CN116303408A - DAMA data frame-based data governance process management method and system - Google Patents
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Abstract
The invention discloses a data management process management method and system based on a DAMA data framework, and relates to the technical field of data management. The method comprises the following steps: firstly, acquiring original project data based on a DAMA data frame, and performing data management to obtain standard project data. Then, metadata of standard project data is determined, a standard project database of an initial data governance model is constructed, a deployment data governance model is generated, and real-time project data is input into the deployment data governance model in combination with application scene requirements and business requirements to obtain a decision management result. And finally, dynamically updating the deployment data management model and the data management strategy according to the feedback result. The method solves the problems that the prior method can not meet the overall treatment requirement of organizations on data, and has unstable data quality and difficult data sharing.
Description
Technical Field
The invention relates to the technical field of data management, in particular to a data management process management method and system based on a DAMA data frame.
Background
Data governance is a process that ensures that data remains high quality, reliable, safe, and compliant throughout the life cycle. In data management, a number of aspects need to be involved, including data acquisition, data management planning, and the like. Existing data governance process management methods are typically based on rules and procedures that are used to ensure the quality, availability, trustworthiness and security of data assets.
The existing data management process management method has a decentralized management mode and lacks systematic and integral planning and management. The method is difficult to meet the overall management requirement of organizations on data, and has the problems of unstable data quality, difficult data sharing and the like.
Disclosure of Invention
The invention provides a data management process management method and system based on a DAMA data framework, which solve the problems that the prior method can not meet the overall management requirement of organizations on data, and has unstable data quality and difficult data sharing.
In a first aspect, an embodiment of the present invention provides a data management method based on a DAMA data frame, where the method includes:
acquiring original project data of different project dimensions based on a DAMA data frame, and performing data management on the original project data according to a preset data management strategy to acquire standard project data;
determining metadata of standard project data, and constructing a standard project database at least comprising an initial data governance model according to the metadata of the standard project data;
acquiring application scene requirements and business requirements, and generating a deployment data management model by combining the initial data management model;
Acquiring real-time project data, and inputting the real-time project data into a deployment data management model to obtain a decision management result;
and obtaining a feedback result of the decision management result, and dynamically updating the deployment data management model and the data management strategy according to the feedback result.
In an alternative implementation, obtaining raw project data for different project dimensions based on a DAMA data framework includes:
determining at least one target item dimension, and configuring a DAMA data frame link of the target item dimension;
and generating a data query channel corresponding to the dimension of the target item according to the DAMA data frame link.
In an alternative implementation, the data management policies include a data format management policy and a data naming management policy, and performing data governance on the original project data according to a preset data management policy to obtain standard project data, including:
according to the data format management strategy, carrying out data cleaning and data aggregation on the original project data with different project dimensions so as to obtain first intermediate project data;
renaming and regulating the first intermediate item data according to a data naming management strategy to obtain second intermediate item data;
And checking and rechecking the second intermediate project data to obtain standard project data.
In an alternative implementation, constructing a standard project database including at least one initial data governance model based on metadata of the standard project data, includes:
creating a data warehouse according to the metadata of the standard project data;
generating a data architecture corresponding to the data warehouse according to the metadata of the standard project data;
and constructing an initial data governance model according to the data architecture.
In an alternative implementation, the method includes obtaining an application scenario requirement and a service requirement, and generating a deployment data governance model in combination with an initial data governance model, including:
creating a plurality of first management nodes corresponding to application scene requirements;
creating a plurality of second management nodes corresponding to the service demands;
and creating a node connection relation between the first management node and the second management node, and mapping the node connection relation to the initial data governance model to obtain a deployment data governance model.
In an alternative implementation, acquiring real-time project data and inputting the real-time project data into a deployment data governance model to obtain decision management results, comprising:
Acquiring an identification tag carried in real-time item data, wherein the identification tag comprises an item type tag and a data type tag;
according to the type label, carrying out grading desensitization treatment on the real-time project data;
and inputting the real-time project data into a corresponding deployment data management model according to the project type label so as to obtain a decision management result.
In an alternative implementation, dynamically updating the deployment data governance model and the data management policy according to the feedback result includes:
performing surface layer dimension analysis on the feedback result to obtain an optimization grade of feedback result representation;
deep dimension analysis is carried out on the feedback result to obtain a root cause analysis result of the feedback result;
and executing a dynamic update strategy matched with the optimization level and the root cause analysis result so as to dynamically update the deployment data management model and the data management strategy.
In a second aspect, an embodiment of the present invention provides a data management process management system based on a DAMA data frame, where the system includes:
the data processing module is used for acquiring original project data with different project dimensions based on the DAMA data frame, and carrying out data treatment on the original project data according to a preset data management strategy so as to acquire standard project data;
The database construction module is used for determining metadata of the standard project data and constructing a standard project database at least comprising an initial data management model according to the metadata of the standard project data;
the model generation module is used for acquiring application scene requirements and business requirements and generating a deployment data treatment model by combining the initial data treatment model;
the decision module is used for acquiring real-time project data and inputting the real-time project data into the deployment data management model so as to acquire a decision management result;
and the updating module is used for acquiring a feedback result of the decision management result and dynamically updating the deployment data management model and the data management strategy according to the feedback result.
In an alternative implementation, the data processing module includes:
a first configuration sub-module for determining at least one target item dimension and configuring a DAMA data frame link of the target item dimension;
and the second configuration submodule is used for generating a data query channel corresponding to the dimension of the target item according to the DAMA data frame link.
In an alternative implementation, the data processing module further includes:
the first processing sub-module is used for carrying out data cleaning and data aggregation on the original project data with different project dimensions according to the data format management strategy so as to obtain first intermediate project data;
The second processing sub-module is used for renaming and regulating the first intermediate item data according to the data naming management strategy so as to obtain second intermediate item data;
and the verification sub-module is used for carrying out checksum rechecking on the second intermediate item data so as to obtain standard item data.
In an alternative implementation, the database construction module includes:
a first construction sub-module for creating a data warehouse according to metadata of standard project data;
the second construction submodule is used for generating a data architecture corresponding to the data warehouse according to the metadata of the standard project data;
and the third construction submodule is used for constructing an initial data governance model according to the data architecture.
In an alternative implementation, the model generation module includes:
the first management node construction sub-module is used for creating a plurality of first management nodes corresponding to the application scene requirements;
the second management node construction sub-module is used for creating a plurality of second management nodes corresponding to the service demands;
the model construction sub-module is used for creating a node connection relation between the first management node and the second management node, and mapping the node connection relation to the initial data governance model so as to obtain a deployment data governance model.
In an alternative implementation, the decision module includes:
the label determining sub-module is used for obtaining an identification label carried in real-time item data, wherein the identification label comprises an item type label and a data type label;
the desensitization sub-module is used for carrying out grading desensitization treatment on the real-time project data according to the type label;
and the decision management result obtaining sub-module is used for inputting real-time project data into the corresponding deployment data management model according to the project type label so as to obtain a decision management result.
In an alternative implementation, the updating module includes:
the first analysis sub-module is used for carrying out surface layer dimension analysis on the feedback result so as to obtain the optimization grade of the feedback result characterization;
the second analysis submodule is used for carrying out deep dimension analysis on the feedback result so as to obtain a root cause analysis result of the feedback result;
and the updating sub-module is used for executing a dynamic updating strategy matched with the optimization level and the root cause analysis result so as to dynamically update the deployment data management model and the data management strategy.
A third aspect of an embodiment of the present invention provides an electronic device, including:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method set forth in the first aspect of the embodiments of the present invention.
A fourth aspect of the embodiments of the present invention proposes a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as proposed in the first aspect of the embodiments of the present invention.
The invention has the following beneficial effects:
in the scheme of the invention, firstly, the original project data is acquired based on a DAMA data frame, and data management is carried out to obtain standard project data. Then, metadata of standard project data is determined, a standard project database of an initial data governance model is constructed, a deployment data governance model is generated, and real-time project data is input into the deployment data governance model in combination with application scene requirements and business requirements to obtain a decision management result. And finally, dynamically updating the deployment data management model and the data management strategy according to the feedback result. Thus having the following advantages:
compared with the existing data management process management method, the DAMA data framework is adopted, so that the management requirements of organizations on data can be more comprehensively met. Meanwhile, through the establishment of a data management strategy and a data management model, the problems of unstable data quality, difficult data sharing and the like in the existing method can be effectively solved. In addition, the scheme can dynamically update the data management strategy and the data management model, so that the data management work is more real-time and efficient.
Drawings
FIG. 1 is a schematic diagram of an electronic device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flow chart of steps of a method for managing a data governance process based on a DAMA data framework according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of functional modules of a data governance process management system based on a DAMA data framework according to an embodiment of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The scheme of the invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an electronic device in a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the electronic device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage system separate from the processor 1001 described above.
Those skilled in the art will appreciate that the structure shown in fig. 1 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
As shown in fig. 1, an operating system, a data storage module, a network communication module, a user interface module, and an electronic program may be included in the memory 1005 as one type of storage medium.
In the electronic device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the electronic device of the present invention may be disposed in the electronic device, and the electronic device invokes the data management process management system based on the DAMA data frame stored in the memory 1005 through the processor 1001, and executes the data management process management method based on the DAMA data frame provided by the embodiment of the present invention.
Referring to fig. 2, based on the foregoing hardware operating environment, an embodiment of the present invention provides a data management process management method based on a DAMA data framework, which specifically includes the following steps:
s201: the method comprises the steps of obtaining original project data of different project dimensions based on a DAMA data frame, and carrying out data management on the original project data according to a preset data management strategy to obtain standard project data.
In this embodiment, the DAMA (Data Management Association) data framework is a framework widely used in the field of data management, and is intended to help an organization to better manage and utilize data resources. The method comprises the steps of obtaining original project data of different project dimensions based on a DAMA data frame, and carrying out data management on the original project data according to a preset data management strategy to obtain standard project data. In the process, first, a plurality of original project data with different project dimensions are required to be acquired, and data management operation is carried out on the data according to a preset data management strategy so as to acquire project data meeting the standard.
The DAMA data framework is used for acquiring standard project data, so that the organization can be helped to better manage the data, and the data quality and the data availability are improved, thereby improving the service decision efficiency and accuracy. Meanwhile, the data management process based on the DAMA data framework can also provide better data security and data compliance, protect data privacy and confidentiality and reduce risks faced by organizations in terms of data management and management.
The specific implementation step for acquiring the original project data of different project dimensions based on the DAMA data frame comprises the following steps:
S201-1: at least one target item dimension is determined and DAMA data frame links of the target item dimension are configured.
In this embodiment, the target item dimension refers to an item dimension that needs to be subjected to data management, and may be a customer, a product, a geographic location, or the like. Then, the data links of the target item dimension need to be configured according to the DAMA data framework. The DAMA data framework provides a standard set of data management specifications that can help data governors define the flow and method of data management. The data link refers to the transmission and conversion modes of data in different links, including data acquisition, data cleaning, data storage and the like.
S201-2: and generating a data query channel corresponding to the dimension of the target item according to the DAMA data frame link.
In this embodiment, generating the data query channel corresponding to the dimension of the target item according to the DAMA data frame link refers to performing standardization processing on the original data and organizing the original data into the corresponding data warehouse in order to meet the data query requirements of different service fields in the data management process, so as to perform quick query and analysis on the data.
As an example, assume that there is a data governance project of an e-commerce platform, where the project needs to acquire original project data of different project dimensions such as order information, commodity information, user information, transaction information, and the like, and perform data governance on the project data according to a preset data management policy, so as to obtain standard project data. Then, a standard project database of at least one initial data governance model is constructed according to the metadata of the standard project data, and a corresponding data warehouse and data architecture are generated. In this process, data query requirements of different business fields, such as order information query, user information query, etc., need to be considered. Therefore, a corresponding data query channel needs to be constructed according to the dimension of the target item so as to quickly query and analyze the data.
For order information inquiry, an order data inquiry channel can be constructed, the channel comprises fields of order number, order placing time, commodity name, commodity price and the like, and corresponding indexes are built according to the fields so as to quickly inquire the order information. For user information inquiry, a user data inquiry channel can be constructed, and the channel contains fields of user ID, user name, registration time, receiving address and the like, and a corresponding index is built according to the fields so as to quickly inquire the user information.
Therefore, according to the DAMA data frame link, the generation of the data query channel corresponding to the dimension of the target item is a key link in the data treatment process, and can meet the data query requirements of different service fields, and the availability and the value of the data are improved.
And carrying out data management on the original project data according to a preset data management strategy to obtain specific implementation steps of standard project data, wherein the specific implementation steps comprise:
s201-3: and according to the data format management strategy, carrying out data cleaning and data aggregation on the original project data with different project dimensions so as to obtain first intermediate project data.
In this embodiment, before data cleaning, the original project data needs to be initially identified and classified in format, then data cleaning and conversion are performed according to a set data format management policy, and useless data such as missing values, abnormal values and the like in the original data are processed, so that the integrity and accuracy of the data are ensured. The data aggregation process is to combine the cleaned data according to a set rule, and aggregate the data according to the required project dimension to obtain first intermediate project data.
As an example, the e-commerce platform needs to acquire various data such as order information, commodity information, user information, and the like. Since the data formats of different sources may be different, these raw data need to be cleaned and aggregated according to a data format management policy. For example, order information may come from multiple sources, including online ordering, offline stores, third party platforms, etc., requiring integration of order data from different sources, removal of duplicate, erroneous data, and conversion to a uniform data format. The commodity information may include a plurality of attributes, such as names, prices, brands, places of production, etc., and the commodity data of different channels need to be subjected to duplicate removal, screening and standardization processing so as to facilitate subsequent data analysis.
S201-4: and renaming and regulating the first intermediate item data according to the data naming management strategy to obtain second intermediate item data.
In this embodiment, the data naming management policy refers to naming data according to a certain rule and standard to ensure the readability, maintainability and consistency of the data, so that the first intermediate item data needs to be renamed and ordered to obtain the second intermediate item data. Before renaming, the first intermediate item data needs to be subjected to preliminary data cleaning and conversion, and naming standardization processing is carried out according to a set data naming management strategy. Naming standardization may include a unified specification of column names, table names, etc., following certain naming rules and naming conventions to facilitate management and use of data. In the normalization process, the data is required to be subjected to processing such as de-duplication, sorting and screening, so that the data is easier to manage and use.
By way of example, in an e-commerce platform, data such as order information, merchandise information, etc. may contain a large number of fields that need to be renamed and ordered to facilitate management and analysis of the data. For example, for order information, field names may be unified, such as "order number", etc. fields are unified as "order"; for the commodity information, the commodity attributes may be classified, for example, the attributes such as "name", "name" and the like are unified as "name", and the attributes such as "brand", "manufacturer" and the like are unified as "brand".
S201-5: and checking and rechecking the second intermediate project data to obtain standard project data.
In this embodiment, the verification is to check the data to confirm the correctness and integrity of the data. The rechecking is to perform secondary checking on the data so as to ensure the quality and reliability of the data.
As an example, in sales data of an e-commerce platform, there may be some data anomalies or deletions, and a checksum rechecking process is required. As for the sales field, checks need to be made to ensure that the data has no outliers, such as negative numbers, maxima, etc.; for the commodity information field, a review is required to ensure accuracy and integrity of the data, such as whether the commodity name, description, category, etc. information is correct.
And finally, obtaining second intermediate item data by carrying out data cleaning and data aggregation on the original item data with different dimensions and renaming and regularizing the first intermediate item data. And checking and rechecking the second intermediate item data to finally obtain the standard item data. The data management process can improve the data quality and usability, and provides guarantee for subsequent data analysis and application.
S202: and determining metadata of the standard project data, and constructing a standard project database comprising at least one initial data governance model according to the metadata of the standard project data.
In this embodiment, metadata of standard item data is determined, and when standard item data is established, it is necessary to define and describe data attributes involved and to model and manage relationships between data, and data formed by these definitions and descriptions is metadata. According to the metadata of the standard project data, a standard project database at least comprising an initial data management model is constructed, namely, a standardized data warehouse which can meet service requirements and support data analysis is established based on the metadata to regulate, integrate and manage the data. The data warehouse needs to contain various links such as data acquisition, integration, storage, management and use so as to ensure data quality and data reliability and provide a reliable data basis for business applications.
And according to the metadata of the standard project data, the specific implementation steps of constructing a standard project database at least comprising an initial data governance model include:
s202-1: a data warehouse is created from metadata of standard project data.
S202-2: and generating a data architecture corresponding to the data warehouse according to the metadata of the standard project data.
S202-3: and constructing an initial data governance model according to the data architecture.
In the implementation of S202-1 through S202-3, creating a data warehouse is a critical step in converting data into a format that can be analyzed and queried. The main purpose of this step is to process the raw data in the format specified by the metadata and import it into the data warehouse. For example, assume that there is a sales data set including fields of an order number, a customer name, a product name, a sales date, a sales number, a sales amount, and the like. The table structure in the data warehouse may be designed to contain sales facts tables and multiple dimension tables (e.g., time dimension, product dimension, customer dimension, etc.) for all of the fields described above, according to the definition of metadata. By creating a data warehouse, the raw data can be converted into structured data that is easy to analyze and query, and support for subsequent analysis and queries.
Data architecture refers to structures and components used to organize and manage data, including tables, columns, relationships, and constraints, among others, in a data warehouse. Based on the data format and data storage manner defined by the metadata, a data structure corresponding to the data warehouse may be generated. For example, in the example of sales data sets, the data architecture may be generated by dimensions and fact table structures defined from metadata. In the data architecture, dimension tables, fact tables and relationships between them will be created, as well as containing detailed information of data types, constraints and indexes.
Constructing an initial data governance model is a key step in creating a data model from data architecture and metadata to manage and control data quality. The initial data governance model will describe the attributes, features and relationships of the data assets and provide standardized and normalized control of the data. For example, in the example of sales data sets, an initial data governance model may be created based on the data architecture, including data dictionaries, data quality rules, data validation programs, and data cleansing programs, among others. This initial data governance model will provide quality assurance and management of the data assets and ensure that the data quality is consistent with expectations.
S203: and acquiring application scene requirements and business requirements, and generating a deployment data governance model by combining the initial data governance model.
In this embodiment, in the data management process, it is very important to acquire the application scenario requirements and the service requirements. The application scenario requirements refer to the scenario and requirements to which the data is applied, such as reporting, analysis, machine learning, etc., while the business requirements refer to the content, quality, accuracy, etc. of the data that the business needs to use. By combining the application scene requirement and the service requirement, the target and the index of the data management can be better determined, and a more definite direction and method are provided for the data management. In generating the deployment data governance model, it is necessary to incorporate the initial data governance model, i.e., the previously constructed standard project database and data warehouse. The initial data governance model is defined based on metadata, and can help identify and correct problems in data, and ensure data quality and accuracy. By combining the application scene requirements and the business requirements, the initial data governance model can be better understood and optimized, and a deployment data governance model is generated for a specific application scene.
And the specific implementation steps of generating the deployment data governance model may include:
S203-1: a plurality of first management nodes corresponding to application scene requirements are created.
S203-2: a plurality of second management nodes corresponding to the business requirements are created.
S203-3: and creating a node connection relation between the first management node and the second management node, and mapping the node connection relation to the initial data governance model to obtain a deployment data governance model.
In the embodiments of S203-1 to S203-3, first, a plurality of first management nodes corresponding to the application scenario requirements need to be created, and these nodes may be divided according to data sources, such as order data nodes, commodity data nodes, user data nodes, and the like. The nodes can respectively process different data sources, process the data, clean the data and the like, and finally form standardized data. Then, a plurality of second management nodes corresponding to the service requirements need to be created, and the nodes can be divided according to different service requirements, such as a sales data analysis node, a user portrayal node, a commodity recommendation node and the like. The nodes can analyze and process the standardized data according to different business requirements, thereby generating useful business values. In order to achieve cooperation between the first management node and the second management node, it is necessary to create a node connection relationship between the nodes. These node connections may be defined and mapped in terms of data flow and data dependencies, such as connecting order data nodes to sales data analysis nodes, connecting user data nodes to user portrayal nodes, etc. Through the node connection relationship, different data nodes can share and exchange data, so that the integration of data and the maximization of value are realized.
Finally, the node connection relationship needs to be mapped to the initial data governance model to obtain the deployment data governance model. This process may be implemented through data warehouse construction and data architecture design. Through converting the node connection relation into a data model and a data structure, centralized management and unified specification of data can be realized, so that the data quality and the data value are improved.
S204: and acquiring real-time project data, and inputting the real-time project data into a deployment data management model to obtain a decision management result.
In this embodiment, acquiring real-time project data and inputting the real-time project data into the deployment data management model to obtain a decision management result refers to acquiring and processing the project data in real time in a data management process so as to establish the deployment data management model in a data warehouse, thereby obtaining a data analysis and decision result with practical application value.
Taking data of an e-commerce platform as an example, acquiring real-time item data may include user transaction behavior, order information, commodity sales data, logistics information, and the like. After pretreatment and cleaning, the data can be input into a data warehouse to build a deployment data governance model. In the model, the data of the electronic commerce platform can be analyzed and mined to identify hot goods for sale, analyze purchasing behavior of consumers, identify bad transaction behavior and the like, provide data support for enterprises and formulate better business strategies.
For example, commodity recommendation and marketing strategies may be optimized by analyzing real-time transaction data to analyze the user's shopping preferences, consumption capabilities, and purchasing habits. In addition, the order data of the electronic commerce platform can be analyzed, and abnormal order behaviors and fraud risks can be identified, so that security threats can be timely found and prevented. And finally, the data analysis and mining results can help the e-commerce platform to formulate a better marketing strategy, so that the user satisfaction and the service benefit are improved.
The specific implementation steps of acquiring real-time project data and inputting the real-time project data into the deployment data governance model to obtain the decision management result may include:
s204-1: and acquiring an identification tag carried in the real-time item data, wherein the identification tag comprises an item type tag and a data type tag.
S204-2: and carrying out hierarchical desensitization treatment on the real-time project data according to the type label.
S204-3: and inputting the real-time project data into a corresponding deployment data management model according to the project type label so as to obtain a decision management result.
In the embodiments of S204-1 through S204-3, the item type tags and the data type tags are identification tags commonly used in data governance. Wherein, the item type label generally refers to the item type to which the data belongs, such as finance, medical treatment, electronic commerce and the like; the data type tag generally refers to the type of data itself, such as customer information, order information, product information, etc. During the process of acquiring real-time project data, sensitive data may exist, and the data needs to be subjected to hierarchical desensitization processing to ensure the data security. At the same time, identification tags are also important metadata that can help better understand and analyze real-time data. According to the project type labels which are defined in advance, inputting real-time project data into corresponding deployment data management models, and processing and analyzing the project data by a data management method to finally obtain a result required by decision management. The process involves preprocessing of data, data analysis, decision support, etc.
Taking e-commerce platform as an example, it is assumed that some real-time user transaction data is to be obtained. Such data may include personal information of the user, order details, payment means, etc., where the personal information and payment means are sensitive data and require desensitization. While acquiring data, it is also necessary to acquire identification tags of the data, including item type tags and data type tags, for subsequent processing and analysis.
For desensitization processing of data, hierarchical processing can be performed according to item type tags. For example, for personal information of a user and payment means, the personal information can be classified into three levels of high, medium and low according to item type tags, and desensitization processes of different degrees can be performed respectively. For higher level data, more stringent processing may be performed, such as processing using encryption algorithms; for medium-level data, partial desensitization processing can be performed, such as displaying only partial information or blurring the information; for low-level data, some simple desensitization process may be performed, such as hiding part of the information or performing a data mask.
The decision management result refers to a conclusion or suggestion which has guiding significance on business decisions and is obtained by analyzing and processing data. In particular, it may be a number, a report or a predictive model. For example, an e-commerce platform wants to optimize the shopping experience of a user, and can obtain information such as user preference, popular merchandise, and bottlenecks in shopping flow by analyzing data such as browsing behavior, search keywords, and purchase records of the user on a website. Based on the method, a recommendation algorithm, a personalized pricing strategy, a flow optimization scheme and the like can be established, so that user satisfaction, sales and profits are improved. These fall within the category of decision management results, which are of great significance to the business and development of enterprises.
S205: and obtaining a feedback result of the decision management result, and dynamically updating the deployment data management model and the data management strategy according to the feedback result.
In this embodiment, in the data governance process, feedback results are obtained, and a process of dynamically adjusting the data management policy and the deployment data governance model is performed using the feedback results. The process is a continuous iterative process, and aims to continuously optimize a data management strategy and deploy a data management model so that the data management model can better meet business requirements and data quality requirements. The problems of data quality, insufficient data management strategies, defects of a data management model and the like can be found through feedback results, and the problems can be caused by various reasons such as service demand change, data source change, data use scene change and the like. Therefore, there is a need to dynamically update data management policies and deploy data governance models through analysis and evaluation of feedback results to better address changing business needs and data quality issues.
And the specific implementation steps of dynamically updating the deployment data management model and the data management strategy according to the feedback result comprise the following steps:
S205-1: and carrying out surface layer dimension analysis on the feedback result to obtain the optimization grade of the feedback result representation.
S205-2: and carrying out deep dimension analysis on the feedback result to obtain a root cause analysis result of the feedback result.
S205-3: and executing a dynamic update strategy matched with the optimization level and the root cause analysis result so as to dynamically update the deployment data management model and the data management strategy.
In the embodiment of S205-1 to S205-3, the first stage is skin dimension analysis, which may be integrated and performed to identify which feedback relates to quality, performance, availability, etc. issues. Depending on the nature and importance of the feedback, an optimization level may be assigned to each feedback result for prioritization in subsequent steps. The second stage is deep dimension analysis, which aims at deep mining the fundamental problem in the feedback results. Through this analysis phase, the cause of the problem can be determined in order to formulate a more efficient solution. Deep dimension analysis typically requires the use of higher level analysis techniques such as cluster analysis, association rule mining, decision tree analysis, and the like. After the surface dimension analysis and the deep dimension analysis are completed, a third stage, namely the execution of the dynamic update strategy, can be entered. And determining a dynamic update strategy according to the optimization level and the root cause analysis result represented by the feedback result, and dynamically updating the deployment data management model and the data management strategy to adapt to the change in the data management process. The process is a cyclic iterative process, and the efficiency and quality of the data management process are gradually improved through continuous feedback and optimization.
As an example, continuing with the description of the above embodiments, assuming that the e-commerce platform finds that there is a performance bottleneck in its merchandise search function, the search engine needs to be optimized to improve the search speed and accuracy. By monitoring and analyzing the data, the average response time of the search engine is found to be higher, and serious bottleneck problems exist.
Firstly, surface layer dimension analysis is required to be carried out on the feedback result so as to obtain the optimization grade of the feedback result representation. According to analysis of the performance problems of the search engine, the optimization grades thereof can be divided into three grades: high, medium, low. Wherein a high optimization level indicates a severe performance problem that needs to be addressed immediately, a medium optimization level indicates a problem that needs to be optimized but is not very urgent, and a low optimization level indicates a problem that needs to be further observed and evaluated.
Secondly, deep dimension analysis is required to be carried out on the feedback result so as to obtain a root cause analysis result of the feedback result. By further analyzing the performance problems of the search engine, it can be found that the root cause is due to the search inefficiency caused by the insufficient optimization of the query algorithm of the search engine. Therefore, optimization of the query algorithm is needed to improve the search efficiency.
And finally, executing a corresponding dynamic update strategy according to the optimization level and the root cause analysis result so as to dynamically update the deployed data management model and the data management strategy. Specifically, for high optimization level problems, immediate action needs to be taken to optimize and update the data governance model and data management policy accordingly. For the problems of medium and low optimization level, further evaluation and observation are needed, and the data management model and the data management strategy are updated timely.
Compared with the existing data management process management method, the DAMA data framework is adopted, so that the management requirements of organizations on data can be more comprehensively met. Meanwhile, through the establishment of a data management strategy and a data management model, the problems of unstable data quality, difficult data sharing and the like in the existing method can be effectively solved. In addition, the scheme can dynamically update the data management strategy and the data management model, so that the data management work is more real-time and efficient.
The embodiment of the invention also provides a data management process management system based on the DAMA data frame, referring to fig. 3, there is shown a functional block diagram of a data management process management system 300 based on the DAMA data frame, where the system may include the following modules: the data processing module 301 is configured to obtain raw project data with different project dimensions based on a DAMA data framework, perform data management on the raw project data according to a preset data management policy to obtain standard project data, the database construction module 302 is configured to determine metadata of the standard project data, construct a standard project database including at least one initial data management model according to the metadata of the standard project data, the model generation module 303 is configured to obtain application scenario requirements and business requirements, combine the initial data management model to generate a deployment data management model, the decision module 304 is configured to obtain real-time project data, input the real-time project data into the deployment data management model to obtain a decision management result, the update module 305 is configured to obtain a feedback result of the decision management result, and dynamically update the deployment data management model and the data management policy according to the feedback result.
In an alternative implementation, the data processing module includes: the first configuration submodule is used for determining at least one target item dimension and configuring a DAMA data frame link of the target item dimension, and the second configuration submodule is used for generating a data query channel corresponding to the target item dimension according to the DAMA data frame link.
In an alternative implementation, the data processing module further includes: the first processing sub-module is used for carrying out data cleaning and data aggregation on the original project data with different project dimensions according to the data format management strategy to obtain first intermediate project data, the second processing sub-module is used for renaming and regulating the first intermediate project data according to the data naming management strategy to obtain second intermediate project data, and the checking sub-module is used for checking and rechecking the second intermediate project data to obtain standard project data.
In an alternative implementation, the database construction module includes: the system comprises a first construction submodule, a second construction submodule and a third construction submodule, wherein the first construction submodule is used for creating a data warehouse according to metadata of standard project data, the second construction submodule is used for generating a data architecture corresponding to the data warehouse according to the metadata of the standard project data, and the third construction submodule is used for constructing an initial data governance model according to the data architecture.
In an alternative implementation, the model generation module includes: the system comprises a first management node construction sub-module, a second management node construction sub-module and a model construction sub-module, wherein the first management node construction sub-module is used for creating a plurality of first management nodes corresponding to application scene demands, the second management node construction sub-module is used for creating a plurality of second management nodes corresponding to business demands, and the model construction sub-module is used for creating node connection relations between the first management nodes and the second management nodes and mapping the node connection relations to an initial data management model so as to obtain a deployment data management model.
In an alternative implementation, the decision module includes: the label determining sub-module is used for obtaining an identification label carried in real-time project data, wherein the identification label comprises a project type label and a data type label, the desensitizing sub-module is used for carrying out grading desensitization processing on the real-time project data according to the type label, and the decision management result obtaining sub-module is used for inputting the real-time project data into a corresponding deployment data management model according to the project type label so as to obtain a decision management result.
In an alternative implementation, the updating module includes: the system comprises a first analysis sub-module, a second analysis sub-module and an updating sub-module, wherein the first analysis sub-module is used for carrying out surface layer dimension analysis on a feedback result to obtain an optimization grade represented by the feedback result, the second analysis sub-module is used for carrying out deep dimension analysis on the feedback result to obtain a root cause analysis result of the feedback result, and the updating sub-module is used for executing a dynamic updating strategy matched with the optimization grade and the root cause analysis result so as to dynamically update a deployment data management model and a data management strategy.
Based on the same inventive concept, another embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus, and the memory is used for storing a computer program, and the processor is used for implementing the data management process management method based on the DAMA data frame according to the present invention when executing the program stored on the memory.
The communication bus mentioned by the above terminal may be a peripheral component interconnect standard (Peripheral Component Interconnect, abbreviated as PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated as EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus. The communication interface is used for communication between the terminal and other devices. The memory may include random access memory (Random Access Memory, RAM) or non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one storage system located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In addition, in order to achieve the above objective, an embodiment of the present invention further provides a computer readable storage medium storing a computer program, where the computer program when executed by a processor implements a data management method based on a DAMA data frame according to an embodiment of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable vehicles having computer-usable program code embodied therein, including but not limited to disk storage, CD-ROM, optical storage, and the like.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. "" and/or "" "means either or both of these can be selected. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the statement "" comprising one … … "", does not exclude the presence of other identical elements in a process, method, article or terminal device comprising the element.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A method for managing a data governance process based on a DAMA data framework, the method comprising:
acquiring original project data of different project dimensions based on a DAMA data frame, and performing data management on the original project data according to a preset data management strategy to acquire standard project data;
determining metadata of the standard project data, and constructing a standard project database at least comprising an initial data governance model according to the metadata of the standard project data;
acquiring application scene requirements and business requirements, and generating a deployment data management model by combining the initial data management model;
acquiring real-time project data, and inputting the real-time project data into the deployment data management model to obtain a decision management result;
And obtaining a feedback result of the decision management result, and dynamically updating the deployment data management model and the data management strategy according to the feedback result.
2. The DAMA data frame-based data governance process management method of claim 1, wherein the DAMA data frame-based acquisition of raw project data of different project dimensions comprises:
determining at least one target item dimension, and configuring a DAMA data frame link of the target item dimension;
and generating a data query channel corresponding to the dimension of the target item according to the DAMA data frame link.
3. The DAMA data frame-based data governance process management method of claim 1, wherein the data management policies include a data format management policy and a data naming management policy, the data governance is performed on the original project data according to a preset data management policy to obtain standard project data, and the method comprises:
according to the data format management strategy, carrying out data cleaning and data aggregation on the original project data with different project dimensions so as to obtain first intermediate project data;
renaming and regulating the first intermediate item data according to the data naming management strategy to obtain second intermediate item data;
And checking and rechecking the second intermediate item data to obtain the standard item data.
4. The DAMA data frame-based data governance process management method of claim 1, wherein constructing a standard project database comprising at least one initial data governance model from metadata of said standard project data comprises:
creating a data warehouse according to the metadata of the standard project data;
generating a data architecture corresponding to the data warehouse according to the metadata of the standard project data;
and constructing the initial data governance model according to the data architecture.
5. The DAMA data framework-based data governance process management method of claim 1, wherein said obtaining application scenario requirements and business requirements and combining said initial data governance model generates a deployment data governance model comprises:
creating a plurality of first management nodes corresponding to the application scene requirements;
creating a plurality of second management nodes corresponding to the service demands;
and creating a node connection relation between the first management node and the second management node, and mapping the node connection relation to the initial data governance model to obtain the deployment data governance model.
6. The DAMA data framework-based data governance process management method of claim 1, wherein said acquiring real-time project data and inputting said real-time project data into said deployment data governance model to obtain decision management results comprises:
acquiring an identification tag carried in the real-time item data, wherein the identification tag comprises an item type tag and a data type tag;
according to the type label, carrying out grading desensitization treatment on the real-time project data;
and inputting the real-time project data into a corresponding deployment data management model according to the project type label so as to obtain the decision management result.
7. The DAMA data framework-based data governance process management method of claim 1, wherein said dynamically updating said deployment data governance model and said data management policy in accordance with said feedback results comprises:
performing surface layer dimension analysis on the feedback result to obtain an optimization grade of the feedback result representation;
carrying out deep dimension analysis on the feedback result to obtain a root cause analysis result of the feedback result;
And executing a dynamic update strategy matched with the optimization level and the root cause analysis result so as to dynamically update the deployment data management model and the data management strategy.
8. A DAMA data management process management system based on a DAMA data framework, the system comprising:
the data processing module is used for acquiring original project data with different project dimensions based on the DAMA data frame, and carrying out data management on the original project data according to a preset data management strategy so as to acquire standard project data;
the database construction module is used for determining the metadata of the standard project data and constructing a standard project database at least comprising an initial data governance model according to the metadata of the standard project data;
the model generation module is used for acquiring application scene requirements and business requirements, and generating a deployment data treatment model by combining the initial data treatment model;
the decision module is used for acquiring real-time project data and inputting the real-time project data into the deployment data management model to obtain a decision management result;
and the updating module is used for acquiring a feedback result of the decision management result and dynamically updating the deployment data management model and the data management strategy according to the feedback result.
9. The DAMA data frame-based data governance process management system of claim 8, wherein said data processing module comprises:
a first configuration sub-module for determining at least one target item dimension and configuring a DAMA data frame link of the target item dimension;
and the second configuration submodule is used for generating a data query channel corresponding to the dimension of the target item according to the DAMA data frame link.
10. The DAMA data frame-based data governance process management system of claim 8, wherein said data processing module further comprises:
the first processing sub-module is used for carrying out data cleaning and data aggregation on the original project data with different project dimensions according to a data format management strategy so as to obtain first intermediate project data;
the second processing sub-module is used for renaming and regulating the first intermediate item data according to a data naming management strategy so as to obtain second intermediate item data;
and the verification sub-module is used for carrying out checksum rechecking on the second intermediate item data so as to obtain the standard item data.
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