In-Situ Data Quality Evaluation for Sensitive Data Locality

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Solution Overview

Problem

Existing data quality evaluation systems require organizations to send sensitive data out of their native environment for assessment, violating data locality requirements and compromising security and performance, while being inflexible to varying legal and regulatory needs.

Innovation Solution

A data quality evaluation system that performs in-situ assessments using a processing platform and an execution platform, segregated by networks, allowing for customizable data quality evaluations with deep neural networks to detect complex issues, and supports Bring Your Own Code capabilities for user-defined algorithms, ensuring data integrity and compliance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If data is sent to a central processing environment for quality evaluation, then comprehensive quality assessment can be performed, but data security and privacy are compromised

Engineering Contradiction:

Improvedata quality assessment accuracyVSAvoiddata security risk

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent introduces an intermediary mechanism where data quality evaluation algorithms are transmitted to the customer's environment rather than moving the data itself. The execution environment acts as an intermediary that runs the algorithms locally and returns only aggregate quality metrics, preventing exposure of sensitive data while maintaining assessment accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments the data quality evaluation process into two distinct components: (1) algorithm generation and management on the provider's processing platform, and (2) algorithm execution and data processing in the customer's segregated execution environment. This segmentation allows comprehensive quality assessment while maintaining data locality and security

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If data is transferred to external systems for evaluation, then quality metrics can be derived, but data locality requirements are violated

Engineering Contradiction:

Improvequality metric derivationVSAvoiddata locality compliance

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent inverts the traditional data quality evaluation model by bringing the processing capability to the data rather than bringing the data to the processing environment. Algorithms are sent to the customer's execution environment where they process data locally, reversing the conventional flow and ensuring data locality compliance while maintaining metric derivation capability

Inventive Principle:

Principle #13The other way round (Inversion)

3Productivity

If existing data quality systems are used, then evaluation can be performed, but customization for specific governance needs is limited

Engineering Contradiction:

Improveevaluation performanceVSAvoidcustomization capability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system implements dynamic adaptability through configurable algorithm parameters and support for customer-provided custom algorithms. The processing platform allows dynamic adjustment of evaluation criteria, data quality rules, and algorithm selection based on specific organizational needs and regulatory requirements, making the system versatile across different jurisdictions and use cases

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent creates a universal data quality evaluation framework that can handle multiple evaluation scenarios through a single platform. The system supports various data types, evaluation algorithms, and compliance requirements through configurable parameters and algorithm selection, enabling one system to serve diverse organizational needs across different regions and industries

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260037658A1Systems and methods for in-situ quality evaluation of sensitive data

Publication Date: 2026.02.05 JPMORGAN CHASE BANK NA

AI summary

Aspects of the subject disclosure may include, for example, a processing platform configured to analyze data quality evaluation requirements associated with source data, identify data quality evaluation algorithm(s) based on an analysis of the data quality evaluation requirements, and store metadata relating to evaluation of the source data, and an execution platform configured to receive the data quality evaluation algorithm(s) from the processing platform and execute the data quality evaluation algorithm(s) for the evaluation of the source data to derive data quality metrics, and provide the data quality metrics to the processing platform for storage as the metadata, wherein the processing platform is segregated from the execution platform by network(s), and wherein the execution platform is implemented with in-situ access to the source data such that the source data is withheld from being shared with system(s) outside of the execution platform during the evaluation, thereby ensuring data integrity/locality compliance.