Multi-dimensional data asset dynamic evaluation system and method

Abstract

The invention provides a multi-dimensional data quality evaluation and optimization method. According to the method, a four-dimensional evaluation system with integrity, accuracy, consistency and compliance is constructed, quality scores are automatically calculated, and low-quality data are identified; and then an optimization strategy is automatically executed, and circular evaluation is carried out until the standard is reached. According to the method, comprehensive quantitative evaluation and automatic management closed loop of data quality are realized, and the quality management efficiency and level of data assets are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of data asset management and value assessment technology, and in particular to a data asset value assessment method and system that integrates multi-dimensional indicators and can be dynamically updated. Background Technology

[0002] Existing data asset valuation methods often have a single perspective, considering only historical costs or predicting future returns. This results in biased assessments that fail to accurately reflect the intrinsic value of the data. Furthermore, the value of data assets is not static; it fluctuates dynamically with the growth of data volume, the expansion of application scenarios, and changes in the market environment. Current valuation methods are mostly static, valid for a long period after a single assessment, failing to capture this dynamic nature. This leads to outdated valuation results, making it difficult to support accurate data transaction pricing and asset management decisions. Summary of the Invention

[0003] Technical problem to be solved: In order to address the one-sided and static nature of existing data asset evaluation methods, this invention provides a multi-dimensional and dynamic data asset evaluation method and system to achieve accurate quantification and real-time tracking of data value.

[0004] Technical solution: The technical solution of the present invention is as follows: 1. Construct a general evaluation model framework that integrates "cost-benefit-risk".

[0005] - Cost dimension: Quantify the full lifecycle cost of data from collection, storage, governance, labeling to compliance management.

[0006] - Benefit dimension: Quantify the direct and indirect benefits brought by data.

[0007] - Risk Dimension: Assess the risks of data in terms of compliance, quality, liquidity, etc., and generate risk adjustment coefficients.

[0008] 2. Configure differentiated and segmented evaluation indicators and weights for different application scenarios.

[0009] 3. Set up a dynamic evaluation trigger mechanism, including timed triggers and event triggers.

[0010] 4. Based on the aforementioned model, indicators, and real-time data, calculate the comprehensive value score and fair value of the data assets.

[0011] 5. Generate a detailed assessment report, which not only shows the value amount, but also includes value composition analysis, risk warnings, and future value trend predictions.

[0012] Beneficial effects: The beneficial effects of this invention are as follows: 1. Comprehensive and accurate assessment: Taking into account the three dimensions of cost, benefit, and risk, the assessment results are more comprehensive and objective, and can effectively support data transaction pricing and internal accounting.

[0013] 2. Dynamic Value Perception: By reassessing data on a timed basis and through event-triggered events, the assessment results can reflect changes in the value of data assets in a timely manner, providing support for dynamic decision-making.

[0014] 3. Strong scenario adaptability: It supports the configuration of evaluation models according to different scenarios, has strong versatility, and can be widely used in various organizations such as government, finance, and enterprises.

Claims

1. A method for dynamic evaluation of multi-dimensional data assets, characterized in that, Includes the following steps: S1: Construct an evaluation model that includes cost, benefit, and risk dimensions; S2: Configure detailed indicators and weights for different evaluation scenarios; S3: Set up a dynamic evaluation trigger mechanism; S4: Based on the aforementioned model and data, calculate the comprehensive value score and fair value of the data assets; S5: Generate a data asset valuation report.

2. The method according to claim 1, characterized in that, The cost dimension in step S1 includes the full lifecycle cost of collection, storage, governance, labeling, and compliance.

3. The method according to claim 1, characterized in that, The risk dimensions in step S1 include compliance risk, quality risk, and liquidity risk.

4. The method according to claim 1, characterized in that, The dynamic evaluation triggering mechanism in step S3 includes timed triggering and event triggering.

5. The method according to claim 1, characterized in that, The evaluation report generated in step S5 includes value composition analysis and trend prediction.