Composite Data Asset Creation from Undervalued Sources
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Solution Overview
Problem
Enterprises face challenges in effectively assigning economic value to data assets, leading to undervalued data that may hold significant intrinsic economic benefits, as existing methods focus on non-economic valuation and fail to recognize the value of less leveraged but critical data assets.
Innovation Solution
A method that identifies undervalued data assets by pruning data assets with low economic valuation scores, revaluating them based on updated non-economic scores, and combining them with high-value assets to create composite data assets with higher economic value, leveraging data lineage maps and valuation engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If enterprises use traditional non-economic valuation methods for data assets, then they can assign some type of business value to data assets, but they fail to effectively assign economic value and recognize data assets that could result in economic benefits
Solution Approach 1:
The patent segments the valuation process into distinct phases: initial non-economic valuation using existing methods, identification of undervalued assets based on discrepancies between non-economic and economic indicators, and targeted re-valuation of specific data assets. This segmentation allows the system to leverage simple existing methods while systematically improving economic valuation accuracy for critical assets.
Solution Approach 2:
The patent introduces an intermediary analysis layer that bridges non-economic valuation and economic value recognition. This intermediary step involves comparing non-economic valuation scores with economic potential indicators to identify undervalued assets, which then undergo targeted re-valuation. This intermediary mechanism resolves the contradiction by adding precision without requiring complete redesign of the valuation system.
2Productivity
If enterprises focus valuation resources on highly leveraged data assets, then they can efficiently evaluate prominent data assets, but they overlook less leveraged but critical data assets that may have significant intrinsic economic benefits
Solution Approach 1:
The patent extracts undervalued data assets from the mainstream valuation flow by identifying discrepancies between non-economic valuation scores and economic potential indicators. This extraction mechanism allows the system to maintain efficient bulk valuation of highly leveraged assets while separately identifying and re-evaluating overlooked assets that contain hidden economic value.
Solution Approach 2:
The patent changes the valuation parameters for identified undervalued assets by re-applying valuation models specifically to these assets after they have been flagged. This parameter change approach allows the system to maintain high productivity for the majority of assets while applying enhanced evaluation criteria to specific assets that show signs of undervaluation, thereby preventing loss of information about valuable data opportunities.
3Device complexity
If enterprises assign lower valuation scores to less leveraged data assets, then they can prioritize resources toward high-value assets, but they fail to recognize that combining undervalued assets with other data assets may create high-value composite data assets
Solution Approach 1:
The patent performs preliminary identification and flagging of undervalued data assets before the actual combining operation. By预先 marking assets with low non-economic scores but high economic potential, the system maintains simple management for the bulk of assets while preparing specific assets for combination operations, thus preserving both simplicity and adaptability.
Solution Approach 2:
The patent implements a merging mechanism that combines undervalued data assets with other data assets to create composite data assets. This combining operation is triggered specifically for identified undervalued assets, allowing the system to maintain simple management structures for individual assets while enabling adaptive combination operations that can unlock hidden value through data integration and synergistic effects.
Data Source
AI summary
Techniques are disclosed for creating high value data assets from undervalued data. In one example, a method identifies at least one data asset associated with an enterprise which has a lower non-economic valuation score as compared with one or more other data assets associated with the enterprise and is determined to be undervalued. The method then combines the at least one identified data asset with at least one of the one or more other data assets to form a composite data asset, wherein the composite data asset has a higher non-economic valuation score as compared to the at least one identified data asset.