Data Fabric Quality Scoring for Multi-Source Integration

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Solution Overview

Problem

Existing data management systems fail to provide effective monitoring and tracking of data quality within data fabrics, leading to degraded integration and consumption.

Innovation Solution

A method and system for assessing data fabric quality using a quality scoring engine that analyzes input data products based on scoring parameters, metadata, and rule definitions, generating a data fabric quality scoreboard for evaluation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If data integration is expanded to include multiple data sources into the data fabric, then data accessibility and integration capability are improved, but data fabric quality tends to degrade

Engineering Contradiction:

Improvedata integration capabilityVSAvoiddata fabric quality

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent implements a quality scoring engine that continuously monitors and evaluates data fabric quality metrics, providing feedback loops that enable detection of quality degradation and trigger appropriate responses to maintain reliability while preserving integration capabilities

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary quality assessments and validations before data integration operations are completed, establishing quality thresholds and rules in advance to prevent degradation before it occurs

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If data fabric integration is expanded without monitoring, then data accessibility is improved, but quality monitoring and tracking capability remains insufficient

Engineering Contradiction:

Improvedata accessibilityVSAvoidquality monitoring capability

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The quality scoring engine establishes continuous feedback mechanisms that track data fabric quality metrics, enabling precise measurement and monitoring of integration quality across multiple data sources

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system defines and tracks multiple quality parameters and metrics (such as data completeness, accuracy, timeliness) that can be independently measured and monitored to provide comprehensive quality assessment

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250363439A1System and method for assessing quality of data fabric

Publication Date: 2025.11.27 JPMORGAN CHASE BANK NA

AI summary

A system and method for assessing a quality of a data fabric are disclosed. The method includes: receiving a plurality of input data products from at least one data source into the data fabric; and transmitting the plurality of input data products to a quality scoring engine for assessing the quality of the data fabric based on an analysis of each of the plurality of input data products. The analysis includes receiving a plurality of scoring parameters, rule definitions, and a metadata for each of the plurality of input data products; calculating a respective data offering quality score against each of the plurality of scoring parameters during the lifecycle of the plurality of input data products; generating a data fabric quality scoreboard based on an aggregation of the respective data offering quality scores calculated for each of the input data product; and displaying the data fabric quality scoreboard.