Multi-Tier Data Certification for Consistent Internal Analysis

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Solution Overview

Problem

Conventional internal data analysis is hectic and difficult to implement, resulting in inconsistent data quality and insufficient security, making it challenging to perform accurate and secure data analysis within organizations.

Innovation Solution

Automated multi-tier data certification system that includes data specification definition, similarity determination, and certification of transformed data to ensure compliance with defined specifications, followed by merging certified data to generate a certified dataset and determining metrics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional internal data analysis is performed manually, then data can be processed and analyzed, but data quality becomes inconsistent and security is insufficient

Engineering Contradiction:

Improvedata quality consistencyVSAvoidimplementation complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system enables data to certify itself automatically through multi-tier certification processes. Data undergoes self-validation against defined specifications, constraints, and policies without requiring manual review, thereby ensuring consistent quality while reducing implementation burden through automation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual data analysis processes are replaced with an automated computer-implemented system. The mechanical manual review process is substituted with electronic automated certification tiers that systematically validate data against predefined criteria, improving consistency while managing complexity through algorithmic processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If data transformation is performed without automated certification, then data processing is faster, but data quality and security are compromised

Engineering Contradiction:

Improvedata processing speedVSAvoiddata quality consistency

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

Data specifications, constraints, and certification criteria are defined in advance before data transformation occurs. This preliminary setup enables automated validation to happen during or immediately after transformation, ensuring quality consistency is maintained without slowing down the overall processing speed

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The certification process operates continuously alongside data transformation rather than as a separate batch process. Multiple certification tiers execute in parallel or sequence without interrupting the data flow, maintaining both high productivity and consistent data quality through uninterrupted automated validation

Inventive Principle:

Principle #20Continuity of useful action

3Reliability

If manual data certification is implemented, then data quality can be improved, but the process becomes hectic and difficult to implement

Engineering Contradiction:

Improvedata securityVSAvoidimplementation ease

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system automates the certification process so that data and processes certify themselves without requiring manual intervention. The automated system handles specification matching, constraint validation, and security checks, improving data security while making implementation straightforward through configuration rather than manual execution

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

An automated certification system acts as an intermediary between data transformation and data usage. This intermediary layer systematically applies security and quality checks without requiring direct manual involvement, thereby improving data security while simplifying the overall implementation process through centralized automated management

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If automated multi-tier data certification is implemented, then data quality and security are improved, but system complexity increases

Engineering Contradiction:

Improvedata consistency and integrityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The certification system is divided into multiple independent tiers, each handling specific validation aspects such as format checking, constraint verification, and security validation. This segmentation allows complex certification requirements to be broken down into manageable, modular components that can be configured and maintained separately, improving data consistency while controlling system complexity through modularity

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250384024A1Automated multi-tier data certification for internal data analysis

Publication Date: 2025.12.18 UIPATH INC

AI summary

Systems and methods of certifying data are provided. 1) data that was transformed from initial data and 2) data specifications for the transformed data are received. A similarity between the transformed data and the data specifications is determined. It is determined whether the similarity is satisfactory. The transformed data is certified in response to determining that the similarity is satisfactory.