Implementation method for process optimization and monitoring of data governance
By combining a dynamic rule engine and machine learning algorithms, real-time monitoring and intelligent optimization of the data governance process are achieved, solving the problems of insufficient real-time performance and poor flexibility in existing technologies, and improving the efficiency and visualization of data governance.
Patent Information
- Application Number
- CN202510829704.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-20
- Publication Date
- 2025-10-31
AI Technical Summary
Existing data governance technologies suffer from insufficient real-time performance, poor flexibility, lack of intelligent analysis, and unintuitive governance progress, resulting in delayed problem detection and an inability to adapt to dynamic changes and data environments.
We use a dynamic rule engine to configure data governance rules, combine machine learning algorithms for real-time monitoring and analysis, use visualization tools to display progress, and automatically optimize the data governance process.
It enables real-time monitoring, flexible adaptation, and intelligent analysis of the data governance process, improving the efficiency and visualization of data governance, and lowering the barrier to understanding and use for users.
Smart Images
Figure CN120873054A_ABST
Abstract
Description
Technical Field
[0001] This invention discloses a method for optimizing and monitoring the data governance process, which relates to the field of data governance technology. Background Technology
[0002] With the advent of the big data era, data governance has become a crucial foundation for management and decision-making. Existing data governance technologies typically rely on static rules and manual monitoring, which presents several problems, such as:
[0003] Insufficient real-time capability: Existing methods are unable to monitor the data governance process in real time, resulting in a delay in problem detection;
[0004] Poor flexibility: Static rules cannot adapt to dynamically changing business needs and data environments;
[0005] Lack of intelligence: Existing technologies lack the ability to intelligently analyze and optimize the data governance process; Unintuitive progress of governance: The current progress of data governance cannot be visualized. Summary of the Invention
[0006] This invention addresses the problems of existing technologies by providing a method for optimizing and monitoring the data governance process. It addresses issues such as unintelligent data governance processes, unclear progress, ambiguous results, and difficulties in data rollback by implementing dynamic task configuration and process archiving, real-time monitoring of data governance task status, and data backup and rollback.
[0007] The specific solution proposed in this invention is as follows:
[0008] This invention provides a method for optimizing and monitoring the data governance process, comprising:
[0009] Step 1: Connect Dynamic Data: Configure the data source, supporting access to multiple data sources, including API data and database data.
[0010] Step 2: Configure rules: Based on the dynamic rule engine, configure the execution rules and monitoring rules for data governance, supporting real-time updates and adjustments.
[0011] Step 3: Real-time monitoring: Monitor the data governance process in real time to identify abnormal data and violations.
[0012] Step 4: Intelligent Analysis: Utilize machine learning algorithms to analyze monitoring data, predict potential problems, and provide optimization suggestions.
[0013] Step 5: Visualization: Display monitoring results and analysis reports using visualization tools to help users quickly understand the progress and status of data governance.
[0014] Step 6: Automatic Optimization: Based on the analysis results, automatically suggest data governance strategies or trigger alarm mechanisms.
[0015] Furthermore, in step 1 of the data governance process optimization and monitoring implementation method, data source configuration includes: in interface data access, confirming the API URL, request method and required request parameters, inputting relevant information and setting request headers, verifying that the data format meets expectations, and verifying the validity of the interface connection so as to correctly obtain data and save the configuration.
[0016] For database access, select the appropriate database type and enter the database connection information, including hostname, port number, database name, and login credentials.
[0017] After a successful connection, select the target data table and configure the required fields and their attributes. Finally, perform a connection test to ensure that the data is available.
[0018] Furthermore, in step 2 of the method for optimizing and monitoring the data governance process, the execution rules and monitoring rules for data governance are configured, including: defining rules for data cleaning, transformation and compliance, and adjusting them in real time to adapt to data changes, monitoring data flow and quality, and triggering preset alarms in a timely manner for data problem handling.
[0019] Furthermore, step 3 of the data governance process optimization and monitoring implementation method includes identifying abnormal data and violations, including:
[0020] When identifying anomalous data, it detects data pattern deviations, missing values, duplicate data, and values outside the reasonable range, uses machine learning algorithms to assess data quality, and automatically marks potential objections.
[0021] When identifying violations, monitor audit logs, track user activity, detect unauthorized data modifications or access, and issue alerts promptly.
[0022] This invention also provides a device for optimizing and monitoring the data governance process, including an access module, a rule configuration module, a monitoring module, an analysis module, a display module, and an optimization module.
[0023] The access module handles dynamic data: it configures data sources and supports access to multiple data sources, including API data and database data.
[0024] The rules configuration module configures rules: based on a dynamic rules engine, it configures execution and monitoring rules for data governance, supporting real-time updates and adjustments.
[0025] The monitoring module provides real-time monitoring of the data governance process, identifying abnormal data and violations.
[0026] The analysis module performs intelligent analysis: it uses machine learning algorithms to analyze monitoring data, predict potential problems, and provide optimization suggestions.
[0027] The visualization module displays monitoring results and analysis reports through visualization tools, allowing users to quickly understand the progress and status of data governance.
[0028] The optimization module performs automatic optimization: based on the analysis results, it automatically prompts data governance strategies or triggers alarm mechanisms.
[0029] Furthermore, the access module of the data governance process optimization and monitoring implementation device configures the data source, including: in interface data access, confirming the API URL, request method and required request parameters, inputting relevant information and setting request headers, verifying that the data format meets expectations, and verifying the validity of the interface connection, so as to correctly obtain data and save the configuration.
[0030] For database access, select the appropriate database type and enter the database connection information, including hostname, port number, database name, and login credentials.
[0031] After a successful connection, select the target data table and configure the required fields and their attributes. Finally, perform a connection test to ensure that the data is available.
[0032] Furthermore, the rule configuration module of the data governance process optimization and monitoring implementation device configures the execution rules and monitoring rules for data governance, including: defining rules for data cleaning, transformation and compliance, and adjusting them in real time to adapt to data changes, monitoring data flow and quality, and triggering preset alarms in a timely manner for data problem handling.
[0033] Furthermore, the monitoring module of the data governance process optimization and monitoring implementation device identifies abnormal data and violations, including:
[0034] When identifying anomalous data, it detects data pattern deviations, missing values, duplicate data, and values outside the reasonable range, uses machine learning algorithms to assess data quality, and automatically marks potential objections.
[0035] When identifying violations, monitor audit logs, track user activity, detect unauthorized data modifications or access, and issue alerts promptly.
[0036] The advantages of this invention are:
[0037] High real-time performance: Through real-time data collection and monitoring, problems can be identified and resolved in a timely manner.
[0038] High flexibility: The dynamic rule engine supports quick adjustment of monitoring rules to adapt to business changes.
[0039] Intelligent analysis: Utilizing machine learning techniques to provide optimization suggestions for data governance and improve governance efficiency.
[0040] Visual presentation: By using intuitive visualization tools, the barrier to understanding and use for users is lowered. Attached Figure Description
[0041] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation
[0042] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.
[0043] Example 1
[0044] This invention provides a method for optimizing and monitoring the data governance process, comprising:
[0045] Step 1: Connect dynamic data: Configure the data source, which supports the connection of multiple data sources, including interface data and database data.
[0046] This includes configuring the data source, including:
[0047] During API data access, users need to confirm the API URL, request method (e.g., GET or POST), and required request parameters, input relevant information, and set request headers to ensure the data format (e.g., JSON) meets expectations. Next, a testing function is provided to verify the validity of the API connection, ensuring that data can be correctly retrieved and configurations saved.
[0048] For database access, users need to select the appropriate database type, such as MySQL or MongoDB, and enter the database connection information, including the hostname, port number, database name, and login credentials. After a successful connection, users select the target data table and configure the required fields and attributes. Finally, a connection test is performed to ensure data availability. After completing the above steps, users can save the configuration to enable automatic extraction and analysis of dynamic data, supporting business decision-making. This process ensures the flexibility of the data source and the scalability of the system.
[0049] Step 2: Configure rules: Based on the dynamic rule engine, configure the execution rules and monitoring rules for data governance, supporting real-time updates and adjustments.
[0050] When configuring execution and monitoring rules for data governance, users can define rules for data cleansing, transformation, and compliance through the interface, and adjust them in real time to adapt to data changes. Automatic monitoring of data flow and quality triggers pre-set alerts promptly, ensuring data issues are addressed quickly. The dynamic updating capability of rules enables enterprises to flexibly respond to market changes, improve data governance efficiency, and ultimately optimize business decision-making processes and data asset management.
[0051] Step 3: Real-time monitoring: Monitor the data governance process in real time to identify abnormal data and violations.
[0052] When identifying outliers, this includes detecting data pattern deviations, missing values, duplicate data, and values outside the reasonable range. Machine learning algorithms are used to assess data quality and automatically flag potential objections.
[0053] Meanwhile, when identifying violations, the monitoring system tracks user activity through audit logs, detects unauthorized data modifications or access, and issues timely alerts. This real-time monitoring mechanism ensures the compliance and reliability of data governance, improving the overall level of data management.
[0054] Step 4: Intelligent Analysis: Utilize machine learning algorithms to analyze monitoring data, predict potential problems, and provide optimization suggestions.
[0055] The analysis of monitoring data using machine learning algorithms allows for the training of models to identify normal data patterns and behaviors. Algorithms such as clustering analysis, anomaly detection, and classifiers are applied to historical data, automatically learning data characteristics and identifying anomalies in real-time data streams. These algorithms enable the rapid identification of anomalous data points and potential violations. The continuously learning models are constantly optimized with the addition of new data, improving monitoring efficiency and accuracy, ensuring the effectiveness and compliance of the data governance process, and providing a reliable basis for decision-making.
[0056] Step 5: Visualization: Display monitoring results and analysis reports using visualization tools to help users quickly understand the progress and status of data governance.
[0057] Step 6: Automated Optimization: Based on the analysis results, the system automatically prompts data governance strategies or triggers alarm mechanisms. During the data governance process, an intelligent rule engine is established to monitor the data flow and analyze its quality in real time. When data anomalies or potential risks are detected, corresponding governance strategy suggestions are automatically generated, such as data cleaning, completion, or auditing measures. Simultaneously, the triggered alarm mechanism immediately notifies relevant personnel, providing detailed anomaly information and suggested handling solutions.
[0058] Example 2
[0059] This invention also provides a device for optimizing and monitoring the data governance process, including an access module, a rule configuration module, a monitoring module, an analysis module, a display module, and an optimization module.
[0060] The access module handles dynamic data: it configures data sources and supports access to multiple data sources, including API data and database data.
[0061] The rules configuration module configures rules: based on a dynamic rules engine, it configures execution and monitoring rules for data governance, supporting real-time updates and adjustments.
[0062] The monitoring module provides real-time monitoring of the data governance process, identifying abnormal data and violations.
[0063] The analysis module performs intelligent analysis: it uses machine learning algorithms to analyze monitoring data, predict potential problems, and provide optimization suggestions.
[0064] The visualization module displays monitoring results and analysis reports through visualization tools, allowing users to quickly understand the progress and status of data governance.
[0065] The optimization module performs automatic optimization: based on the analysis results, it automatically prompts data governance strategies or triggers alarm mechanisms.
[0066] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description in the method embodiment of the present invention, and will not be repeated here.
[0067] Similarly, the advantages of the device of the present invention are:
[0068] High real-time performance: Through real-time data collection and monitoring, problems can be identified and resolved in a timely manner.
[0069] High flexibility: The dynamic rule engine supports quick adjustment of monitoring rules to adapt to business changes.
[0070] Intelligent analysis: Utilizing machine learning techniques to provide optimization suggestions for data governance and improve governance efficiency.
[0071] Visual presentation: By using intuitive visualization tools, the barrier to understanding and use for users is lowered.
[0072] It should be noted that not all steps and modules in the above processes and device structures are mandatory; some steps or modules can be omitted as needed. The execution order of each step is not fixed and can be adjusted as required. The system structure described in the above embodiments can be a physical structure or a logical structure. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be jointly implemented by certain components in multiple independent devices.
[0073] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.
Claims
1. A method for optimizing and monitoring the data governance process, characterized by: include: Step 1: Connect Dynamic Data: Configure the data source, supporting access to multiple data sources, including API data and database data. Step 2: Configure rules: Based on the dynamic rule engine, configure the execution rules and monitoring rules for data governance, supporting real-time updates and adjustments. Step 3: Real-time monitoring: Monitor the data governance process in real time to identify abnormal data and violations. Step 4: Intelligent Analysis: Utilize machine learning algorithms to analyze monitoring data, predict potential problems, and provide optimization suggestions. Step 5: Visualization: Display monitoring results and analysis reports using visualization tools to help users quickly understand the progress and status of data governance. Step 6: Automatic Optimization: Based on the analysis results, automatically suggest data governance strategies or trigger alarm mechanisms.
2. The method for optimizing and monitoring the data governance process according to claim 1, characterized in that: In step 1, the data source is configured, including: in the interface data access, confirming the API URL, request method and required request parameters, entering relevant information and setting request headers, verifying that the data format meets expectations, and verifying the validity of the interface connection so as to correctly obtain data and save the configuration; For database access, select the appropriate database type and enter the database connection information, including hostname, port number, database name, and login credentials. After a successful connection, select the target data table and configure the required fields and their attributes. Finally, perform a connection test to ensure that the data is available.
3. The method for optimizing and monitoring the data governance process according to claim 1, characterized in that: Step 2 involves configuring execution and monitoring rules for data governance, including defining rules for data cleaning, transformation, and compliance, adjusting them in real time to adapt to data changes, monitoring data flow and quality, and triggering preset alerts in a timely manner for handling data issues.
4. The method for optimizing and monitoring the data governance process according to claim 1, Its characteristic is that step 3 identifies anomalous data and violations, including: When identifying anomalous data, it detects data pattern deviations, missing values, duplicate data, and values outside the reasonable range, uses machine learning algorithms to assess data quality, and automatically marks potential objections. When identifying violations, monitor audit logs, track user activity, detect unauthorized data modifications or access, and issue alerts promptly.
5. A device for optimizing and monitoring the data governance process, characterized in that: It includes an access module, a rule configuration module, a monitoring module, an analysis module, a display module, and an optimization module. The access module handles dynamic data: it configures data sources and supports access to multiple data sources, including API data and database data. The rules configuration module configures rules: based on a dynamic rules engine, it configures execution and monitoring rules for data governance, supporting real-time updates and adjustments. The monitoring module provides real-time monitoring of the data governance process, identifying abnormal data and violations. The analysis module performs intelligent analysis: it uses machine learning algorithms to analyze monitoring data, predict potential problems, and provide optimization suggestions. The visualization module displays monitoring results and analysis reports through visualization tools, allowing users to quickly understand the progress and status of data governance. The optimization module performs automatic optimization: based on the analysis results, it automatically prompts data governance strategies or triggers alarm mechanisms.
6. The data governance process optimization and monitoring implementation device according to claim 5, characterized in that: The access module configures the data source, including: in interface data access, confirming the API URL, request method and required request parameters, inputting relevant information and setting request headers, verifying that the data format meets expectations, and verifying the validity of the interface connection in order to correctly obtain data and save the configuration; For database access, select the appropriate database type and enter the database connection information, including hostname, port number, database name, and login credentials. After a successful connection, select the target data table and configure the required fields and their attributes. Finally, perform a connection test to ensure that the data is available.
7. The data governance process optimization and monitoring implementation device according to claim 5, characterized in that: The rules configuration module configures the execution and monitoring rules for data governance, including: defining rules for data cleaning, transformation, and compliance, and adjusting them in real time to adapt to data changes; monitoring data flow and quality; and triggering preset alerts in a timely manner for handling data issues.
8. The data governance process optimization and monitoring implementation device according to claim 5, characterized in that: The monitoring module identifies abnormal data and violations, including: When identifying anomalous data, it detects data pattern deviations, missing values, duplicate data, and values outside the reasonable range, uses machine learning algorithms to assess data quality, and automatically marks potential objections. When identifying violations, monitor audit logs, track user activity, detect unauthorized data modifications or access, and issue alerts promptly.