Configurable Data Quality Gates for Real-Time Flow Control

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Solution Overview

Problem

Conventional data analysis technologies are limited in breadth and scale, lack actionability and accountability, and fail to provide transparency and real-time quality metrics, leading to unawareness of upstream data quality issues until data is consumed downstream.

Innovation Solution

Implementing configurable quality components (gates) that determine quality metrics across multiple dimensions, allowing dynamic reconfiguration and real-time data quality analysis, with machine learning for automatic updates and self-healing configurations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple single-data-quality systems are implemented to cover multiple dimensions, then data quality analysis breadth is improved, but implementation complexity and time increase

Engineering Contradiction:

Improvedata quality analysis breadthVSAvoidimplementation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent merges multiple data quality assessment dimensions (completeness, accuracy, consistency, timeliness) into a unified quality component framework. The quality component can be configured to assess different dimensions by loading different configuration files, consolidating what would traditionally require multiple separate systems into a single versatile component.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The quality component is designed as a universal, multi-functional system that can assess various data quality dimensions through configuration rather than requiring separate specialized components for each dimension. The same core component handles completeness, accuracy, consistency, and timeliness assessments by loading different quality configuration files.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Reliability

If conventional data quality solutions are used, then basic quality checking is possible, but real-time quality metrics and transparency are not provided

Engineering Contradiction:

Improvedata quality reliabilityVSAvoidreal-time quality measurement time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs data quality assessment in advance before data is consumed downstream. By evaluating quality metrics (completeness, accuracy, consistency, timeliness) upfront and making results available before data consumption, the system enables consumers to make informed decisions about whether to proceed with data usage, preventing time-consuming issues later in the data lifecycle.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The quality component provides real-time feedback on data quality status to consumers through quality metrics and detailed reports. This feedback mechanism allows consumers to understand upstream quality issues immediately and take appropriate actions, whether to proceed with data consumption or request corrections from data producers.

Inventive Principle:

Principle #23Feedback

3Productivity

If data quality issues are not tracked transparently, then data flow can proceed without interruption, but accountability and defect details are lost

Engineering Contradiction:

Improvedata flow continuityVSAvoidquality issue transparency

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The quality component acts as an intermediary between data producers and consumers, introducing a dedicated layer that tracks, records, and communicates quality metrics. This intermediary maintains full visibility of quality issues while allowing data flow to continue uninterrupted, as the quality assessment is performed separately from the actual data transmission.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces opaque mechanical data flow with a transparent, configurable quality assessment mechanism. Instead of relying on implicit assumptions about data quality, the system explicitly measures and reports quality metrics (completeness, accuracy, consistency, timeliness) that can be configured and monitored as needed, providing accountability without blocking data flow.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12554690B2Service chain for complex configuration-driven data quality rationalization and data control

Publication Date: 2026.02.17 CAPITAL ONE SERVICES LLC

AI summary

Techniques for data quality analysis may include the determination of data quality metrics using reconfigurable quality components. Access to the data may be based on the determined quality metrics. The configurable quality components may determine quality metrics for corresponding datasets. The quality components may be configured automatically based on quality configurations. The configuration of the quality components may be facilitated using a data orchestrator.