CN112800042B - Data processing method - Google Patents
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Abstract
The invention relates to the technical field of computer big data, and particularly provides a method for managing data, which is characterized by comprising the following steps of combing a data resource list and a data element standard, and determining a data management range and a management target; then, three stages of single-table data detection, multi-table data integration and application feedback treatment are used for implementing treatment; and finally, the treatment condition statistics, monitoring and displaying. Compared with the prior art, the invention can greatly improve the efficiency of data management work, is convenient for the data management work to be carried out orderly and assuredly, and has good popularization value.
Description
Technical Field
The invention relates to the technical field of computer big data, and particularly provides a data management method.
Background
The government affair data has the characteristics of large scale, multiple types and high value, the demand of cross-department business data is more and more obvious, and in addition, the government affair informationization is achieved for years, the government affair big data collects business data of all levels of government departments, the data types are numerous, the aspects of people life are concerned, and the potential value of the data is huge.
With the development of information technology, government departments attach more and more importance to the quality of government affair data and the value of the data. Purposeful planning is carried out for the collection and treatment of the data, and the data is managed in a planning way. However, currently, most government affairs data practitioners lack top-level design planning guidance when performing data maintenance and data cleaning, or planning is difficult to implement on the ground, so that the actual data governance implementation workload is large and cumbersome, and a standardized system is not formed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for managing data with strong practicability.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for managing data, at first, carding data resource list and data element standard, determining data management scope and management target; then, three stages of single-meter data detection, multi-meter data integration and application feedback treatment are used for implementing treatment; and finally, the treatment condition statistics, monitoring and displaying.
Further, dividing the data into a department preposition library, a central preposition area, an original area, a standard area, a problem area, a subject library area and a service application area;
the department front-end library provides data for each department to the department front-end library;
the central preposed area is a channel for external exchange;
the original area reserves the original form of data and is used for tracing the data problem;
the standard area stores standard data after cleaning and processing;
the problem area stores problem data and conflict data in the treatment process, and the problem data and the conflict data are pushed to the central preposed problem area at regular time and are exchanged to each department;
the theme library area stores basic theme library data and performs data recombination and integration according to the service scene of the basic theme library;
and the service application area extracts related data from the original area, the standard area or the basic subject area for storage.
Furthermore, the central preposed area is used for providing data for departments and exchanging the data to the central preposed area, and the central preposed area is communicated with each department preposed library and an externally exchanged channel;
the data in the original area is newly added each time, and updating and deleting operations are not carried out;
and the data in the problem area is newly added each time, and updating and deleting operations are not carried out.
Furthermore, the single table detection is carried out in two steps, namely, a primary integrity check is carried out in the process of extracting data from the central preposed library to the original library; and secondly, performing accuracy verification in the process of cleaning and converting the original library to the standard library.
Further, when the preliminary integrity check and the accuracy check are not passed in the single-table detection, the data are not passed and stored in a single-table data question bank of a question bank, and the single-table data question bank stores data questions according to department sub-banks.
Preferably, the single table detection is performed according to a universal rule.
Further, in the multi-table data integration, multi-table data conflict processing is performed when the standard library is integrated into the subject library or the service application library, and the check is not performed through data and is stored into the multi-table service conflict library of the question library, wherein the multi-table data conflict library is named by specific service application, and the name of a question table is the specific detection content _ DIFF.
Further, in multi-table data integration, problem data synchronization tasks are established, one task is arranged in each conflict table, conflict data are synchronized into the central preposed library in a timed increment mode, and problem data of the central preposed library are exchanged back to the department preposed library by the exchange system;
and the service department acquires the problem data and then confirms the data, and synchronizes the data to the front-end database of the department again after the error problem is modified, and resynchronizes the data which is not checked and passed in the batch if the data is confirmed to be free of problems.
Preferably, the application feedback processing is processed by combining a human and a tool.
Compared with the prior art, the method for treating data has the following outstanding beneficial effects:
according to the method for managing the data, the management steps are clearly and definitely divided into three stages according to the special characteristics of government affair data, and the management work can be systematically expanded. The three stages of single-meter detection, multi-meter integration and application problem feedback treatment are organically combined and relatively independent, can be split and independently implemented, and each treatment rule can be flexibly expanded, so that the flexibility of treatment work is increased. On the basis, data management and monitoring are carried out, controllability of management work can be guaranteed, and various data problems in the timely management work can be found conveniently and timely. Therefore, the method can greatly improve the efficiency of data management work, and is convenient for the data management work to be carried out orderly and assuredly.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the embodiments or technical solutions in the prior art are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of remediating data;
FIG. 2 is a schematic diagram of a data resource combing template in a method for managing data;
FIG. 3 is a schematic diagram of a disassembled target template in a method for treating data;
FIG. 4 is a diagram of a data resource center architecture for a method of administering data;
FIG. 5 is a data governance flow diagram of a method of governing data;
FIG. 6 is a schematic diagram of a single-surface abatement process in a method for abatement data;
FIG. 7 is a schematic view of a multi-table data integration process in a method for treating data;
FIG. 8 is a schematic diagram of a feedback process used in a method of remediating data.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments in order to better understand the technical solutions of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A preferred embodiment is given below:
as shown in FIG. 1, the invention comprises a data governance and target carding, a data governance framework and an implementation method, and data governance condition statistics and analysis. The data management range and the target carding are the early preparation of management work; the governing architecture and the implementation method are the process of governing work; the statistics and analysis of the treatment condition are the display of the data treatment result and the monitoring of the treatment process.
As shown in fig. 2 and 3, the government affair data relates to data of each business object such as a natural person, a legal person, a social object, a natural and mental object, and the like managed and served by each hierarchy and each business institution. The data management work faces the problems of large data volume and complexity, so that before implementation, firstly, the range of management needs to be determined clearly, data of which levels, which business organizations and which business objects need to be managed are cleared, and a specific resource list is combed. And then combing out specific treatment targets based on national standards, industry standards, regional standards, project accumulation standards and specific service requirements. The treatment goals are divided into achievement goals and dismantling goals. The ultimate goal of data governance is to perform social governance based on the governed data, a task that is a long-lasting task. The achievable treatment target is a specific business target in the final targets, such as improving the data quality of business departments, constructing a data resource center, establishing a population basic information resource library, and the like. Different business targets need to construct different data governance architectures. The disassembly target is a target that specific business data needs to achieve in the governance process, for example, a standard constraint that specific metadata needs to meet, a detection rule that field data needs to meet, and the like.
As shown in fig. 4, a data governance architecture diagram is developed based on the different objectives to be achieved. Different achievement goals can be selected to comprise different data areas, and the whole data resource center is composed of the following components: original area, standard area, problem area, basic/subject library area, service application area.
Description of each data partition:
department preposition library: each business division provides data to a division pre-repository.
Central pre-region: the department provides data, and the data is exchanged to the central preposed area, and the central preposed area is a channel for communicating the preposed libraries of all departments and exchanging the data to the outside.
Original area: the original area keeps the original form of the data, which is convenient for tracing the data problem. And the original database data is newly added each time, and updating and deleting operations are not performed.
Standard area: and the standard area stores standard data after cleaning and processing. And performing updating and inserting operation on the standard area data.
And the problem area stores problem data and conflict data in the treatment process, and pushes the problem data and the conflict data to the front problem area of the center at regular time to exchange the problem data and the conflict data to each department. And the problem library/conflict library data are newly added each time, and updating and deleting operations are not carried out.
Base/subject library area: and the basic/subject library area stores basic subject library data and performs data recombination and integration according to the service scene of the basic/subject library.
Service application area: the data table is distinguished according to service scenes, and relevant data are extracted from an original area, a standard area or a basic subject area to the area.
As shown in fig. 5, a specific data treatment process is designed, which can be specifically divided into a single-meter treatment, multi-meter integration, and application of a feedback three-level treatment system.
As shown in fig. 6, single-surface remediation:
and (4) performing single-table detection according to a universal rule, and finding and solving the problems of the original data. The single table detection is carried out in two steps, namely, primary integrity check is carried out in the process of extracting data from the central preposed library to the original library, and accuracy check is carried out in the process of cleaning and converting the original library to the standard library. And storing the verification data which do not pass the verification into a single-form data question bank of the question bank, and storing the data questions in the single-form data question bank according to the department sub-banks.
The specific contents of the single-surface treatment are as follows:
and (4) integrity checking: the key field is missing check, and the mandatory item is null check.
Checking the accuracy: the method comprises the following steps of field type checking, field length checking, field normalization checking and data value field checking.
Data standard conversion: data format conversion, field normalization conversion, data dictionary conversion and measurement unit conversion.
As shown in fig. 7, multi-table data integration:
the multi-table data conflict processing is carried out when the standard library is integrated into a basic/subject library or a service/application library. The data conflict detection needs to be associated with different business tables of different departments, check that data is not passed and store the data in a multi-table business conflict library of a question library, wherein the multi-table business conflict library is named by specific business application, such as a population conflict library CTK _ RK. The problem table name is specific detection content _ DIFF, such as a DEAD _ TIME _ DIFF table. And creating problem data synchronization tasks, wherein one task is in each conflict table, and synchronizing conflict data to the central preposed library in a timed increment mode. And configuring different tasks according to different conflict types. If the business form conflicts with the primary data, the conflict data is only returned to the business form department. If multi-source data conflict, conflict table data need to be pushed to the central preposed base of each conflict department at the same time. The problem data of the central preposed warehouse is exchanged back to the department preposed warehouse by the exchange system. The business department acquires the problem data and then confirms the problem data, and the error problem is corrected and then is synchronized to the front-end warehouse of the department again; if the data is confirmed to be not problematic, the non-verified data of the upper batch still needs to be synchronized again.
The specific contents of the multi-surface treatment are as follows:
and (3) consistency detection: and (4) verifying the main data with mismatched multi-source data.
Data integration: merging entity data, merging detail data, desensitizing data, encrypting data storage and completing service fields.
As shown in fig. 8, the problem feedback process is applied:
in the data application stage, a data using unit finds data problems, the problems can be fed back to a data management platform through an application platform, and a data management unit obtains the problems through the data management platform and confirms the data problems through manual means.
The general data problem falls into two categories: one is the original data problem and the other is the data governance problem. The problem type of the original data problem needs to be analyzed, the similar problem data in an original library, a standard library or an application library is detected, the problem is stored in a problem library according to a single-table data problem or a multi-table conflict problem, and the problem is synchronized to a business department. The problems need to perfect the data management rules at the same time, so as to prevent the similar problems from continuing to appear. The data management problem is generally that the data processing flow or the data management rule is incorrect, the analysis problem modifies the data processing rule, the analysis influences the data, and the data is corrected. The application data problem processing is a data analysis process, and is mostly processed in a mode of combining manpower and tools. The method is also a process for continuously improving the data governance process.
The overall data condition needs to be monitored in the data management process, and data monitoring needs to be counted from the aspects of data updating timeliness, accuracy and the like. The dimensions of the latest update time, the latest update data quantity, the detection problem quantity, the rule problem quantity, the total data quantity and the like of the data in each area table are required to be counted. The specific implementation can use a regularly executed background task to carry out cyclic statistics on the data resources in the task list.
The above embodiments are only specific examples of the present invention, and the scope of the present invention includes but is not limited to the above embodiments, and any appropriate changes or substitutions according to the method claims of the present invention and the data of the present invention by those skilled in the art should fall within the scope of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (1)
1. A method for managing data is characterized in that firstly, a data resource list and a data element standard are combed, and a data management range and a management target are determined; then, three stages of single-meter data detection, multi-meter data integration and application feedback treatment are used for implementing treatment; finally, the treatment condition statistics, monitoring and display;
dividing data into a department preposed library, a central preposed area, an original area, a standard area, a problem area, a subject library area and a service application area;
the department front-end library provides data for each department to the department front-end library;
the central preposed area is a channel for external exchange;
the original area reserves the original form of data and is used for tracing the data problem;
the standard area stores standard data after cleaning and processing;
the problem area stores problem data and conflict data in the treatment process, and the problem data and the conflict data are pushed to the central preposed problem area at regular time and are exchanged to each department;
the theme library area stores basic theme library data and recombines and integrates the data according to the service scene of the basic theme library;
the service application area extracts and stores related data from an original area, a standard area or a basic subject area;
the central preposed area is used for providing data for departments and exchanging the data to the central preposed area, and the central preposed area is communicated with each department preposed library and is used for an externally exchanged channel;
the data in the original area is newly added each time, and updating and deleting operations are not carried out;
the data of the problem area is newly added each time, and updating and deleting operations are not carried out;
the single table detection is carried out in two steps, namely, the preliminary integrity check is carried out in the process of extracting data from the central preposed library to the original library; secondly, performing accuracy verification in the process of cleaning and converting the original library to the standard library;
when the preliminary integrity check and the accuracy check are not passed in the single-table detection, storing the data which are not passed into a single-table data question bank of a question bank, wherein the single-table data question bank stores data problems according to department sub-banks;
the single table detection is carried out according to a universal rule;
in the multi-table data integration, multi-table data conflict processing is carried out when a standard library is integrated into a subject library or a service application library, and the multi-table data conflict processing is verified to be stored in a multi-table service conflict library of a question library without passing data, wherein the multi-table data conflict library is named by specific service application, and the name of a question table is specific detection content _ DIFF;
in multi-table data integration, problem data synchronization tasks are established, one task is arranged in each conflict table, conflict data are synchronized to a central preposed library in a timed increment mode, and problem data of the central preposed library are exchanged back to a department preposed library by an exchange system;
the service department acquires the problem data and then confirms the problem data, and synchronizes the data to the front-end database of the department again after the error problem is modified, if the data is confirmed to be free of problems, the data which are not checked and passed in the batch are resynchronized;
the application feedback processing is mostly processed in a mode of combining manpower and tools.
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