standard;

Referring to fig. 5, for S4, the business data modeling includes the following steps:

For S403, when the business data model is to be built, the business model is to be built with data as guidance, the first step is to set data expectation, where the data expectation includes data of the data expectation and created data expectation, when the data expectation is set, the data needs to be disassembled into life cycle stages of the product, each life cycle of the product corresponds to different data expectation, the same data, in different life cycle stages of the product, expectation values are not necessarily the same, and long-term targets are specified according to the overall data expectation;

the method is characterized in that the service is reconstructed by data iteration service models, the existing service models are improved by the data, the service models are optimized by data to find the iteration directions of the service, the behavior data and the process data which can promote expected data are mainly found, then the behavior data and the process data are changed by adjusting and optimizing the service, finally the purpose of promoting the expected data is achieved, namely the data is used as a guide to construct the service models, the complex and changeable user demands are met, the data can help to judge service problems when the business market of competitive excitation is faced, the service is optimized, and the final result is that products are expected to be guaranteed.

Referring to fig. 6, for S5, constructing a data distribution includes the steps of:

Aiming at S5, the self-service line is more biased to a production enterprise, financial analysis is needed, the analysis of income, various expenses and profits of each self-service product is related, financial expertise of amortization of fixed asset depreciation management cost is also needed, a drawn service flow chart is used for sorting out internal and external clients needing service and data appeal of the clients in each link, a table is drawn, the carded service flow and the client appeal are combined with company strategic guidance and existing resources of a data department, a strategic execution map is drawn, data service content to be built is preset, and the service objective is mainly to know the overall situation of a user, improve the total amount of the user, improve the retention, improve daily activity, improve payment and the like, further refine to the data service which can be provided and simply comb the data source, analyze the data graph of a component, intuitively know the disadvantage of enterprise operation, improve the disadvantage, and play a better role in the operation and fine management of the enterprise through the data analysis.

In summary, the data architecture method proposed in the present invention reclassifies data based on the formed data acquisition routine, and determines the data acquisition responsibility of each department, and performs quality evaluation on the acquired data, so that a large amount of data acquisition is performed at the production line, the speed of data acquisition is guaranteed, the integrity of the original project of data acquisition is improved, the data acquisition efficiency is improved, each comprehensive standardization work needs to establish a structured standard framework, so as to ensure the quality of the standard itself, ensure the comprehensive, consistent and wide application of the standard, each part of the structured standard acts on the same specific application requirement at the same time, so as to meet any specific standardized requirement, mainly find the behavior data and the process data capable of improving the expected data, then changes the behavior data and the process data by adjusting and optimizing the service, finally, the purpose of improving the expected data is achieved, that the method is to construct a service model by taking the data as guidance, the requirement of complex and variable users, when facing the business stimulus, the data may help judge the service problem, and optimize the service, finally guarantee the quality of the product, set up to the customer, and map data can be further advanced by the map element, the map element is further provided for the current situation of the customer, the customer can be made to have a better understanding, the current situation, the customer can be better, and the customer is required to be made, and the user's data is better, and the user is required to be better, and the map element is required to be better, and the user's resources can be better, and the user's required to be better, and the user's resources and the user is better, and the user data is better, the method can intuitively know the defects of enterprise operation, can improve the defects, and can play a good role in enterprise operation and fine management through data analysis.

The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method of data structuring, characterized by: the method comprises the following steps:

2. A method of data structuring as defined in claim 1, wherein: for S1, the collected data is used as input according to the service domain catalogue, the data asset catalogue is built by combining the characteristics of the data architecture, and the dividing rule comprises a theme domain group, a theme domain, a service object, a logic entity object and an attribute.

3. A method of data structuring as defined in claim 2, wherein: for S2, the step of data use includes the steps of:

4. A data architecture method according to claim 3, wherein: for S3, the standard structured framework may be expressed as a framework structure part, an information service part, a data management part, a data model part, and a dedicated standard part;

data model part: carrying out data modeling according to the definition elements and the characteristics thereof, wherein the data modeling only comprises a time mode and an application mode rule, and further expanding the data modeling along with the deep standardization process to increase a flight mode and a passenger mode;

5. A method of data structuring according to claim 4, wherein: for S3, the unified data standard includes the following steps:

6. A method of data structuring according to claim 5, wherein: for S4, before modeling, defining a standardized framework in the enterprise data field through a reference model, and determining a basic principle to be followed by standardized work, wherein the reference model comprises a conceptual modeling, a domain reference model, an architecture reference model and a special standard;

7. A method of data structuring according to claim 6, wherein: for S4, establishing a business data model comprises the following steps:

8. A method of data structuring as defined in claim 7, wherein: for S403, the first step of constructing the business data model is to set data expectations, wherein the data expectations comprise data of the data expectations and created data expectations, the data are disassembled into life cycle stages of the product when the data expectations are set, each life cycle of the product corresponds to different data expectations, and long-range targets are designated according to the whole data expectations in different life cycle stages of the product;

after the needed data is finalized, the link of actually using the data is reached, the first step of the data is to verify the data, the verification data is divided into two types, namely data tracking and data copying, the data is to be stared at any time in the running process of the service, the performance of the data is to be concerned at any time, meanwhile, the periodic integral data copying is also needed, when the data is abnormal or has a difference value with the expected data, the data is analyzed at the first time, the reason is searched, the coping strategy is searched, and if the result data is abnormal, the process data is started;

the core is to reconstruct the service through the data iteration service model, the existing service model is improved through the data, the iteration direction of the service is searched through the data, the service model is optimized, the behavior data and the process data which can promote the expected data are found, then the behavior data and the process data are changed through adjusting and optimizing the service, and finally the purpose of promoting the expected data is achieved.

9. A method of data structuring as defined in claim 8, wherein: for S4, building a data distribution includes the steps of:

10. A method of data structuring as defined in claim 9, wherein: aiming at the concrete operation of S4, the drawn business flow chart is used for sorting out the internal and external clients needing service and the data appeal in each link, drawing a table form, drawing a strategy execution map by combining the combed business flow and each client appeal with the company strategy guidance and the existing resources of the data department, and presetting the data service content to be built.