A small and medium-sized enterprise data asset whole life cycle management and control system and an implementation method thereof

Abstract

A data asset lifecycle management system for SMEs and its implementation method are disclosed, comprising a data access and governance module, a data asset modeling and evaluation module, a data ownership and compliance module, a data circulation and authorization module, a data usage monitoring and revenue module, a data archiving and destruction module, and a unified control and scheduling platform. By constructing a lifecycle state model centered on data asset objects, the system provides unified management of the data collection, governance, ownership confirmation, valuation, authorized use, revenue calculation, and archiving and destruction processes. Data quality assessment results, risk levels, and value assessment results are embedded into the access control and authorization decision-making mechanism, achieving synergistic optimization of data asset availability, security, and economic value. This addresses the problems of unclear data ownership, fragmented governance, restricted circulation, and high compliance risks faced by SMEs, enabling controllable operation and value transformation of data assets throughout the entire process.

Description

Technical Field

[0001] This invention belongs to the field of digital asset management technology, specifically referring to a data asset full lifecycle management system for small and medium-sized enterprises and its implementation method. Background Technology

[0002] As digital transformation deepens, SMEs have gradually accumulated a large amount of business data resources in the process of operation and management, production and manufacturing, customer service and supply chain collaboration. However, the existing data management model is mostly centered on databases or information systems, focusing on data storage and business support functions. It lacks the ability to systematically control the ownership boundaries, quality status, usage trajectory and value change process of data assets, making it difficult for data resources to form identifiable, quantifiable and operable data assets.

[0003] Existing data governance tools primarily focus on single functions such as data quality testing, data standard management, or access control, making it difficult to achieve closed-loop management covering the entire data lifecycle, from generation and governance to use, circulation, and disposal. Meanwhile, large enterprises face high deployment costs and complex implementation of data asset management platforms, failing to meet the practical needs of SMEs for rapid deployment and flexible expansion under resource constraints. Furthermore, in the context of data compliance regulation and the marketization of data elements, enterprises face issues such as insufficient auditability of data use, unclear data ownership definitions, and difficulty in controlling data circulation risks, further hindering the release and secure utilization of data asset value.

[0004] Therefore, there is a need for a data asset lifecycle management system and its implementation method for SMEs. By constructing a unified data asset object model and lifecycle management mechanism, the system can make the entire process of data assets from generation, governance, use, evaluation, circulation to exit visible, controllable and traceable, so as to solve the problems of fragmented data asset management, difficulty in quantifying value and difficulty in controlling compliance risks in existing technologies. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the present invention provides a data asset full lifecycle management system for small and medium-sized enterprises and its implementation method, so as to at least partially solve the above-mentioned technical problems.

[0006] The technical solution adopted by this invention is as follows: This invention proposes a data asset full lifecycle management system and its implementation method for SMEs, including a data acquisition and access module, a data asset registration and ownership confirmation module, a data quality governance module, a lifecycle status management module, a data asset value assessment module, a data usage and circulation control module, a risk compliance audit module, and a unified management platform module. These modules are interconnected through data interfaces and control interfaces. Specifically, the data acquisition and access module is configured to collect raw data from enterprise business systems, production systems, or external interfaces, and construct candidate data asset objects based on metadata parsing and lineage analysis. The data asset registration and ownership confirmation module is configured to perform ownership confirmation processing on candidate data asset objects based on data source credibility, generation of responsible entities, and business attribution rules, generating a data asset master file containing asset identifiers, ownership entities, usage boundaries, and initial lifecycle status. The data quality governance module is configured to... The system performs integrity, consistency, accuracy, and timeliness checks on registered data assets and generates quality level labels. The lifecycle state management module is configured to build a lifecycle state machine model for each data asset and automatically migrate and verify its lifecycle state based on data asset state change events. The data asset value assessment module is configured to build a value assessment model based on data asset usage behavior, business coverage, quality stability, and risk level, and output dynamic value assessment results. The data usage and circulation control module is configured to implement fine-grained control over data access, invocation, sharing, and external circulation behavior based on data asset status, ownership rules, and authorization policies. The risk compliance audit module is configured to monitor and analyze the entire lifecycle behavior log of data assets and output compliance audit results. The unified management platform module is configured to provide visual presentation and centralized management of the data asset catalog, lifecycle state, value assessment results, and risk situation.

[0007] Furthermore, the data asset registration and ownership confirmation module includes an asset identifier generation unit, an ownership subject identification unit, a responsibility attribution matching unit, and an authorization boundary modeling unit, which are used to construct a data asset master file model that includes a unique asset code, business attribution department, responsible person, scope of use, and authorization level.

[0008] Furthermore, the data quality governance module includes a quality rule base, a quality detection engine, and a quality scoring model unit. The quality scoring model outputs quality level results based on multiple dimensions such as completeness, consistency, accuracy, timeliness, and availability, and binds the quality level results to the usage permission threshold and lifecycle migration conditions of the corresponding data assets.

[0009] Furthermore, the lifecycle state management module constructs a set of data asset lifecycle states based on a finite state machine model. The lifecycle states include at least the generation state, governance state, availability state, operation state, sharing state, frozen state, and deregistration state, and perform automatic state transition control based on data access events, authorization events, quality change events, and risk trigger events.

[0010] Furthermore, the data asset valuation module constructs a multi-factor valuation model. The model generates asset value scores based on parameters such as data asset usage frequency, number of business scenarios covered, quality stability, risk level, decision contribution, and external substitutability. These scores are then used to drive dynamic adjustments to data asset authorization levels, operational strategies, or circulation strategies.

[0011] Furthermore, the data usage and circulation control module includes an asset-level access control unit, a field-level access control unit, and a usage-level authorization control unit, and performs real-time verification and access control on data calling, exporting, sharing, or external provision behaviors based on data asset ownership rules, lifecycle status, and risk level.

[0012] Furthermore, the risk compliance audit module includes a behavior log collection unit, a compliance rule matching unit, and a risk identification and analysis unit, which are used to continuously monitor the behavior trajectory of data assets during the collection, processing, use, sharing, and destruction processes, and generate an audit evidence chain with timestamps and responsible entity identifiers.

[0013] This invention proposes a method for full lifecycle management and control of data assets for small and medium-sized enterprises, including the following steps: S1, collecting raw data from enterprise business systems or external data sources, and constructing candidate data asset objects based on metadata parsing and lineage analysis; S2. Based on the credibility of data sources, the responsible entity, and the rules of business attribution, perform registration and ownership confirmation on candidate data asset objects, generate data asset master files, and initialize lifecycle status; S3. Perform multi-dimensional quality checks on registered data assets and generate quality level results; S4. Automatically migrate and verify the lifecycle status of data assets based on data access, authorization, quality changes, or risk-triggered events. S5. Perform value assessment and generate dynamic value results based on data asset usage behavior, business coverage, quality stability and risk level. S6. Implement fine-grained control over data access, retrieval, sharing, and circulation behaviors based on data asset status, ownership rules, and authorization policies; S7. Monitor and analyze the behavior logs of data assets throughout their entire lifecycle and generate compliance audit results; S8. When data assets meet the exit conditions, perform freezing, destruction, or archiving and seal the historical status trajectory.

[0014] Furthermore, in step S4, by constructing a lifecycle state transition model based on a finite state machine, the data asset quality level is transitioned from the generation state to the available state when it reaches a preset threshold, from the shared state when the data asset is authorized to be shared externally, and from the frozen state when the risk level is detected to exceed the threshold.

[0015] Furthermore, in step S5, the value of data assets is quantified by constructing a multi-factor value assessment function, wherein the value assessment function includes at least the parameters of data usage frequency, business coverage, quality stability, risk level, decision contribution, and external substitutability, and the authorization level or circulation strategy of data assets is dynamically adjusted based on the value assessment results.

[0016] Compared with the prior art, the present invention has the following advantages: By constructing a rights confirmation modeling and lifecycle status control mechanism centered on data asset objects, data is transformed from raw resources into data asset units with unique identifiers, ownership boundaries, and status constraints. This enables the entire process of data asset operation to be computable, controllable, and traceable, fundamentally solving the problems of unclear data ownership and uncontrollable management for SMEs.

[0017] By embedding data quality assessment results, risk levels, and value assessment results into the access control and authorization decision-making model, data governance results can influence data usage behavior and circulation strategies in real time, achieving synergistic optimization of data asset availability, security, and economic value, and improving data asset operation efficiency and compliance.

[0018] A unified control and scheduling platform enables closed-loop collaborative management of data access, governance, ownership confirmation, circulation, usage monitoring, and archiving and destruction. It adopts a modular, event-driven architecture to support progressive deployment and elastic expansion, reducing implementation costs for SMEs and improving the feasibility of system implementation. Attached Figure Description

[0019] Figure 1 This is an architecture diagram of the data asset full lifecycle management and control system for SMEs proposed in this embodiment of the invention; Figure 2 This is a flowchart illustrating the implementation method for the full lifecycle management of data assets for small and medium-sized enterprises proposed in this embodiment of the invention.

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Example 1: like Figure 1 and Figure 2 As shown in the figure, this embodiment discloses a data asset full lifecycle management and control system for SMEs. The system is deployed in the private cloud or local server environment of SMEs and includes a data acquisition and access module, a data asset registration and confirmation module, a data quality governance module, a lifecycle status management module, a data asset value assessment module, a data usage and circulation control module, a risk compliance audit module, and a unified management and control platform module. Each module communicates with data and interacts with control commands through a service bus and a unified interface protocol to realize full-process management and control of enterprise data assets from generation to cancellation.

[0024] The data acquisition and access module establishes data channels with the enterprise's ERP system, CRM system, MES system, and external business systems through API connectors, log acquisition components, or database synchronization components. It automatically collects structured table data, semi-structured log data, and unstructured document data, and extracts field structure, data type, data source system identifier, generation timestamp, and business context information based on metadata parsing algorithms. At the same time, it constructs a source relationship mapping between data entities through lineage analysis algorithms, thereby forming a set of candidate data asset objects containing data source paths and responsible entity information.

[0025] After receiving the candidate data asset object, the data asset registration and ownership confirmation module first generates a unique asset code for it through the asset identification generation unit, and then determines the department and responsible entity of the data asset based on the business system ownership table, data production process mapping table and job responsibility relationship table through the ownership subject identification unit. Then, it configures the scope of use, sharing scenarios and authorization level rules for it through the authorization boundary modeling unit. Finally, it generates a data asset master file containing the unique asset code, ownership subject, responsible person, scope of use and authorization level, and writes it into the asset master data warehouse. At the same time, the initial life cycle state of the data asset is set to the generation state.

[0026] The data quality governance module calls pre-configured integrity rules, consistency rules, accuracy rules, and timeliness rules in the quality rule library to perform quality checks on data assets. Among them, integrity rules are used to detect the proportion of null values ​​in fields, consistency rules are used to detect the consistency of value formats across tables or systems for the same field, accuracy rules are used to detect the proportion of outliers, and timeliness rules are used to detect the data update time interval. The quality scoring model normalizes and weights the above multi-dimensional detection results to generate a quality score value and maps it to a quality level label. When the quality level reaches a preset threshold, the lifecycle state management module automatically triggers the state migration control logic, which migrates the data asset from the generation state to the usable state and simultaneously opens the corresponding data access interface.

[0027] The lifecycle state management module constructs a set of data asset lifecycle states based on a finite state machine model. These lifecycle states include at least the generation state, governance state, availability state, operational state, shared state, frozen state, and deregistration state. Entry conditions, retention conditions, and exit conditions are configured for each state. When a data asset is detected being used by a business system for operational analysis or model training, the lifecycle state management module migrates the data asset from the availability state to the operational state and records the usage event. When a data asset is authorized for cross-departmental sharing or external data service provision, it is migrated to the shared state, and the authorizing entity, authorized purpose, and authorization period information are recorded. When a quality score drops below a threshold, the authorization period expires, or the risk level exceeds a threshold, the data asset is automatically migrated to the frozen state, and its access interface is closed. When the retention period or compliant destruction conditions are met, it is migrated to the deregistration state, and historical trajectory information is archived.

[0028] The data asset valuation module constructs a multi-factor valuation model. This model includes at least the following indicators: data asset usage frequency, number of covered business scenarios, quality stability, risk level, decision contribution, and external substitutability. Each indicator is normalized and then weighted and fused to generate an asset value score and corresponding value level range. Specifically, the usage frequency is obtained by counting the number of data calls per unit time; the number of covered business scenarios is obtained by counting the number of business systems that call the data asset; the quality stability is obtained by counting the fluctuation range of the quality score over multiple periods; the risk level is output by the risk compliance audit module; the decision contribution is obtained by observing changes in related business decision-making results; and the external substitutability is obtained through an external similar data availability evaluation model. The asset value score is used to drive the dynamic adjustment of the data asset's authorization level, usage scope, and circulation strategy.

[0029] The data usage and circulation control module includes an asset-level access control unit, a field-level access control unit, and a usage-level authorization control unit. The asset-level access control unit performs overall access verification on access requests based on the asset's unique code and lifecycle status. The field-level access control unit performs desensitization, masking, or rejection processing on field-level data access based on the field's sensitivity level. The usage-level authorization control unit performs verification by matching the usage purpose declared in the access request with the asset's authorized usage whitelist. When an access request does not match the rules of any control unit, the system rejects the access request and generates an audit record. When the access request passes the verification, the system generates a one-time access token and grants time-limited access permissions.

[0030] The risk compliance audit module deploys behavior log collection components at the data access interface layer, data processing engine layer, and data sharing gateway layer to continuously collect the behavior trajectory of data assets during the collection, processing, use, sharing, and destruction processes. It also identifies risk events such as unauthorized access, deviation from intended use, abnormal increase in access frequency, and abnormal changes in data export volume through a compliance rule matching engine and anomaly pattern recognition algorithm. When a risk event is detected, the system automatically freezes the access permissions of the corresponding data asset, notifies the person responsible for the data asset, and generates an audit evidence chain containing timestamps, asset codes, access subject identifiers, access purpose, and disposal results.

[0031] The unified management and control platform module uses the data asset object model as the core display unit, providing managers with a data asset catalog tree view, lifecycle status flow view, asset value assessment trend chart and risk situation analysis dashboard. It also supports multi-dimensional retrieval and strategy configuration based on asset number, business affiliation, lifecycle status, value level and risk level, thereby realizing centralized and visualized management and control of the entire lifecycle of data assets for SMEs.

[0032] Based on the above system structure, this embodiment also provides a method for full lifecycle management of data assets for SMEs. The implementation process includes: First, collecting raw data from the enterprise's business system or external data sources, and constructing candidate data asset objects through metadata parsing and lineage analysis; then, performing registration and ownership confirmation processing on the candidate data asset objects based on data source credibility, responsible entity, and business attribution rules, generating a data asset master file containing a unique asset code, ownership entity, usage boundaries, and initial lifecycle state; next, performing integrity, consistency, accuracy, and timeliness checks on the registered data assets, and generating quality level results. When the quality level reaches a preset threshold, the data asset state is migrated from the generation state to the usable state; when the data asset is accessed, authorized, its quality changes, or a risk is triggered, a finite state machine model is used... The system automatically migrates and verifies the data asset's lifecycle status. Subsequently, it performs multi-factor value assessment based on data asset usage behavior, business coverage, quality stability, and risk level, generating dynamic value results. Based on these value results, it dynamically adjusts the data asset's authorization level, usage scope, and circulation strategy. Simultaneously, it implements fine-grained access control at the asset, field, and usage levels for the data asset access, access, sharing, and destruction processes, and collects and performs compliance analysis on the entire process behavior logs. When a violation risk is detected, it automatically triggers freezing and auditing processes. When a data asset meets the exit conditions, it is frozen, de-identified, destroyed, and archived, and its historical lifecycle trajectory is sealed, thereby achieving closed-loop management of the entire lifecycle of SME data assets from generation, governance, use, evaluation, circulation to exit.

[0033] Example 2: In Example 2, the lifecycle state management module further introduces state migration priority rules and conflict resolution mechanisms. When the same data asset meets multiple state migration conditions at the same time, the system executes state migration in the order of risk level priority, quality level priority, and authorization level priority. For example, when the data asset meets both the shared state migration condition and the frozen state migration condition at the same time, the frozen state migration is executed first, thereby ensuring that risk control takes precedence over business sharing needs.

[0034] Simultaneously, when the risk compliance audit module detects unauthorized access, deviation from intended use, or abnormal export behavior, it can directly send a risk trigger event to the lifecycle status management module, causing the data asset to jump to a frozen or governance state. Furthermore, the unified management platform module will output risk alert information to administrators, achieving real-time linkage between lifecycle control and risk management. Example 3: In Example 3, in addition to periodic assessments, the data asset valuation module can also trigger real-time assessments based on events such as a sudden increase in data access volume, expansion of business scenarios, or significant changes in quality scores. When the asset value score exceeds a preset high-value threshold, the system automatically raises its authorization level cap and recommends entering shared or operational mode; when the asset value score drops below the threshold, the system automatically lowers its authorization level and restricts its sharing scope or access usage, thereby achieving adaptive linkage adjustment between changes in data asset value and authorization control strategies. It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0035] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A data asset lifecycle management system for small and medium-sized enterprises and its implementation method, characterized in that: This includes a data acquisition and access module, a data asset registration and ownership confirmation module, a data quality governance module, a lifecycle status management module, a data asset valuation module, a data usage and circulation control module, a risk compliance audit module, and a unified management and control platform module. These modules are interconnected through data and control interfaces. The data acquisition and access module is configured to collect raw data from enterprise business systems, production systems, or external interfaces, and construct candidate data asset objects based on metadata parsing and lineage analysis; The data asset registration and ownership confirmation module is configured to perform ownership confirmation processing on candidate data asset objects based on the credibility of data sources, the generation of responsible entities and business attribution rules, and generate a data asset master file containing asset identifier, ownership entity, usage boundaries and initial lifecycle status. The data quality governance module is configured to perform integrity, consistency, accuracy, and timeliness checks on registered data assets and generate quality level labels. The lifecycle state management module is configured to build a lifecycle state machine model for each data asset and automatically migrate and verify its lifecycle state based on data asset state change events. The data asset valuation module is configured to build a valuation model based on data asset usage behavior, business coverage, quality stability and risk level, and output dynamic valuation results. The data usage and circulation control module is configured to implement fine-grained control over data access, retrieval, sharing, and external circulation behaviors based on data asset status, ownership rules, and authorization policies. The risk compliance audit module is configured to monitor and analyze the behavior logs of data assets throughout their entire lifecycle and output compliance audit results. The unified management and control platform module is configured to provide a visual presentation and centralized management of the data asset catalog, lifecycle status, value assessment results, and risk situation.

2. The small and medium-sized enterprise data asset whole life cycle management and control system and implementation method thereof according to claim 1, characterized in that: The data asset registration and ownership confirmation module includes an asset identifier generation unit, an ownership subject identification unit, a responsibility attribution matching unit, and an authorization boundary modeling unit, which are used to construct a data asset master file model that includes a unique asset code, business attribution department, responsible person, scope of use, and authorization level.

3. The system and method for managing the lifecycle of data assets of small and medium enterprises according to claim 2, wherein: The data quality governance module includes a quality rule base, a quality detection engine, and a quality scoring model unit. The quality scoring model outputs quality level results based on multiple dimensions such as completeness, consistency, accuracy, timeliness, and availability, and binds the quality level results to the usage permission threshold and lifecycle migration conditions of the corresponding data assets.

4. The small and medium-sized enterprise data asset whole life cycle management and control system and implementation method thereof according to claim 3, characterized in that: The lifecycle state management module constructs a set of data asset lifecycle states based on a finite state machine model. The lifecycle states include at least the generation state, governance state, available state, operational state, shared state, frozen state, and deregistration state, and perform automatic state transition control based on data access events, authorization events, quality change events, and risk trigger events.

5. The small and medium-sized enterprise data asset whole life cycle management and control system and implementation method thereof according to claim 4, characterized in that: The data asset valuation module constructs a multi-factor valuation model. The model generates asset value scores based on parameters such as data asset usage frequency, number of business scenarios covered, quality stability, risk level, decision contribution, and external substitutability. These scores are then used to drive dynamic adjustments to data asset authorization levels, operational strategies, or circulation strategies.

6. The SME data asset full lifecycle management system and its implementation method according to claim 5, characterized in that: The data usage and circulation control module includes an asset-level access control unit, a field-level access control unit, and a usage-level authorization control unit. Based on data asset ownership rules, lifecycle status, and risk level, it performs real-time verification and access control on data calling, exporting, sharing, or external provision behaviors.

7. The SME data asset full lifecycle management system and its implementation method according to claim 6, characterized in that: The risk compliance audit module includes a behavior log collection unit, a compliance rule matching unit, and a risk identification and analysis unit. It is used to continuously monitor the behavior trajectory of data assets during the collection, processing, use, sharing, and destruction processes, and generate an audit evidence chain with timestamps and responsible entity identifiers.

8. A method for full lifecycle management and control of data assets for SMEs based on the system described in any one of claims 1 to 7, characterized in that, Includes the following steps: S1. Collect raw data from enterprise business systems or external data sources, and construct candidate data asset objects based on metadata parsing and lineage analysis; S2. Based on the credibility of the data source, the responsible entity, and the business attribution rules, perform registration and ownership confirmation processing on the candidate data asset objects, generate the data asset master file, and initialize the lifecycle status; S3. Perform multi-dimensional quality checks on registered data assets and generate quality level results; S4. Automatically migrate and verify the lifecycle status of data assets based on data access, authorization, quality changes, or risk-triggered events. S5. Perform value assessment and generate dynamic value results based on data asset usage behavior, business coverage, quality stability and risk level; S6. Implement fine-grained control over data access, retrieval, sharing, and circulation behaviors based on data asset status, ownership rules, and authorization policies; S7. Monitor and analyze the behavior logs of data assets throughout their entire lifecycle and generate compliance audit results; S8. When data assets meet the exit conditions, perform freezing, destruction, or archiving and seal the historical status trajectory.

9. The method for implementing full lifecycle management and control of data assets for SMEs according to claim 8, characterized in that: In step S4, a lifecycle state transition model based on a finite state machine is constructed. When the data asset quality level reaches a preset threshold, the data asset transitions from the generation state to the available state. When the data asset is authorized to be shared externally, the data asset transitions to the sharing state. When the risk level is detected to exceed the threshold, the data asset transitions to the frozen state.

10. The method for implementing full lifecycle management and control of data assets for SMEs according to claim 9, characterized in that: In step S5, the value of data assets is quantified by constructing a multi-factor value assessment function. The value assessment function includes at least the parameters of data usage frequency, business coverage, quality stability, risk level, decision contribution, and external substitutability. The authorization level or circulation strategy of data assets is dynamically adjusted based on the value assessment results.

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