Secure Data Marketplace for Enriching Disjointed Records
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Solution Overview
Problem
Modern enterprises face inefficiencies and risks in reconciling incomplete and disorganized data records, leading to inadequate data collection and potential exposure of personally identifiable information, as they struggle to combine disjointed records and obtain missing data attributes from trusted sources.
Innovation Solution
A secure data marketplace system that enables data matching, profiling, masking, consolidating, and enrichment by using tokenized data from both customer and reference sources, allowing for secure comparison and filling of gaps in data records through a third-party platform, which facilitates efficient data integrity and reduces manual efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If entities manually combine disjointed data records to reconcile incomplete information, then data completeness may improve, but the process becomes inefficient and time-consuming while exposing personally identifiable information
Solution Approach 1:
The patent introduces a data marketplace platform as an intermediary between data providers and data consumers. This platform enables automated matching and reconciliation of disjointed records through structured data profiles and contextual analysis, eliminating manual combination processes while protecting personally identifiable information through controlled access mechanisms.
Solution Approach 2:
The patent replaces manual mechanical processes of record reconciliation with automated computational systems. These systems use algorithms to profile data, identify contextual relationships, and automatically merge disjointed records based on predefined criteria, dramatically improving efficiency while maintaining data completeness.
2Loss of information
If entities manually reconcile data records, then data integration may be achieved, but the process exposes personally identifiable information creating security risks
Solution Approach 1:
The data marketplace platform serves as a secure intermediary that facilitates data integration without direct exposure of personally identifiable information. The system uses contextual profiling and matching algorithms to integrate records while maintaining privacy through indirect reference mechanisms and controlled data access protocols.
Solution Approach 2:
The patent extracts and separates personally identifiable information from the core data reconciliation process. By using contextual profiles and anonymized identifiers as intermediaries, the system achieves necessary data integration while removing sensitive information from the matching and combination operations.
3Loss of information
If organizations collect data from multiple sources to fill missing data attributes, then data completeness improves, but the complexity of tracking and obtaining data from various sources increases
Solution Approach 1:
The data marketplace platform provides a universal interface that handles multiple data collection functions through a single system. It profiles data from various sources, identifies missing attributes, matches records across organizations, and facilitates data exchange through standardized protocols, replacing multiple complex tracking mechanisms with one integrated solution.
Solution Approach 2:
The system implements feedback mechanisms where data profiles are continuously analyzed to identify missing attributes, then automated queries are generated to obtain needed data from appropriate sources. The system tracks and updates data completeness status, providing feedback loops that automatically resolve gaps without manual intervention.
Data Source
AI summary
The present disclosure is directed to a data marketplace for enriching data records. Specifically, the systems and methods disclosed enable the enrichment of data via matching, identifying composite data records, and utilizing Reference Source datasets. In one example aspect, Customer data is tokenized and then subsequently transmitted to a third-party Data Marketplace Platform. Similarly, a Reference Source dataset may be tokenized and transmitted to a Data Marketplace Platform. On the Data Marketplace Platform, the customer data and the reference source data may be compared, wherein certain data attributes (i.e., tokens on the Data Marketplace Platform) may be identified as missing in the customer dataset and present in the reference source dataset. The customer may then have the ability to acquire the missing and value-added data attributes by transacting with the reference source via a data broker, such as the Data Marketplace Platform.