CN112734226A - Data asset management method for power customer service business - Google Patents
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Abstract
The invention discloses a data asset management method of power customer service business, which comprises the following steps: (1) a data system is constructed by adopting a mode of combining service drive and data drive; (2) data quality management and data governance; (3) performing data asset transformation; (4) performing data asset management based on the data inventory and the data aggregation; (5) and (4) data is shared in an open mode. The invention can effectively open up data circulation channels between the interiors of companies, solve the core problem of company management informatization on a data level, form a transversely integrated and longitudinally communicated efficient and ordered information flow, play the basic supporting role of data information, meet the requirements of enterprises on information and data, and help the enterprises to solve the problems of data integration and sharing, data asset inventory and information island avoidance effectively.
Description
Technical Field
The invention relates to a data management method, in particular to a data asset management method of a power customer service business.
Background
In recent years, national grid companies gradually develop data resource integration management, and develop data resource integration management work through systematic data resource inventory according to the construction principles of unified data, unified standards, unified platforms and unified services. According to the planning design of the previous data middling station, the data resource inventory, the data asset aggregation and the normal data management are gradually developed, and the data opening work is constructed.
Current data asset management schemas are organized in a data warehouse hierarchy based on dimensional modeling and in a resource directory hierarchy. Meanwhile, a data asset management tool is constructed, data circulation channels between the interiors of companies are effectively opened, the core problem of company management informatization on a data layer is solved, a transversely integrated and longitudinally communicated efficient and ordered information flow is formed, the basic supporting function of data information is exerted, the requirements of enterprises on information and data are met, and the problems that the enterprises integrate and share data, keep track of data assets, effectively avoid information islands and the like are solved.
A data asset management method based on power customer service is based on the planning design of mass data of the power customer service and a data center platform architecture, a data system is constructed and perfected, the data quality is improved, the data resource capitalization is carried out, an asset management and open platform is constructed, the value of the existing data assets is fully utilized, and finally a set of data asset management system of data system-quality management-data capitalization-asset management-open sharing is formed.
At present, the problems of uneven data quality, system islanding, blocked data circulation, lack of effective management mechanism and potential safety hazard of data exist in the current situation analysis of all service systems of a national network customer service center and customer service data storage and distribution.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention aims to provide a safe, efficient and orderly data asset management method for power customer service business.
The technical scheme is as follows: a data asset management method of power customer service business comprises the following steps:
(1) a data system is constructed by adopting a mode of combining service drive and data drive;
(2) data quality management and data governance;
(3) performing data asset transformation;
(4) performing data asset management based on the data inventory and the data aggregation;
the data checking adopts a mode of combining a data system off-line construction method and an automatic checking method to check data resources, and comprises the following steps:
(4.1) ODS layer paste source construction;
(4.2) carrying out multi-table normalization processing on the detail layer;
and (4.3) automatically acquiring metadata of libraries, tables, fields and the like.
(4.4) setting a business rule, and carrying out data classification on the data, wherein the data classification comprises business classification, theme classification and region classification;
(4.5) identifying the sensitive field through a data resource management tool, and carrying out sensitive field marking and desensitization decryption treatment on the autonomously configured sensitive rule;
the data collection adopts a data extraction method, in the process of constructing a digital warehouse system, metadata management rules are precipitated, and finally a data resource catalog and a data map are constructed; constructing a data resource tree-shaped resource catalog based on the result of data resource inventory; taking data classification as a tree structure of a resource directory, carrying out all-around recombination on data resources, and displaying data traceability, reference data, main data and metadata; finally, data query and retrieval are constructed, resource retrieval is carried out on the names of the data resources on the basis of the data directory, and data positioning is carried out on the basis of metadata indexes;
(5) and (4) data is shared in an open mode.
Further, the electric power customer service business comprises 4 business fields of customer service and customer relation, electric charge management, electric energy metering and information acquisition and market and demand sides.
Further, the service information is divided into seven subject domains according to the service characteristics of the information:
a client domain: the system comprises clients, client public and client label information, and provides core data for developing specialized and lean management and supporting big data analysis for the center;
service domain: the system comprises service trace information, customer service information and value-added service information, is sourced from a 95598 system and an online national network, is used for customer service, accurate service and big data analysis service, and provides production service data; meanwhile, the method focuses on providing service, service interaction and other service management related process information for terminal users and channel data;
an operation domain: the system comprises service operation, telephone traffic operation, website operation, online national network operation and data operation, is derived from a 95598 service support system, an online national network, a tag library and an operation regulation and control system, and is used for carrying out specialized and lean management and supporting big data analysis in the center and providing service and data management data;
resource domain: the system comprises human resources, power grid resources, service resources, geographic resources, information resources and resource information for providing center development business;
analysis domain: the system comprises big data analysis results, inspection monitoring analysis results, business operation analysis results and service quality analysis results, is derived from big data analysis, reports, service appraisal and satisfaction investigation analysis, and is used for headquarter decision support, central operation promotion, provincial company service promotion, partner value increment and providing analysis data;
a basic supporting domain: the system comprises information technology management, operation and maintenance management, safe operation management and comprehensive management, and is used for carrying out specialized and lean management and supporting big data analysis in the center and providing supporting data;
external data field: external data outside the company and needed by the center is used for expanding the category of the center metadata and providing collaborative data.
Further, in the step (2), the data management is data asset management, model management and standard management are combined in the data exploration process, data quality quantitative evaluation is performed after automatic etl, data resources with unqualified quality are subjected to iterative tracing and overloading, and finally a closed-loop flow of the data management is formed.
Further, the step (3) comprises the value extraction of the data resources, the quantification of the data resources and the productization of the data assets.
Further, the realization mode of the productization of the data assets comprises the following steps: visualization display of a data asset service directory, customization of data products, various forms of result release and data asset open service provision.
Further, the step (5) comprises the steps of improving policies and management methods, opening a data portal, reusing data, and protecting data security and privacy.
Compared with the prior art, the invention has the following remarkable advantages: the data circulation channel between the insides of the companies can be effectively opened, the core problem of company management informatization on a data layer is solved, high-efficiency and ordered information flow which is transversely integrated and longitudinally communicated is formed, the basic supporting effect of data information is exerted, the requirements of enterprises on information and data are met, and the problems of data integration and sharing, data assets surviving and information isolated island avoidance of the enterprises are solved.
Drawings
FIG. 1 is a business data hierarchy;
FIG. 2 is a business division associated with the customer service domain;
FIG. 3 is a power customer service data architecture;
FIG. 4 is a data quality management mechanism;
FIG. 5 is an automated flow of data inventorying;
fig. 6 is a data aggregation process.
Detailed Description
The technical solution of the present invention will be further described in detail with reference to the following examples and accompanying drawings.
A data asset management method of power customer service business comprises the following steps:
(1) data architecture construction
The construction of a data system is the first step of data asset management, and the data system determines the planning and blueprint of data and influences the classification partition planning of data storage on the actual ground.
In the process of planning an actual data system, the invention adopts a method of combining business drive and data drive, and combs the evolution process from the electric power customer service business to the data based on the related marketing data in the electric power industry. And combing the business process, the business application entity and the information generated by the business entity, and classifying the business information according to the information characteristics and the business application characteristics to form a business data system. The idea of constructing a data system is shown in FIG. 1.
Planning and designing are carried out from the business perspective, and the power customer service business is characterized in that various services are provided for customers through the division and cooperation of specific businesses in various fields, various business processes are completed, and support is provided for the management, operation and decision of power supply enterprises; meanwhile, the sharing degree of the information resources of the whole power grid enterprise is improved through the ordered cooperation of the customer service and other services.
The services related to the customer service field are divided into 4 service fields such as "customer service and customer relationship", "electric charge management", "electric energy metering and information acquisition" and "market and demand side" and "comprehensive management", and there are 22 service classes, 184 service items and 1114 service sub-items, as shown in fig. 2.
The business information view taking the client as the center is constructed according to business architecture and business flow and the business logic thought of supporting client service, penetrating through the service process, enriching analysis dimension and promoting quality improvement and efficiency improvement.
A client domain: the system comprises clients, client public and client label information, and provides core data for developing specialized and lean management and supporting big data analysis for the center.
Service domain: the system comprises client service related process information such as service trace information, client service information, value-added service information and the like, mainly comes from a 95598 system and an online national network, is mainly used for services such as client service, accurate service, big data analysis and the like, and provides production service data. Meanwhile, the method focuses on providing service, service interaction and other service management related process information and channel data for terminal users.
An operation domain: the system comprises service management related process information such as service operation, telephone traffic operation, website operation, online national network operation, data operation and the like, mainly comes from a 95598 service support system, an online national network, a tag library, an operation regulation and control system and the like, and is used for carrying out specialized and lean management and supporting big data analysis in a center and providing service and data management data.
Resource domain: the system comprises human resources, power grid resources, service resources, geographic resources, information resources and resource information for providing the center to develop business.
Analysis domain: the system comprises result data generated by analyzing and mining data such as big data analysis results, inspection monitoring analysis results, business operation analysis results, service quality analysis results and the like, mainly comes from big data analysis, reports, service appraisal and satisfaction investigation analysis, and is used for headquarter decision support, central operation promotion, provincial company service promotion, partner value increment and providing analysis data.
A basic supporting domain: the system comprises basic support information such as information technology management, operation and maintenance management, safe operation management, comprehensive management and the like, and is used for carrying out specialized and lean management and supporting big data analysis in the center and providing supporting data.
External data field: outside the company, external data required by the center. The method expands the category of the central metadata, improves the analysis accuracy, the universality and the credibility of the big data, and provides the collaborative data.
And finally, obtaining a power customer service data system as shown in fig. 3.
(2) Data quality management and data governance
On the basis of a good and perfect data system, data quality management and data value evaluation need to be carried out on original data resources. In the process of quality detection, if the data quality problem is found, data management needs to be carried out on the data.
In the data asset management method provided by the invention, a set of complete and sound data quality and treatment system is provided. Covering a closed-loop treatment working mechanism of treatment demand proposing, task initiating, problem rectifying and reforming, feedback evaluation and supervision reporting. The specific data quality management mechanism is shown in fig. 4.
(3) Data capitalization
The process of transitive transformation of data resources to data assets is an important step of data value promotion, in an enterprise, not all data can constitute data assets, and the data assets are data resources capable of generating values for the enterprise. A data asset management professional forum and organization-International data management Association provide a data asset management related theoretical guidance system DAMA-DMBOK, which provides value-oriented data applications.
The invention is based on the electric power customer service business, and carries out the value extraction process on the basis of data resource checking. Therefore, the data resources are assets-oriented around value guidance, and the value of the data is fully released to help the electric power customer service field to improve service and self value.
The data assets are mainly characterized in that:
value extraction of data resources: processing the resources based on the data resources with certain quality, constructing a complete data system, carrying out comprehensive data hierarchy classification on the data resources, and finally realizing the assets of the data.
Quantification of data resources: ownership is one of the important attributes of an asset, so the necessary process to implement data assets is to build the right control of the data asset.
Commercialization of data assets: the necessary process for converting data resources into data assets is to form productions. The way of realizing the productization of the data assets mainly comprises the following steps:
1) visual display of the data asset service directory;
2) customizing data products and publishing results in various forms;
3) data asset open service provisioning.
(4) Data asset management
Data asset management mainly develops around two cores of data inventory and data collection, wherein the mainstream method of data inventory comprises the following steps: the method comprises a manual checking method, a data marking method, a time stamp coding method, a data system off-line construction method, an automatic checking method and the like. The data collection mainly comprises a data extraction method, a data dictionary method, a data index method, a metadata management method and the like.
According to the exploration, the marketing data resources in the power industry are suitable for checking the data resources in a mode of combining a data system offline construction method and an automatic checking method in the aspect of data checking. The core of the method is that equivalent backup of the existing data resources is realized in the bottommost data warehouse, and automatic checking of the data is realized in the process of constructing a data middle platform.
The method mainly comprises the following steps:
1) constructing an ODS layer paste source;
2) performing detail layer multi-table normalization processing;
3) automatic acquisition of metadata for libraries, tables, fields, etc.
4) Setting a business rule and carrying out data classification on the data. Including mainly by business classification, topic classification, and region classification.
5) Setting a business rule, and automatically identifying sensitive fields: in the process of managing and controlling the data resources through the data resource management tool, the identification of sensitive fields required by original data and sensitive field marking and desensitization decryption processing are carried out on the autonomously configured sensitive rules.
The inventory flow is shown in fig. 5.
In the aspect of data collection, a data extraction method is combined, in the process of constructing a digital storage system, a metadata management rule is precipitated, and finally a data resource catalog and a data map are constructed. And (4) data recombination display, namely constructing a data resource tree-shaped resource catalog based on the result of data resource inventory. The data classification is used as a tree structure of a resource directory, the data resources are recombined in an all-around manner, and the data tracing, reference data, main data and metadata are displayed in detail. And finally, constructing data query and retrieval, performing resource retrieval on the names of the data resources on the basis of the data directory, and realizing accurate positioning of the data based on metadata indexes.
According to the method, the customer service business data is combed to form a data set, the business covers 12 business faces such as electric vehicles, electric energy calculation, electronic channels, power failure repair, business, comprehensive support and the like, the online trial operation of a marketing data service resource directory system is completed, and 8000 data service APIs are provided for users.
(5) Data open sharing
Massive electric power marketing field data are accumulated inside a national network company, and how to integrate construction and open utilization of electric power data resources and assist in construction of a national data resource system need to be actively explored urgently.
Data opening needs to pay attention to the form, data safety, data opening degree, data quality, data opening implementation method and technology and the like of the open data. The key points of data opening management comprise:
1) sophisticated policies and management approaches. And formulating a data open management method, which comprises the steps of constructing a data open list, an open mode, guest group interaction, data utilization and a complete system chain of data safety.
2) And opening a data portal. And (5) grading and classifying the open data set.
3) And (5) reusing the data. Paying attention to the reuse of data, adopting encouraging measures to arouse enterprises and innovators to develop more applications by utilizing open data and promoting the harmonious development of various industries.
4) Data security and privacy protection. While data is opened, the security, privacy protection and confidentiality of the data are emphasized, and the requirements of relevant laws and regulations are strictly followed.
Claims (7)
1. A data asset management method of power customer service business is characterized by comprising the following steps:
(1) a data system is constructed by adopting a mode of combining service drive and data drive;
(2) data quality management and data governance;
(3) performing data asset transformation;
(4) performing data asset management based on the data inventory and the data aggregation;
the data checking adopts a mode of combining a data system off-line construction method and an automatic checking method to check data resources, and comprises the following steps:
(4.1) ODS layer paste source construction;
(4.2) carrying out multi-table normalization processing on the detail layer;
and (4.3) automatically acquiring metadata of libraries, tables, fields and the like.
(4.4) setting a business rule, and carrying out data classification on the data, wherein the data classification comprises business classification, theme classification and region classification;
(4.5) identifying the sensitive field through a data resource management tool, and carrying out sensitive field marking and desensitization decryption treatment on the autonomously configured sensitive rule;
the data collection adopts a data extraction method, in the process of constructing a digital warehouse system, metadata management rules are precipitated, and finally a data resource catalog and a data map are constructed; constructing a data resource tree-shaped resource catalog based on the result of data resource inventory; taking data classification as a tree structure of a resource directory, carrying out all-around recombination on data resources, and displaying data traceability, reference data, main data and metadata; finally, data query and retrieval are constructed, resource retrieval is carried out on the names of the data resources on the basis of the data directory, and data positioning is carried out on the basis of metadata indexes;
(5) and (4) data is shared in an open mode.
2. The method as claimed in claim 1, wherein the power customer service comprises 4 service areas of customer service and customer relationship, power charge management, electric energy metering and information collection, and market and demand side.
3. The data asset management method of power customer service business as claimed in claim 1, wherein the business information is divided into seven subject domains according to the business characteristics of the information itself:
a client domain: the system comprises clients, client public and client label information, and provides core data for developing specialized and lean management and supporting big data analysis for the center;
service domain: the system comprises service trace information, customer service information and value-added service information, is sourced from a 95598 system and an online national network, is used for customer service, accurate service and big data analysis service, and provides production service data; meanwhile, the method focuses on providing service, service interaction and other service management related process information for terminal users and channel data;
an operation domain: the system comprises service operation, telephone traffic operation, website operation, online national network operation and data operation, is derived from a 95598 service support system, an online national network, a tag library and an operation regulation and control system, and is used for carrying out specialized and lean management and supporting big data analysis in the center and providing service and data management data;
resource domain: the system comprises human resources, power grid resources, service resources, geographic resources, information resources and resource information for providing center development business;
analysis domain: the system comprises big data analysis results, inspection monitoring analysis results, business operation analysis results and service quality analysis results, is derived from big data analysis, reports, service appraisal and satisfaction investigation analysis, and is used for headquarter decision support, central operation promotion, provincial company service promotion, partner value increment and providing analysis data;
a basic supporting domain: the system comprises information technology management, operation and maintenance management, safe operation management and comprehensive management, and is used for carrying out specialized and lean management and supporting big data analysis in the center and providing supporting data;
external data field: external data outside the company and needed by the center is used for expanding the category of the center metadata and providing collaborative data.
4. The data asset management method of power customer service business as claimed in claim 1, wherein in step (2), the data governance is data asset management, model management and standard management are combined in the data exploration process, data quality quantitative evaluation is performed after automatic etl, data resources with unqualified quality are iteratively traced to source and reloaded, and finally a closed loop flow of data governance is formed.
5. The data asset management method of power customer service business as claimed in claim 1, wherein the step (3) comprises value extraction of data resources, quantification of data resources and productization of data assets.
6. The method for managing data assets of power customer service business as claimed in claim 5, wherein the realization of the productization of the data assets comprises: visualization display of a data asset service directory, customization of data products, various forms of result release and data asset open service provision.
7. The method for managing data assets of power customer service business as claimed in claim 1, wherein the step (5) comprises perfecting policies and management methods, opening data portals, data reuse, data security and privacy protection.
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