Data Valuation Platform for Secure Exchange
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Solution Overview
Problem
Traditional data exchange systems are inefficient in systematically gathering, scoring, valuating, and exchanging data across a wide range, limiting its use to its highest value due to limited availability and lack of systematic methods for data valuation and certification.
Innovation Solution
A method and system for valuing datasets through a combination of objective and subjective scoring, using a DIM score that considers data size, information quality, and meaning, with certification processes to ensure data quality and provenance, enabling secure and efficient data exchange across platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data capture methods are used by individual entities, then data can be collected and exploited by the entity, but data availability to other entities is limited and data cannot be used at its highest value
Solution Approach 1:
The patent introduces a data exchange platform as an intermediary between data providers and data consumers. This platform includes a valuation module that automatically scores datasets using multiple criteria (completeness, accuracy, timeliness, relevance) and a matching module that connects data with potential users. This intermediary system resolves the contradiction by enabling broad data availability while managing complexity through automated valuation and matching mechanisms rather than direct peer-to-peer negotiations.
Solution Approach 2:
The data exchange platform is designed as a universal system that serves multiple functions: data submission, automated valuation, quality certification, matching, and exchange facilitation. This multi-functional platform can handle diverse data types and serve various user needs within a single system, increasing data availability across different entities without requiring separate systems for each use case.
2Productivity
If data is captured by professional entities for commercial exploitation, then data can be marketed to commercial entities, but systematic gathering and exchanging across a wide-ranging market is not enabled
Solution Approach 1:
The platform implements self-service mechanisms where datasets automatically undergo valuation and quality assessment when submitted. The valuation module autonomously scores data based on predefined criteria, and the certification process is automated rather than requiring manual review for each dataset. This self-service approach increases data exchange productivity by eliminating bottlenecks while the standardized automated processes manage complexity through consistency and scalability.
Solution Approach 2:
The system uses parameter-based valuation where datasets are assessed against multiple quantifiable parameters (completeness, accuracy, timeliness, relevance) and assigned scores accordingly. This parameter-driven approach enables systematic evaluation of diverse data types through consistent metrics, improving exchange efficiency while managing complexity through standardized assessment criteria that can be applied uniformly across the platform.
3Adaptability or versatility
If data exchange is limited to traditional methods, then existing data can be exploited by current entities, but data cannot be exchanged in a wide-ranging and efficient market
Solution Approach 1:
The platform performs preliminary valuation and quality certification of datasets at the time of submission, before any exchange transactions occur. This advance assessment establishes the data's value and suitability upfront, enabling rapid matching and exchange without delays during the transaction process. The preliminary action extends market reach by pre-qualifying data for various uses while minimizing time loss through one-time assessment rather than repeated evaluations during exchange.
Solution Approach 2:
The system replaces manual data valuation and certification processes with automated computational algorithms. The valuation module uses algorithmic scoring based on multiple parameters, and certification is performed through automated verification rather than human review. This substitution dramatically reduces valuation and certification time while extending market reach through scalable automated processing that can evaluate numerous datasets simultaneously.
Data Source
AI summary
A system and method for valuing a plurality of sets of data and similar medium, comprising receiving datasets, creating a first sub-score for each of the datasets, creating a second numerical sub-score for each of the plurality of datasets, the second numerical value varying based on information characteristics, the second sub-score being larger for improved information characteristics characterized by one or more of increased structural quality, increased completeness, increased interconnectivity, increased diversity, decreased redundancy, creating a third sub-score for each of the plurality of datasets, the third sub-score comprising a third numerical value being larger for improved meaning characteristics characterized by one or more of increased impact on a community, an increased number of impacted communities, greater veracity, greater relevance to an impacted community, greater scarcity; creating a composite score for each of the plurality of datasets that is a mathematical combination of the first, second, and third sub-scores.